System

Abstract

To provide a system.SOLUTION: A system comprises: means for transmitting a security-related question input by a user to a server; means for generating a prompt sentence that instructs the server to generate an optimal answer to the question; a generative AI model for receiving the input of the prompt sentence received from the server and generating the optimal answer to the question; means for analyzing the answer received from the generative AI model by the server and presenting it to the user; means for the server evaluating security risks in real time and generating a warning when a security risk is detected; and means for the server learning past security incidents and predicting future risks.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] In conventional system development, there were many challenges, such as ensuring the accuracy of security responses and security compliance rates, responding to the latest security trends, preparing security designs before development, inputting server development, setting milestones based on internal and external data, etc. In addition, there was a need to shorten the lead time for communication with consultation departments and responses from a system development perspective. [Means for solving the problem]

[0005] The system of the present invention solves these problems by including means for enabling the accuracy of security-related answers, security compliance rates, and application of security to the latest trends in system development, means for providing guidance on preparing for security design before development, means for providing server development input, and means for enabling the setting of milestones based on internal and external data.Furthermore, by enabling communication with the consultation department from a system development perspective and providing means for shortening the lead time until a response, efficient system development is realized. [Brief explanation of the drawings]

[0006] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 2 is a sequence diagram showing a flow of processing in the data processing system according to the first embodiment of the first form example. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Embodiment 1. [Figure 13] FIG. 10 is a sequence diagram showing a processing flow of a data processing system in a second embodiment of the second form example. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Embodiment Example 2. [Figure 15] FIG. 10 is a sequence diagram showing the flow of processing in a data processing system according to a third embodiment of the third embodiment. [Figure 16] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Embodiment 3. [Figure 17] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the first embodiment of the first form example when an emotion engine is combined. [Figure 18] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Form Example 1 when an emotion engine is combined. [Figure 19] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the second embodiment of the second form example when an emotion engine is combined. [Figure 20] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Form Example 2 when an emotion engine is combined. [Figure 21] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the third embodiment of the third form example when an emotion engine is combined. [Figure 22] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Form Example 3 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0007] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0008] First, the terms used in the following description will be explained.

[0009] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (TENSOR PROCESSING UNIT (registered trademark)).

[0010] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0011] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0012] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0013] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0014] [First embodiment]

[0015] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0016] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0017] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0018] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0019] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0020] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0021] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0022] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0023] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0024] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0025] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0026] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0027] "Example 1"

[0028] The system of the present invention uses algorithms based on the latest security technologies to improve the accuracy of security-related answers. Specifically, it utilizes machine learning and artificial intelligence to generate optimal answers to security-related questions.

[0029] "Example 2"

[0030] In addition, as a means of improving security compliance, the system periodically checks security and immediately notifies developers to correct any violations that are found. Specifically, the system performs security checks late at night every day, and if a violation is found, it notifies developers the following morning.

[0031] "Example 3"

[0032] Furthermore, as a means of enabling the application of security to the latest trends in system development, the system periodically checks the latest security trends and reflects them in system development. Specifically, the system checks the latest security trends every Friday and reflects them in the next week's system development.

[0033] The processing flow of each embodiment will be described below.

[0034] "Example 1"

[0035] Step 1: The system uses algorithms based on the latest security technology to generate optimal solutions to security problems.

[0036] Step 2: Specifically, we use machine learning and artificial intelligence to generate optimal answers to security problems.

[0037] Step 3: The generated answers are used to improve the accuracy of the system's security answers.

[0038] "Example 2"

[0039] Step 1: The system will periodically check your security and notify you immediately if any violations are found, prompting you to correct them.

[0040] Step 2: Specifically, the system performs a security check every night, and if a violation is found, it notifies the developer the following morning.

[0041] Step 3: This notification allows developers to immediately fix security violations, improving system security compliance.

[0042] "Example 3"

[0043] Step 1: The system will regularly check the latest security trends and reflect them in system development.

[0044] Step 2: Specifically, the system checks for the latest security trends every Friday and reflects them in the following week's system development.

[0045] Step 3: This method ensures that system development always corresponds to the latest security trends, improving system security.

[0046] Example 1

[0047] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0048] Conventional security systems have had issues with low accuracy of security-related responses, making it difficult to improve security compliance rates. Furthermore, it was difficult to apply the latest security trends to system development, resulting in insufficient preparation for security design before development. Furthermore, there were issues with the inability to effectively provide server development input or set milestones based on internal and external data.

[0049] The identification process by the identification processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means. In this invention, the server includes means for improving the accuracy of security-related answers, means for improving the security compliance rate, means for enabling security to be applied to the latest trends in system development, means for providing guidance on preparing for security design before development, means for providing server development input, means for enabling milestone setting based on internal and external data, means for inputting prompt statements, means for collecting security data, means for preprocessing the collected data, means for training a machine learning model, means for evaluating the trained model, means for generating an optimal answer, and means for providing the generated answer. This makes it possible to quickly and accurately provide optimal answers to security-related questions.

[0050] "Security Answer Accuracy" refers to the accuracy and reliability of answers provided to security questions or problems.

[0051] "Security compliance rate" is an indicator that shows the percentage of compliance with security policies and regulations.

[0052] "Applying the latest trends in security to system development" refers to incorporating the latest security technologies and trends into the system development process.

[0053] "Guidance on preparing for security design before development" refers to providing guidance and advice on security design in the early stages of system development.

[0054] "Server development input" refers to providing information and data necessary for server development.

[0055] "Setting milestones based on internal and external data" refers to using internal and external data to set important milestones that indicate the progress of a project.

[0056] A "prompt sentence" refers to a question or instruction sentence that a user inputs to a system.

[0057] "Security Data" means security-related information and data, including network traffic, log files, and system events.

[0058] "Data preprocessing" refers to the process of converting collected data into a format suitable for analysis and learning. Specifically, it includes data cleaning, normalization, feature extraction, etc.

[0059] A "machine learning model" refers to an algorithm or mathematical model that learns patterns and rules from data and makes predictions and classifications.

[0060] "Model evaluation" refers to the process of measuring the performance of a trained machine learning model and confirming its effectiveness, using metrics such as precision, recall, and F1 score.

[0061] "Generating optimal answers" refers to creating the most appropriate answer to a user's question based on collected data and a trained model.

[0062] "Providing an answer" refers to displaying or communicating the generated answer to the user.

[0063] This invention is a system for improving the accuracy of security-related answers, using algorithms based on the latest security technologies. Specifically, it utilizes machine learning and artificial intelligence to generate optimal answers to security-related questions.

[0064] First, the user inputs a prompt sentence using a terminal, for example, "What is the best way to detect unauthorized access in a company network?"

[0065] The server collects relevant security data based on prompts entered by the user, and this data is obtained from network traffic, log files, system events, etc. The hardware used is a network sensor or a log server.

[0066] The server then preprocesses the collected data, specifically cleaning the data (filling in missing values ​​and removing outliers), normalizing the data (scaling the data), and extracting features (extracting important data points). The software used is Python's Pandas and NumPy libraries.

[0067] The server uses the preprocessed data to train a machine learning model. The algorithms used are deep learning (e.g., CNN, RNN) and support vector machines (SVM). The software used is TENSORFLOW (registered trademark) and Scikit-learn.

[0068] To evaluate the trained model, the server uses evaluation metrics (precision, recall, F1 score, etc.) and the software used is the evaluation function of Scikit-learn. For example, it checks whether the accuracy of the model is 90% or higher.

[0069] Based on the evaluation results, the server generates an optimal solution to the security problem. The software used is a generative AI model (e.g., GPT-3 (registered trademark)). For example, the server generates an answer such as, "It is recommended to use an anomaly detection algorithm to detect anomalies in network traffic."

[0070] Finally, the server provides the generated answer to the user's terminal, and the user can implement security measures based on the provided answer.

[0071] This system allows users to quickly and accurately find the best possible answers to security questions.

[0072] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0073] Step 1: Enter the prompt statement

[0074] The user inputs a prompt into the input field of the terminal. For example, the user might input, "What is the best way to detect unauthorized access in a company network?" This prompt becomes the input to the system.

[0075] Step 2: Data collection

[0076] The server collects relevant security data based on the prompts entered by the user, such as network traffic data, log files, and system events. The hardware used is a network sensor and a log server. The collected data is used as input for the next step.

[0077] Step 3: Data Preprocessing

[0078] The server preprocesses the collected data. Specifically, it performs data cleaning (filling in missing values ​​and removing outliers), normalization (scaling the data), and feature extraction (extracting important data points). The software used is Python's Pandas and NumPy libraries. The preprocessed data becomes the input for the next step.

[0079] Step 4: Model training

[0080] The server uses the preprocessed data to train a machine learning model. The algorithms used are deep learning (e.g., CNN, RNN) and support vector machines (SVM). The software used is TensorFlow or Scikit-learn. The trained model becomes the input for the next step.

[0081] Step 5: Model evaluation

[0082] The server evaluates the trained model. Evaluation uses metrics such as precision, recall, and F1 score. The software used is Scikit-learn's evaluation function. For example, it checks whether the model's accuracy is 90% or higher. The evaluation results are input to the next step.

[0083] Step 6: Generate the optimal solution

[0084] The server generates an optimal answer based on the evaluation results. This answer is for a security-related problem. The software used is a generative AI model (e.g., GPT-3). For example, it generates an answer such as, "It is good to use an anomaly detection algorithm to detect anomalies in network traffic." The generated answer becomes the input for the next step.

[0085] Step 7: Provide the answer

[0086] The server provides the generated answer to the user's terminal. The user can implement security measures based on the provided answer. The answer is output to the user.

[0087] (Application example 1)

[0088] Next, a description will be given of Application Example 1 of Embodiment Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0089] In today's information society, security issues are becoming increasingly complex, requiring rapid and accurate responses. However, conventional systems provide low accuracy in security responses and have not achieved sufficient improvements in security compliance rates. Furthermore, systems often lack the ability to adapt to the latest trends in system development and to adequately prepare for security design prior to development. Furthermore, there is the issue of difficulty in quickly providing optimal answers when users ask security questions.

[0090] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0091] In this invention, the server includes means for enabling the accuracy of security-related answers, security compliance rates, and application of security to the latest trends in system development, means for providing guidance on preparing for security design before development, means for providing server development input, means for enabling the setting of milestones based on internal and external data, means for generating optimal answers to security-related questions entered by users using a generative AI model, means for evaluating security risks in real time and issuing warnings, and means for learning from past security incidents and predicting future risks.This enables quick and accurate responses to security issues, improves security compliance rates, and effectively applies security to system development.

[0092] "Security answer accuracy" refers to the ability to provide accurate and relevant answers to security questions or problems.

[0093] "Security compliance rate" refers to the percentage of actions taken in accordance with security policies and guidelines.

[0094] "Applying security to the latest trends in system development" refers to the appropriate implementation of security measures in system development that incorporates the latest technologies and trends.

[0095] "Guidance on Pre-Development Security Design Preparation" refers to providing guidance and advice on planning and designing security measures in the early stages of system development.

[0096] "Server development input" refers to providing information and data necessary for server design and development.

[0097] "Setting milestones based on internal and external data" refers to using internal and external data to set important milestones that indicate the progress of a project.

[0098] A "generative AI model" refers to an algorithm or system that uses artificial intelligence to learn from data and generate new information or answers.

[0099] "Means of assessing security risks and issuing warnings in real time" refers to the ability to instantly analyze ongoing situations, detect potential security risks, and issue warnings.

[0100] "Means of learning from past security incidents and predicting future risks" refers to the function of analyzing security issues that have occurred in the past and predicting future risks based on that knowledge.

[0101] As an embodiment of the present invention, the following system can be constructed.

[0102] First, the server includes the following means:

[0103] 1. A means to enable the accuracy of security responses, security compliance rates, and application of security to the latest trends in system development.

[0104] 2. A means of providing guidance for security design preparation before development.

[0105] 3. A means of providing server development input.

[0106] 4. A means to enable milestone setting based on internal and external data.

[0107] 5. A means of generating optimal answers to security questions entered by users using generative AI models.

[0108] 6. A means of assessing and alerting security risks in real time.

[0109] 7. A means of learning from past security incidents and predicting future risks.

[0110] Hardware and software used

[0111] Hardware: Smartphones, servers

[0112] Software: Python, OpenAI® API

[0113] Data processing and calculation

[0114] The server receives security questions entered by the user through a smartphone application, then sends the questions to a generative AI model to generate the optimal answer, which is then displayed to the user through the smartphone application.

[0115] The server also assesses security risks in real time, issuing warnings as needed, and has the ability to learn from past security incidents and predict future risks.

[0116] Specific examples

[0117] For example, if a user asks, "How do I spot a phishing email?", the server sends the following prompt to the generative AI model:

[0118] Security Question: How do I spot a phishing email?

[0119] Generate the best answer.

[0120] The generative AI model generates an answer based on this prompt, and the server provides the answer to the user. Examples of generated answers include:

[0121] Here's how to spot a phishing email:

[0122] 1. Check the sender's email address.

[0123] 2. Don't click on links in emails.

[0124] 3. Be wary of emails requesting personal information.

[0125] 4. Check your email for grammar and spelling mistakes.

[0126] 5. Report any suspicious emails to your IT department.

[0127] In this way, users can get fast and accurate answers to their security questions, assess security risks in real time, and predict future risks by learning from past incidents.

[0128] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0129] Step 1:

[0130] A user launches an application on their smartphone and enters a security question. The entered question is sent to the server by the application. The input data is the user's question, and the output data is the question sent to the server.

[0131] Step 2:

[0132] The server analyzes the received question and generates a prompt to send to the generative AI model. The input data is the user's question, and the output data is the generated prompt. Specifically, the server analyzes the content of the question and converts it into an appropriate format.

[0133] Step 3:

[0134] The server sends the generated prompt to the generative AI model and generates the optimal answer. The input data is the prompt, and the output data is the answer returned by the generative AI model. Specifically, the server sends the prompt to the generative AI model and receives a response from the model.

[0135] Step 4:

[0136] The server analyzes the answer received from the generative AI model and converts it into a format for display to the user. The input data is the answer from the generative AI model, and the output data is the answer to be displayed to the user. Specifically, the server analyzes the content of the answer and formats it into a format that is easy for the user to understand.

[0137] Step 5:

[0138] The server sends the formatted answer to the smartphone application and displays it to the user. The input data is the formatted answer, and the output data is the answer displayed on the user's smartphone. Specifically, the answer is sent to the application, and the application displays it to the user.

[0139] Step 6:

[0140] The server evaluates security risks in real time and issues warnings if necessary. The input data is information about the current security situation, and the output data is a warning message. Specifically, it runs an algorithm to evaluate security risks and generates a warning if a risk is detected.

[0141] Step 7:

[0142] The server learns from past security incidents and predicts future risks. The input data is data about past security incidents, and the output data is a prediction of future risks. Specifically, the server analyzes past data and updates the model for predicting future risks.

[0143] Example 2

[0144] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0145] Modern system development requires improved security compliance and rapid response. However, the frequency of security checks and response after detecting violations are often delayed, resulting in a risk of system vulnerabilities being left unaddressed. Furthermore, there is often a lack of means to apply the latest security trends and preparation for security design before development. To solve these issues, regular security checks, rapid notifications, and efficient responses using generative AI models are required.

[0146] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for enabling security response accuracy, security compliance rate, and application of security to the latest trends in system development, a means for providing guidance on preparing for security design before development, a means for providing server development input, a means for enabling milestone setting based on internal and external data, a means for periodically performing security checks, a means for generating a notification urging correction when a violation is found, a means for generating notification content using a generative AI model, and a means for sending a notification to a developer. This enables an improvement in security compliance rate and a quick response.

[0147] "Security answer accuracy" refers to the ability to provide accurate and prompt answers to security questions and problems.

[0148] "Security compliance rate" is an indicator of how well a system complies with security policies and regulations.

[0149] "Applying the latest trends in security to system development" refers to incorporating the latest security technologies and best practices into the system development process.

[0150] "Guidance on preparing for security design before development" refers to providing guidance and advice on how to design with security in mind in the early stages of system development.

[0151] "Server development input" refers to providing information and data necessary for server development.

[0152] "Setting milestones based on internal and external data" refers to setting important milestones based on internal and external data to manage the progress of a project.

[0153] "Means for performing regular security checks" refers to methods and tools for checking the security status of a system at regular intervals.

[0154] "Means for generating notifications to encourage remediation when violations are found" refers to methods or tools for creating notifications to encourage remediation when a security violation is detected.

[0155] "Means for generating notification content using generative AI models" refers to methods or tools that use artificial intelligence models to create notification content that includes details of a security breach and how to remediate it.

[0156] "Means for sending notifications to a developer" means the methods and tools used to deliver generated notifications to a developer.

[0157] The present invention is a system for improving security compliance, in which a server, a terminal, and a user work together. A specific embodiment of this system will be described below.

[0158] The server uses a scheduling tool (e.g., cron job or Windows Task Scheduler) to run a security check every night. The server also launches a security scanning tool (e.g., Nessus or OpenVAS) to perform a system-wide security check, which performs a detailed scan of the network and system configuration to detect vulnerabilities and security violations.

[0159] Once the scan is complete, the server analyzes the scan results. This involves reading the report file generated by the scanning tool and extracting a list of vulnerabilities and security violations. Specifically, the server reads the Nessus report file (e.g., in XML format) and uses XPath queries to extract vulnerability information.

[0160] The server then uses a generative AI model (e.g., GPT-4®) to generate a notification based on the scan results. Specifically, it creates a prompt that details the vulnerabilities or violations detected and explains how to remediate them. Here is an example prompt:

[0161] Example prompt sentence:

[0162] Outdated software has been found. Please update the following software to the latest version:

[0163] Software Name: ExampleSoftware

[0164] Current version: 1.0.0

[0165] Latest version: 1.2.0

[0166] Please see the link below for update instructions:

[0167] [Update procedure link]

[0168] The server sends the generated notification content to the developer. The notification is sent via email or a chat tool (for example, Slack or MICROSOFT(R) TEAMS(R)). Specifically, the server uses an SMTP server to send an email containing the generated notification content to the developer's email address, or uses the Slack API to post the notification content to the developer's Slack channel.

[0169] Users (developers) receive notifications and follow instructions to update their software, which improves system security compliance and quickly fixes vulnerabilities.

[0170] The system can improve security compliance through regular security checks and prompt notifications, and by utilizing generative AI models, it can streamline notification content generation, reducing the burden on developers.

[0171] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0172] Step 1:

[0173] The server uses a scheduling tool to set up a job to run a security check every night.

[0174] Input: Scheduling tool setting information

[0175] Data Processing: Job Scheduling

[0176] Output: A job that runs a security check every night at midnight

[0177] Specific operation: The server sets up a cron job and registers a command to run a security scanning tool at 2:00 AM every day.

[0178] Step 2:

[0179] The server launches a security scanning tool according to a set schedule and performs a security check of the entire system.

[0180] Inputs: Scheduled jobs, security scanning tools

[0181] Data Processing: Full System Scan

[0182] Output: Scan result report file

[0183] What happens: The server launches Nessus and begins a network-wide scan, which examines the configuration of each device and server in detail to detect known vulnerabilities and misconfigurations.

[0184] Step 3:

[0185] Once the scan is complete, the server analyzes the scan results.

[0186] Input: Scan result report file

[0187] Data processing: Analyzing report files and extracting vulnerability information

[0188] Output: A list of vulnerabilities and security violations

[0189] Specific operation: The server reads the Nessus report file (e.g., XML format) and extracts vulnerability information using XPath queries.

[0190] Step 4:

[0191] The server uses a generative AI model to generate notification content based on the scan results.

[0192] Input: List of vulnerabilities and security breaches

[0193] Data processing: Notification content generation

[0194] Output: Generated notification content (prompt text)

[0195] Specific behavior: The server inputs the following prompt into GPT-4 to generate the notification content:

[0196] The following vulnerabilities were discovered:

[0197] Vulnerability Name: Outdated Software

[0198] Affected: ExampleSoftware 1.0.0

[0199] To fix this vulnerability, please update ExampleSoftware to the latest version 1.2.0. Please see the following link for update instructions:

[0200] [Update procedure link]

[0201] Step 5:

[0202] The server sends the generated notification content to the developer.

[0203] Input: Generated notification content (prompt text)

[0204] Data Processing: Sending notifications

[0205] Output: Notification sent to developer

[0206] Specific behavior: The server uses an SMTP server to send an email containing the generated notification content to the developer's email address, or uses the Slack API to post the notification content to the developer's Slack channel.

[0207] Step 6:

[0208] The user (developer) receives a notification and follows the instructions to update the software.

[0209] Input: Notification content (prompt text)

[0210] Data Processing: Software Update

[0211] Output: Updated software

[0212] Action: The user acknowledges the notification and follows the instructions to update the software to the latest version, thereby improving system security compliance.

[0213] (Application example 2)

[0214] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0215] Increasing security compliance is extremely important in modern system development. However, when security checks are performed manually, they tend to be performed infrequently, and when violations are found, responses are often delayed. Furthermore, detailed explanations of violations and the provision of remediation methods are insufficient, making it difficult for developers to respond quickly. Furthermore, the lack of a way to manage past security check results and remediation history makes continuous security improvement difficult.

[0216] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for enabling the accuracy of security-related answers, security compliance rate, and application of security to the latest trends in system development, means for providing guidance on preparing for security design before development, means for providing server development input, means for enabling the setting of milestones based on internal and external data, means for conducting regular security checks and, if a violation is found, for issuing a notification urging immediate correction, means for providing a guide detailing the violation and how to correct it, and means for saving and making available past security check results and correction history. This enables an improvement in security compliance rate and rapid response to violations.

[0217] "Security answer accuracy" refers to the ability to provide accurate and prompt answers to security questions and problems.

[0218] "Security compliance rate" refers to the percentage of a system or organization that complies with established security policies and standards.

[0219] "Applying security to the latest trends in system development" refers to introducing appropriate security measures for the latest system development technologies and trends.

[0220] "Guidance on preparing for security design before development" refers to providing guidance and advice on how to design with security in mind in the early stages of system development.

[0221] "Server development input" refers to providing information and data necessary for server development.

[0222] "Setting milestones based on internal and external data" refers to using internal and external data to set important milestones to manage the progress of a project.

[0223] "Performing regular security checks" refers to the process of checking the security status of a system at regular intervals.

[0224] "If a violation is found, provide immediate notification to prompt correction" refers to sending a notification to promptly correct a security violation when it is detected.

[0225] A "guide detailing the breach and how to fix it" refers to a document or tool that details the specific nature of the security breach and how to fix it.

[0226] "Saving and making available the results of past security checks and correction history" means recording the results of previous security checks and the history of corrections, and making them available for reference as needed.

[0227] As an embodiment of the present invention, a security check alert system is constructed, the details of which are described below.

[0228] System configuration

[0229] This system consists of a server, a terminal (such as a smartphone), and a user (developer). The server periodically performs security checks and sends notifications if a violation is found. The terminal is a device that the user uses to receive notifications and check the details of the violation and how to fix it.

[0230] Hardware and software used

[0231] Hardware: Servers (cloud infrastructure or on-premise servers), smartphones

[0232] Software: Python, smtplib (email sending library), schedule (schedule library)

[0233] Data processing and calculation

[0234] The server processes the data as follows:

[0235] 1. Security check execution: The server automatically executes a security check every night. If a security violation is found as a result of the check, the details of the violation are recorded.

[0236] 2. Sending violation notification: If a violation is found, the server will send a push notification to the developer's device the next morning, containing details of the violation and instructions on how to fix it.

[0237] 3. History storage: The server stores past security check results and correction history in a database and makes them available for reference as needed.

[0238] Specific examples

[0239] For example, if a company's IT infrastructure performs a security check every night and an SQL injection vulnerability is found, the server will send a notification to the developer's smartphone the next morning saying, "An SQL injection vulnerability has been found. Please refer to the link below for how to fix it." The developer will receive the notification and be able to respond quickly by following the detailed fix guide.

[0240] Prompt Sentence Examples

[0241] "Implement a system that performs security checks every night and notifies developers the next morning if a violation is found. Specifically, create a program that uses Python to perform security checks and sends a notification by email if a violation is found."

[0242] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0243] Step 1:

[0244] The server runs a security check every night at midnight. As input, it takes data about the security state of the system and applies the security check algorithm. As output, it generates a list of security violations. Specifically, the server uses a scheduling library to start the security check at the specified time.

[0245] Step 2:

[0246] The server analyzes the results of the security checks to determine if any violations were found. As input, it uses the list of security violations generated in step 1. As output, it generates a notification message containing the details of any violations found. Specifically, the server loops through the list of violations to determine if any exist.

[0247] Step 3:

[0248] If a violation is found, the server will send a push notification to the developer's device the next morning. As input, it uses the notification message generated in step 2. As output, the notification will be displayed on the developer's device. Specifically, the server will use an email sending library to send the notification to the specified email address.

[0249] Step 4:

[0250] The user (developer) receives a notification on their device and checks the details of the violation and how to fix it. The input is the notification message sent from the server. The output is that the user accesses the fix guide and checks the specific fix steps. The specific operation is that the user taps the notification on their device to display the detailed fix guide.

[0251] Step 5:

[0252] The server stores past security check results and correction history in a database and makes them available for reference as needed. It uses security check results and correction history as input and generates historical data stored in the database as output. Specifically, the server connects to the database and executes queries to store the results and history.

[0253] Example 3

[0254] Next, a description will be given of a third embodiment of the third embodiment. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0255] In traditional system development, it was difficult to quickly and accurately reflect the latest security trends, resulting in problems such as a decline in security compliance rates and increased security risks. Furthermore, the creation and notification of security measures was often done manually, which tended to be inefficient and require long lead times. Furthermore, insufficient preparation for security design before development and the setting of milestones based on internal and external data made it difficult to improve the security level of the entire system.

[0256] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0257] In this invention, the server includes means for periodically collecting the latest security trends, means for saving the collected security information in a database, means for analyzing the saved security information, means for generating security measures based on the analysis results, means for notifying the generated security measures, means for enabling the accuracy of security-related answers, security compliance rate, and application of security to the latest trends in system development, means for providing guidance for preparing security design before development, means for providing server development input, and means for enabling the setting of milestones based on internal and external data. This allows the latest security trends to be reflected in system development quickly and accurately, improving security compliance rate and reducing security risks.

[0258] "Latest security trends" refers to the latest information on ongoing security threats and vulnerabilities.

[0259] "Methods of collection" refers to the technologies and methods used to obtain data from reliable sources on the Internet.

[0260] "Means of database storage" refers to the systems and technologies used to store collected information in a structured format.

[0261] "Means for analysis" refers to techniques and methods for analyzing stored data and extracting useful information.

[0262] "Means for generating security measures" refers to the techniques and methods for creating specific security measures based on the analysis results.

[0263] "Means of notification" refers to the techniques and methods for communicating the security measures that have been created to relevant parties.

[0264] "Security answer accuracy" refers to the ability to accurately answer security questions or problems.

[0265] "Security compliance rate" refers to the percentage of actions taken in accordance with security policies and guidelines.

[0266] "Applying security based on the latest trends to system development" refers to reflecting the latest security information in the system development process.

[0267] "Guidance on preparing for security design before development" refers to guidance and advice on planning and preparing necessary security measures in the early stages of system development.

[0268] "Server development input" refers to providing information and data necessary for server development.

[0269] "Setting milestones based on internal and external data" refers to setting important milestones to manage project progress based on internal and external data.

[0270] The present invention provides a system for quickly and accurately reflecting the latest security trends in system development. A specific embodiment of this system will be described below.

[0271] Every Friday, the server collects the latest security trends from trusted security sources on the internet using the Python libraries BeautifulSoup and Requests, for example by scraping information from CVE databases and security blogs.

[0272] The collected security information is stored in a database on the server. The database uses MySQL (registered trademark) or PostgreSQL. The stored information includes the title, content, and publication date of the security information.

[0273] Next, the server analyzes the stored security information. For this analysis, it uses a generative AI model (e.g., OpenAI's GPT-4). Specifically, the security information is input to the generative AI model as a prompt sentence, and the analysis result is obtained.

[0274] Based on the analysis results, the server generates specific security measures, such as "performing escaping of input data" and "using prepared statements" to protect against SQL injection vulnerabilities.

[0275] The server notifies the development team of the generated security measures. Notifications are sent via email or chat tools (e.g., Slack, Microsoft Teams). The notification includes details of the measures and how to implement them.

[0276] The terminal (developer's PC) implements the notified security measures. The developer then modifies the code based on the notified measures to strengthen the security of the system. For example, to counter SQL injection, the developer can change the SQL query part in the code to a prepared statement.

[0277] As a concrete example, the following prompt sentence is input to the generative AI model:

[0278] "According to the information gathered from the latest CVE database, an SQL injection vulnerability has been reported. Please tell me the specific measures to prevent this vulnerability."

[0279] In this way, the server, terminal, and user cooperate to reflect the latest security trends in system development, thereby enhancing security. The flow of the identification process in the third embodiment will be described with reference to FIG.

[0280] Step 1:

[0281] The server collects the latest security trends from reliable security sources on the Internet every Friday. It uses a list of URLs from security sources as input. Specifically, it uses the Python libraries BeautifulSoup and Requests to scrape information from CVE databases and security blogs. The collected security information is then returned as output.

[0282] Step 2:

[0283] The server stores the collected security information in a database. It uses the security information collected in step 1 as input. Specifically, it uses MySQL or PostgreSQL to store the title, content, publication date, etc. of the security information as fields. The output is the security information stored in the database.

[0284] Step 3:

[0285] The server analyzes the stored security information. As input, it uses the security information stored in the database. Specifically, it uses a generative AI model (e.g., OpenAI's GPT-4) and inputs the security information into the generative AI model as a prompt sentence. As output, it obtains the analysis results obtained from the generative AI model.

[0286] Step 4:

[0287] The server generates security measures based on the analysis results. The analysis results obtained in step 3 are used as input. Specific operations include creating specific security measures based on the information obtained from the generative AI model. The generated security measures are obtained as output.

[0288] Step 5:

[0289] The server notifies the development team of the generated security measures. The security measures generated in step 4 are used as input. Specific operations include notifying the development team of the measures via email or chat tools (e.g., Slack, Microsoft Teams). The output is the security measures notified to the development team.

[0290] Step 6:

[0291] The terminal (developer's PC) implements the notified security measures. It uses the security measures notified in step 5 as input. Specific actions include modifying the code based on the notified measures to strengthen the security of the system. The output is a system with the security measures implemented.

[0292] (Application example 3)

[0293] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0294] In modern system development, it is extremely important to quickly and accurately reflect the latest security trends. However, manually checking security trends and incorporating them into systems is time-consuming and labor-intensive, and inefficient. Furthermore, there are a wide range of security-related challenges, such as improving security compliance rates, preparing security designs before development, providing server development input, and setting milestones based on internal and external data. A comprehensive system to solve these challenges is needed.

[0295] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means. In this invention, the server includes: means for enabling security response accuracy, security compliance rate, and application of security to the latest trends in system development; means for providing guidance on preparing for security design before development; means for providing server development input; means for enabling milestone setting based on internal and external data; means for periodically checking the latest security trends and reflecting them in system development; and means for acquiring security trends through an application installed on a smart device and updating system settings. This makes it possible to quickly and efficiently reflect the latest security trends in system development.

[0296] "Security response accuracy" refers to the ability to provide accurate and prompt responses to security questions and problems.

[0297] "Security compliance rate" is an indicator that shows the extent to which a system or organization complies with established security policies and standards.

[0298] "Applying security to the latest trends in system development" refers to incorporating the latest security trends and technologies into the system development process.

[0299] "Guidance on preparing for security design before development" refers to providing guidance and advice on how to properly design security in the early stages of system development.

[0300] "Server development input" refers to providing information and data necessary for server development.

[0301] "Setting milestones based on internal and external data" refers to setting important milestones to manage the progress of a project based on internal and external data.

[0302] "Means of regularly checking the latest security trends and reflecting them in system development" refers to a method of regularly collecting the latest security information and applying it to the system development process.

[0303] "Means of obtaining security trends through applications installed on smart devices and updating system settings" refers to a method of obtaining security information using applications installed on devices such as smartphones and tablets, and updating system settings based on that information.

[0304] As an embodiment of the present invention, a security management system is constructed, which is composed of a server, smart devices, and software that links them together.

[0305] The server is equipped with a means to ensure the accuracy of security-related responses, security compliance rates, and the application of security to the latest trends in system development. Specifically, the server periodically checks the latest security trends and collects data to reflect them in system development. The server obtains the latest security information using a security trend API, analyzes the information, and updates the system settings.

[0306] An application for acquiring security trends and updating system settings is installed on smart devices. This application acquires the latest security information from a server every Friday and notifies the user. The user checks the notification and updates the system settings as necessary.

[0307] In terms of specific hardware, the server is a computer equipped with a high-performance processor and large-capacity storage, and the smart device is a mobile information terminal such as a smartphone or tablet. In terms of software, Python and the requests library are used on the server side, and a dedicated security management application is used on the smart device side.

[0308] For data processing and calculation, the server analyzes the data obtained from the security trend API in JSON format and extracts the necessary information. The extracted information is used to update system settings. On the smart device side, the obtained information is notified to the user and the system settings are updated according to the user's operations.

[0309] As a concrete example, by inputting the following prompt sentence into a generative AI model, a program for a security control system can be generated.

[0310] Example prompt sentence:

[0311] "Write a Python program that checks the latest security trends and provides recommendations for the next week's system development. Include a process to retrieve data from an API every Friday and update the system configuration."

[0312] Using this prompt, the generative AI model can generate the necessary programs to assist in building a security control system.

[0313] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0314] Step 1:

[0315] The server sends a request to the security trend API every Friday. The API endpoint URL and authentication information are required as input. The server retrieves the latest security trend data from the API and receives it in JSON format. The retrieved security trend data is returned as output.

[0316] Step 2:

[0317] The server analyzes the acquired JSON-formatted security trend data. The JSON data acquired in step 1 is required as input. The server extracts the necessary information (e.g., new vulnerability information, recommended security patches, etc.) and stores it in an internal database. The analyzed security information is obtained as output.

[0318] Step 3:

[0319] The server determines whether the system settings need to be updated based on the analyzed security information. The analyzed security information obtained in step 2 is required as input. If the system settings need to be updated, the server generates the necessary content and creates an update script. The output is a system settings update script.

[0320] Step 4:

[0321] An application installed on a smart device retrieves the latest security information from a server every Friday. The server's endpoint URL and authentication information are required as input. The smart device notifies the user of the security information retrieved from the server. The output is the security information notified to the user.

[0322] Step 5:

[0323] The user checks the notification from the smart device and approves the update of the system settings as necessary. The input requires the notification content from the smart device. The user performs the operation of approving or rejecting the notification. The output is the result of the user's approval or rejection.

[0324] Step 6:

[0325] After receiving user approval, the smart device instructs the server to update the system settings. The user's approval result is required as input. The smart device requests the server to execute an update script. The output is an update request to the server.

[0326] Step 7:

[0327] The server receives an update request from the smart device and executes the system settings update script. The input requires an update request from the smart device. The server updates the system settings and sends the update results back to the smart device. The output is the updated system settings and the results.

[0328] Step 8:

[0329] The smart device receives the update results from the server and notifies the user. The input requires the update results from the server. The smart device displays the update results to the user and prompts the user to perform additional operations as necessary. The output is the update results notified to the user.

[0330] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0331] "Example 1"

[0332] As one embodiment of the present invention, a system incorporating an emotion engine is provided. This system recognizes a user's emotions and adjusts the accuracy of security-related answers based on those emotions. Specifically, if the system recognizes that the user is feeling anxious or scared, it provides more detailed security information to enhance the user's sense of security. Also, if the system recognizes that the user is feeling safe, it provides only the minimum necessary security information, allowing the user to perform operations smoothly.

[0333] "Example 2"

[0334] In another embodiment of the present invention, a system is provided in which an emotion engine improves security compliance based on a user's emotions. Specifically, if the system recognizes that the user is feeling anger or frustration, it identifies the security violation that caused it and suggests specific actions to improve it. In this way, by providing security measures that take the user's emotions into consideration, the system improves the user's security compliance.

[0335] "Example 3"

[0336] Furthermore, as another embodiment of the present invention, a system is provided in which an emotion engine adjusts the system's security measures based on the user's emotions. Specifically, if the system recognizes that the user is feeling happy or excited, it uses that emotion to suggest new security measures. For example, if the system recognizes that the user is excited about a new function, it suggests new security measures related to that function. In this way, by suggesting security measures that utilize the user's emotions, it is possible to improve the user's security awareness.

[0337] The processing flow of each embodiment will be described below.

[0338] "Example 1"

[0339] Step 1: The system recognizes the user's emotions using the emotion engine.

[0340] Step 2: If the emotion engine recognizes the user's emotion as anxiety or fear, the system will provide more detailed security information.

[0341] Step 3: If the emotion engine recognizes the user's emotion as safe, the system provides only the minimum necessary security information.

[0342] "Example 2"

[0343] Step 1: The system recognizes the user's emotions using the emotion engine.

[0344] Step 2: If the emotion engine identifies the user's emotion as anger or frustration, the system identifies the underlying security breach.

[0345] Step 3: The system suggests specific actions to remediate the identified security violations.

[0346] "Example 3"

[0347] Step 1: The system recognizes the user's emotions using the emotion engine.

[0348] Step 2: If the emotion engine recognizes the user's emotion as joy or excitement, the system will use that emotion to suggest new security measures.

[0349] Step 3: The system presents the proposed new security measures to the user.

[0350] Example 1

[0351] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0352] Conventional security systems provide uniform security information without considering users' feelings, which has the problem of not being able to fully alleviate users' anxiety and fear. Furthermore, the accuracy of security-related responses is low, making it difficult to improve security compliance rates. Furthermore, it is difficult to apply the latest security trends in system development, and pre-development security design preparation is often insufficient.

[0353] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0354] In this invention, the server includes means for improving the accuracy of security-related answers, means for improving the security compliance rate, means for enabling the application of security to the latest trends in system development, means for providing guidance for preparing security design before development, means for providing server development input, means for enabling the setting of milestones based on internal and external data, means for recognizing user emotions and adjusting the accuracy of security-related answers based on the emotions, means for generating optimal security answers using a machine learning model, means for adjusting the generated answers based on the emotion analysis results, and means for providing the adjusted security answers to the user. This makes it possible to provide optimal security information according to the user's emotions, reduce user anxiety and fear, and improve the security compliance rate.

[0355] "Security Answer Accuracy" refers to the accuracy and appropriateness of the answers provided to security questions.

[0356] "Security compliance rate" refers to the percentage of systems and users that comply with security policies and guidelines.

[0357] "Applying the latest trends in security to system development" refers to incorporating the latest security technologies and trends into the system development process.

[0358] "Guidance on preparing for security design before development" refers to providing guidance and advice on how to properly design security in the early stages of system development.

[0359] "Server development input" refers to providing information and data necessary for server development.

[0360] "Setting milestones based on internal and external data" refers to using internal and external data to set important milestones for a project.

[0361] "Recognizing user emotions" refers to analyzing and understanding the user's emotional state from their input and behavior.

[0362] "Adjusting the accuracy of security-related answers based on emotions" refers to changing the detail and content of the security information provided depending on the user's emotional state.

[0363] "Generating optimal security answers using machine learning models" refers to using machine learning algorithms to generate optimal security answers to user questions.

[0364] "Adjusting the generated answer based on the result of sentiment analysis" refers to appropriately changing the content of the generated security answer to reflect the result of sentiment analysis.

[0365] "Providing a tailored security answer to a user" refers to conveying tailored security information to a user according to the user's emotional state.

[0366] The present invention provides a system for improving the accuracy of security-related answers, which is characterized by recognizing a user's emotions and adjusting the accuracy of security-related answers based on those emotions. Specific embodiments of this system are described below.

[0367] System configuration

[0368] This system mainly consists of three elements: a server, a terminal, and a user. The server generates security-related answers and analyzes the user's emotions. The terminal receives input from the user and communicates with the server. The user enters security-related questions into the terminal.

[0369] Hardware and software used

[0370] Server: A server with high-performance computing power is required. Specifically, a cloud-based server (e.g., Amazon Web Services or Google® Cloud Platform) can be used.

[0371] Emotion Engine: Uses Google Cloud Natural Language API and Microsoft Azure Emotion API to analyze user emotions.

[0372] Machine learning models: Use TensorFlow and PyTorch to generate security answers.

[0373] Data processing and calculation

[0374] 1. Data Collection:

[0375] The user enters a security question into the terminal, for example, "Tell me about recent phishing scams."

[0376] The terminal transmits this input data to the server.

[0377] 2. Emotion recognition:

[0378] The server passes the received user input data to an emotion engine and analyzes the emotion. For example, it uses the Google Cloud Natural Language API to analyze whether the user is feeling "anxious."

[0379] 3. Security Answer Generation:

[0380] The server uses machine learning models (TensorFlow and PyTorch) to generate optimal security answers to user questions, including detailed information on the latest phishing scams and how to prevent them.

[0381] 4. Adjusting your answers:

[0382] The server adjusts the generated answer based on the results of the sentiment analysis. If the user feels anxious, the answer is adjusted to provide detailed security information. Conversely, if the user feels reassured, the answer is adjusted to provide only the minimum necessary information.

[0383] 5. Providing answers:

[0384] The server sends the adjusted security answer to the terminal, and the terminal displays the received security answer to the user, who then confirms the answer through the terminal.

[0385] Specific examples

[0386] If a user asks, "Tell me about recent phishing scams," the system proceeds as follows:

[0387] 1. The user types "Tell me about recent phishing scams" into the terminal.

[0388] 2. The terminal sends this input data to the server.

[0389] 3. The server uses the Google Cloud Natural Language API to analyze the user's emotions and recognizes that they are feeling "anxiety."

[0390] 4. The server uses TensorFlow to generate detailed security answers including the latest phishing scam techniques and countermeasures.

[0391] 5. The server tailors its response to provide more security information because the user is concerned.

[0392] 6. The server sends the adjusted security answer to the terminal.

[0393] 7. The terminal displays the received security answer to the user, who then checks the detailed security information through the terminal.

[0394] Prompt Sentence Examples

[0395] "Users are asking about recent phishing scams. Analyze their sentiment and provide them with relevant security information."

[0396] In this way, the server realizes a system that provides optimal security answers according to the user's emotions.

[0397] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0398] Step 1:

[0399] Collecting user input data

[0400] The user enters a security question into the terminal, for example, "Tell me about recent phishing scams."

[0401] The terminal transmits this input data to the server.

[0402] Input: User question text

[0403] Output: User question data sent to the server

[0404] Step 2:

[0405] emotion recognition

[0406] The server passes the received user input data to an emotion engine and analyzes the emotion. For example, it uses the Google Cloud Natural Language API to analyze whether the user is feeling "anxious."

[0407] Input: User question data

[0408] Data processing: Emotion analysis using an emotion engine

[0409] Output: Sentiment analysis results (e.g., anxiety, relief, etc.)

[0410] Step 3:

[0411] Security Answer Generation

[0412] The server uses machine learning models (TensorFlow and PyTorch) to generate optimal security answers to user questions, including detailed information on the latest phishing scams and how to prevent them.

[0413] Input: User question data, sentiment analysis results

[0414] Data Computation: Answer Generation with Machine Learning Models

[0415] Output: Initial security answer

[0416] Step 4:

[0417] Adjusting your answers

[0418] The server adjusts the generated answer based on the results of the sentiment analysis. If the user feels anxious, the answer is adjusted to provide detailed security information. Conversely, if the user feels reassured, the answer is adjusted to provide only the minimum necessary information.

[0419] Input: Initial security answer, sentiment analysis results

[0420] Data processing: Adjusting responses based on sentiment analysis results

[0421] Output: Adjusted security answers

[0422] Step 5:

[0423] Providing answers

[0424] The server sends the adjusted security answer to the terminal.

[0425] The terminal displays the received security answer to the user, who then confirms the answer through the terminal.

[0426] Input: Tailored security answer

[0427] Output: The security answers displayed to the user

[0428] In this way, the server realizes a system that provides optimal security answers according to the user's emotions.

[0429] (Application example 1)

[0430] Next, a description will be given of Application Example 1 of Embodiment Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0431] Conventional security systems provide uniform security information without considering the user's emotional state, which means that they may not be able to provide appropriate information even when the user is feeling anxious or scared.In addition, while there is a demand for improved accuracy in security-related responses and security compliance rates, there has been a lack of effective means to achieve this.

[0432] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. In this invention, the server includes means for enabling security response accuracy, security compliance rate, and application of security to the latest trends in system development, means for providing guidance for preparation of security design before development, means for providing server development input, means for enabling milestone setting based on internal and external data, means for recognizing user emotions and adjusting the method of providing security information based on those emotions, and means for generating optimal security answers using machine learning and artificial intelligence. This makes it possible to provide appropriate security information according to the user's emotional state, thereby improving the accuracy of security response and security compliance rate.

[0433] "Security answer accuracy" refers to the ability to provide accurate and relevant answers to security questions or problems.

[0434] "Security compliance rate" refers to the percentage of actions taken in accordance with security policies and guidelines.

[0435] "Applying security to the latest trends in system development" refers to applying security measures in system development that incorporates the latest technological trends and developments.

[0436] "Guide to Preparing for Security Design Before Development" provides guidance and advice for designing security in the early stages of system development.

[0437] "Server development input" means providing information and data necessary for server development.

[0438] "Setting milestones based on internal and external data" means using data from both inside and outside the company to set important milestones that indicate the progress of a project.

[0439] "Means for recognizing a user's emotions and adjusting the way security information is provided based on those emotions" refers to technology that detects a user's emotional state and changes the content and level of detail of the security information provided depending on that state.

[0440] "Means for generating optimal answers to security-related questions and problems using machine learning and artificial intelligence" refers to technology that uses machine learning and artificial intelligence techniques to automatically generate optimal answers to security-related questions and problems.

[0441] As an embodiment of the present invention, we will describe a system in which an emotion-aware security assistant is installed on a smartphone, which recognizes the user's emotions in real time and adjusts the way security information is provided based on the user's emotions.

[0442] The server uses the following hardware and software. The hardware uses a smartphone camera, and the software uses OpenCV for face detection and Keras for emotion recognition. This makes it possible to capture a user's facial image and recognize their emotions.

[0443] Specifically, the smartphone camera captures the user's face and detects it using OpenCV. The detected facial region is input into an emotion recognition model using Keras to predict the user's emotion. Based on the predicted emotion, the server adjusts how security information is provided. For example, if the user feels anxious, the server provides detailed security information to increase the user's sense of security. On the other hand, if the user feels reassured, the server provides only the minimum necessary information to ensure smooth operation.

[0444] This system makes it possible to provide appropriate security information according to the user's emotional state, thereby improving the accuracy of security-related responses and security compliance rates.

[0445] For example, when a user uses a smartphone, the camera captures the user's face and recognizes their emotions. For example, if the user feels anxious, the server displays a message saying, "We will provide you with detailed security information."

[0446] Examples of prompts to be input to a generative AI model include:

[0447] "If the user feels uneasy, create an application that provides detailed security information. The user's emotions are recognized from the facial images captured by the camera."

[0448] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0449] Step 1:

[0450] The smartphone camera captures the user's face. The input is the camera image, and the output is the captured facial image. Specifically, the camera captures the image in real time and cuts out the facial part from it.

[0451] Step 2:

[0452] The device uses OpenCV to detect faces. The input is a captured face image, and the output is the face's location (coordinates). Specifically, the face detection algorithm identifies the face's location in the image and obtains its coordinates.

[0453] Step 3:

[0454] The device inputs the detected face area into an emotion recognition model using Keras. The input is a face image extracted based on the face's position information, and the output is the emotion prediction result. Specifically, the face image is preprocessed and input into the emotion recognition model to predict the emotion.

[0455] Step 4:

[0456] The server receives the prediction results of the emotion recognition model and identifies the user's emotion. The input is the emotion prediction result, and the output is the identified emotion (e.g., anxiety, relief). Specifically, the server analyzes the prediction results and selects the emotion with the highest probability.

[0457] Step 5:

[0458] The server adjusts the way it provides security information based on the identified emotion. The input is the identified emotion, and the output is adjusted security information. Specifically, if the user feels anxious, detailed security information is generated, and if the user feels reassured, only the minimum necessary information is generated.

[0459] Step 6:

[0460] The terminal displays the adjusted security information to the user. The input is the adjusted security information, and the output is the information displayed to the user. Specifically, the generated security information is displayed on the screen and provided to the user.

[0461] Through the above processing steps, appropriate security information is provided according to the user's emotional state.

[0462] Example 2

[0463] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0464] Conventional systems have made it difficult to improve security compliance rates or quickly correct security violations. Furthermore, they do not provide security measures that take user emotions into account, potentially increasing user dissatisfaction and stress. Furthermore, automation of security checks and notification of violations are insufficient, resulting in situations where developers are unable to respond quickly. To solve these issues, a system that integrates regular security checks with the ability to analyze user emotions is needed.

[0465] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0466] In this invention, the server includes a means for enabling the accuracy of security-related answers, security compliance rate, and application of security to the latest trends in system development, a means for providing guidance on preparing for security design before development, a means for providing server development input, a means for enabling the setting of milestones based on internal and external data, a means for periodically performing security checks and, if a violation is found, for immediately notifying the user to make a correction, and a means for analyzing user emotions, identifying the cause of the security violation, and proposing remedial measures. This enables an improvement in security compliance rate and rapid security measures that take user emotions into consideration.

[0467] "Security answer accuracy" refers to the ability to provide accurate and prompt answers to security questions and problems.

[0468] "Security compliance rate" refers to the percentage of systems and users that comply with security policies and guidelines.

[0469] "Applying security to the latest trends in system development" refers to applying security measures in system development based on the latest technologies and trends.

[0470] "Guidance on preparing for security design before development" refers to providing guidance and advice on how to design with security in mind in the early stages of system development.

[0471] "Server development input" refers to providing information and data necessary for server development.

[0472] "Setting milestones based on internal and external data" refers to setting important milestones that indicate the progress of a project based on internal and external data.

[0473] "A means of conducting regular security checks and immediately notifying the user to make corrections if a violation is found" refers to a function that periodically checks the security status of the system and notifies the user to make corrections promptly if a problem is found.

[0474] "Means of analyzing user emotions, identifying the cause of a security breach, and proposing remedial measures" refers to a function that analyzes user emotional data to identify the cause of a security breach and proposes specific remedial measures.

[0475] The present invention provides a system for improving security compliance and providing security measures that take into consideration user emotions. Specific embodiments of this system will be described below.

[0476] Security Check Function

[0477] 1. The server starts a security check every day at midnight. This check is triggered automatically using a cron job.

[0478] 2. The server launches a security scanner, such as an open-source or commercial security scanner, to scan the entire system, specifically targeting web application endpoints, database configuration files, etc.

[0479] 3. The server analyzes the scan results and logs any violations found. For example, if a SQL injection vulnerability is found, it identifies the line number and file name of the code and logs a message saying, "A SQL injection vulnerability was found on a specific line in a specific file."

[0480] 4. At 8:00 AM the next morning, the server notifies the developer with details of the detected security violation. This notification is sent via email or chat tool. For example, a notification with the following content may be sent: "Yesterday's security check found an SQL injection vulnerability. File: Specific file, Line number: Specific line. How to fix: Use a parameterized query."

[0481] Emotion engine for improving security compliance

[0482] 1. While the user is operating the system, the device analyzes facial expressions and tone of voice in real time through a camera and microphone, using "emotion recognition software" and "cloud-based emotion analysis API."

[0483] 2. The device then sends the analyzed emotional data to the server. For example, if a user experiences an error while trying to change security settings and feels angry, the device will send data about their facial expression and tone of voice at that moment.

[0484] 3. The server analyzes the received sentiment data to identify the cause of the user's dissatisfaction. For example, if a particular security setting is complex and difficult to understand, it identifies the security violation related to that setting.

[0485] 4. The server suggests specific actions to the user to remediate the identified security violations. These suggestions are made via pop-up notifications or emails, providing specific advice such as "Try the following steps to simplify this configuration."

[0486] Examples of concrete examples and prompts

[0487] Specific examples

[0488] Security check function: Every night at midnight, an "open source security scanner" is used to perform security scans of web applications to detect SQL injection vulnerabilities. The next morning, a notification is sent to the developer stating, "SQL injection vulnerability found. File: specific file, line number: specific line. How to fix: Use a parameterized query."

[0489] Emotion engine for improved security compliance: If a user experiences an error while trying to change a security setting and feels angry, emotion recognition software can detect that emotion. The server then uses this information to identify the security setting that is causing the error and suggests specific actions, such as "Try these steps to simplify this setting."

[0490] Prompt Sentence Examples

[0491] "Please explain in detail the steps of the system that runs security checks every night and notifies developers the next morning if a violation is found."

[0492] Please explain in detail the process steps of a system that analyzes user sentiment, identifies the cause of a security breach, and suggests remediation measures.

[0493] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0494] Security Check Function

[0495] Step 1:

[0496] The server begins security checks every day at midnight.

[0497] Input: Cron job trigger

[0498] What it does: A cron job runs the security check script at the scheduled time.

[0499] Output: Security scanner launch

[0500] Step 2:

[0501] The server uses a security scanner to scan the entire system.

[0502] Input: Security scanner configuration file and list of scan targets

[0503] How it works: The security scanner scans web application endpoints and database configuration files.

[0504] Output: Report of scan results

[0505] Step 3:

[0506] The server analyzes the scan results and logs details of any violations found.

[0507] Input: Scan results report

[0508] What it does: Analyzes scan results and extracts detailed information about each violation (e.g., type of vulnerability, scope of impact, and how to remediate it).

[0509] Output: Violation details recorded in a log file

[0510] Step 4:

[0511] The server will notify the developer at 8am the next morning with details of the detected security breach.

[0512] Input: Violation details recorded in the log file

[0513] How it works: The notification system sends notifications to developers via email and chat tools.

[0514] Output: Notification sent to developer

[0515] Emotion engine for improving security compliance

[0516] Step 1:

[0517] While the user is operating the system, the device analyzes facial expressions and tone of voice in real time through a camera and microphone.

[0518] Input: Camera video, microphone audio

[0519] How it works: Emotion recognition software analyzes facial expressions and tone of voice to identify a user's emotional state.

[0520] Output: Emotion data

[0521] Step 2:

[0522] The device transmits the analyzed emotion data to the server.

[0523] Input: Emotion data

[0524] How it works: Uses a communication protocol to send emotion data to a server.

[0525] Output: Emotion data sent to the server

[0526] Step 3:

[0527] The server analyzes the received emotional data and identifies the cause of the user's dissatisfaction.

[0528] Input: Emotion data

[0529] Behavior: Analyzes emotional data to determine whether a particular action or setting is causing user frustration.

[0530] Output: Information about the cause of the complaint

[0531] Step 4:

[0532] The server suggests specific actions to the user to remedy identified security violations.

[0533] Input: Information about the cause of the complaint

[0534] What it does: Generates suggestions and sends them to the user via a popup notification and / or email.

[0535] Output: Proposal notification sent to user

[0536] (Application example 2)

[0537] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0538] In modern system development, it is difficult to simultaneously improve security compliance and user satisfaction. In particular, there is a need for systems that can respond quickly when security violations occur and suggest specific improvement actions to reduce user frustration and anger. Furthermore, security compliance must be improved by conducting regular security checks and providing immediate notification when violations are found.

[0539] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0540] In this invention, the server includes: a means for enabling the accuracy of security-related answers, the security compliance rate, and the application of security to the latest trends in system development; a means for providing guidance on preparing for security design before development; a means for providing server development input; a means for enabling the setting of milestones based on internal and external data; a means for periodically checking security and immediately notifying if a violation is found; and a means for analyzing user emotions and, if a security violation causes user dissatisfaction or anger, identifying the cause and proposing specific improvement actions. This makes it possible to simultaneously improve the security compliance rate and increase user satisfaction.

[0541] "Security answer accuracy" refers to the ability to provide accurate and prompt answers to security questions and problems.

[0542] "Security compliance rate" is an index that indicates the extent to which systems and users comply with security policies and guidelines.

[0543] "Applying security to the latest trends in system development" refers to introducing appropriate security measures for the latest system development technologies and trends.

[0544] "Guidance on Pre-Development Security Design Preparation" refers to providing guidance and advice on planning and designing necessary security measures in the early stages of system development.

[0545] "Server development input" refers to providing information and data necessary for server development.

[0546] "Setting milestones based on internal and external data" refers to using internal and external data to set important milestones to manage project progress.

[0547] "Means for performing regular security checks" refers to methods or processes for checking the security status of a system at regular intervals and inspecting for any problems.

[0548] "Means for immediate notification if a breach is detected" refers to a method or system for quickly communicating information to relevant parties when a security breach is detected.

[0549] "Means for analyzing a user's emotions" refers to techniques or methods for analyzing a user's facial expressions, tone of voice, etc. to determine their emotional state.

[0550] "Means for identifying the cause and proposing specific remedial actions when a security violation causes user dissatisfaction or anger" refers to a method or system that clarifies the cause and presents solutions when a security violation causes negative emotions in users.

[0551] As an embodiment of the present invention, a system is provided that simultaneously improves security compliance and increases user satisfaction. Specific embodiments of the system are described below.

[0552] System Program

[0553] The system includes a program that integrates security checks, sentiment analysis, and notification functions. The program consists of the following main components:

[0554] 1. Security check module: Periodically checks the system's security status and checks for any violations. If any violations are found, the module returns a list of the violations.

[0555] 2. Emotion Analysis Module: Analyzes the user's facial expressions and tone of voice to determine their emotional state. Data is collected using the camera and microphone, and analyzed using the emotion engine.

[0556] 3. Notification module: Sends appropriate notifications when a security breach is detected or when the user's sentiment is negative. Notifications can be sent via email or in-app notifications.

[0557] Hardware and Software

[0558] Hardware: Server, user device (smartphone, tablet, PC), camera, microphone

[0559] Software: Security check software, Emotion Engine, Notification Service

[0560] Data processing and calculation

[0561] Security Check: The server periodically scans system logs and configuration files to detect security violations. If any violations are found, the information is saved as a list.

[0562] Emotion analysis: The camera and microphone on the user's device are used to collect data on the user's facial expressions and voice, which are then sent to the emotion engine for analysis.

[0563] Notification: If a security breach or negative sentiment is detected, the notification module generates an appropriate message and sends it to the relevant parties.

[0564] Specific examples

[0565] For example, if a security breach is detected while a user is using their smartphone and the user is unhappy, the system will send a notification like this:

[0566] Developer notification: "Security breach detected: weak password"

[0567] User notification: "We suggest the following improvement action: Strengthen your password."

[0568] Prompt Sentence Examples

[0569] Examples of prompts to input to a generative AI model might include:

[0570] If a security breach is detected while a user is using their smartphone and the user is frustrated, what notification should the system send?

[0571] In this way, a system can be provided that simultaneously improves security compliance and increases user satisfaction.

[0572] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0573] Step 1:

[0574] The server periodically performs security checks. As input, it obtains system logs and configuration files and scans these data using security check software. As data processing, it analyzes the logs and configuration files to detect security violations. As output, if any violations are found, it saves the information as a list.

[0575] Step 2:

[0576] When a security violation is detected, the server launches a notification module. As input, it receives a list of security violations and generates a notification message. As data processing, it creates an appropriate message based on the violation details. As output, it sends an email or an in-app notification to the developer.

[0577] Step 3:

[0578] The user device periodically activates the emotion analysis module. As input, it uses a camera and microphone to collect data on the user's facial expressions and voice. As data processing, it sends this data to the emotion engine for analysis. As output, it determines the user's emotional state.

[0579] Step 4:

[0580] If the user's emotional state is negative, the server re-launches the notification module. It receives the analysis results from the emotion engine as input and generates a message suggesting an improvement action. It processes the data and selects an appropriate improvement action based on the user's emotional state. It outputs an email or in-app notification to the user.

[0581] Step 5:

[0582] The server uses a generative AI model to generate prompt sentences based on the user's emotional state and security breaches. As input, it receives the user's emotional data and security breach data and inputs them into the generative AI model. As data processing, the generative AI model generates prompt sentences. As output, it sends the prompt sentences to the notification module, which issues appropriate notifications.

[0583] Step 6:

[0584] The user receives the notification and performs the suggested improvement action. As input, the user receives the notification message and confirms the suggested action. As data processing, the user performs the suggested action and improves security compliance. As output, the security status is improved.

[0585] Example 3

[0586] Next, a description will be given of a third embodiment of the third embodiment. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0587] In conventional system development, it was difficult to quickly reflect the latest security trends, making it difficult to improve security compliance. Furthermore, security measures were not proposed taking user emotions into consideration, and efforts to improve user security awareness were insufficient. Furthermore, there was a lack of means to utilize generative AI models in proposing security measures.

[0588] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0589] In this invention, the server includes a means for periodically checking the latest security trends and reflecting them in system development, a means for adjusting security measures based on user sentiment, and a means for proposing security measures using a generative AI model. This allows the latest security trends to be quickly reflected in system development and security measures that take user sentiment into consideration. Furthermore, by utilizing the generative AI model, more effective security measures can be proposed.

[0590] "Security answer accuracy" refers to the ability to provide accurate and prompt answers to security questions and problems.

[0591] "Security compliance rate" is an indicator that shows the extent to which a system or organization complies with established security policies and standards.

[0592] "Applying the latest trends in security to system development" refers to incorporating the latest security information and technologies into the system development process.

[0593] "Guidance on preparing for security design before development" refers to providing guidance and advice on security design in the early stages of system development.

[0594] "Server development input" refers to providing information and data necessary for server development.

[0595] "Setting milestones based on internal and external data" refers to setting important milestones for a project based on internal and external data.

[0596] "Means of regularly checking the latest security trends and reflecting them in system development" refers to a method of regularly collecting the latest security information and incorporating it into the system development process.

[0597] "Means for adjusting security measures based on user emotions" refers to a method for analyzing a user's emotional state and proposing or implementing security measures accordingly.

[0598] "Means for proposing security measures using generative AI models" refers to a method that utilizes generative AI technology to automatically suggest appropriate security measures.

[0599] "Means for analyzing user emotion data" refers to techniques and methods for analyzing a user's emotions, which allow the user's emotional state to be understood.

[0600] This invention is a system that periodically checks the latest security trends, reflects the results in system development, and adjusts security measures based on user sentiment. A specific embodiment of this system will be described below.

[0601] Check and reflect security trends

[0602] The server accesses reliable security information sources on the Internet (e.g., NIST's CVE database, security blogs) every Friday. It uses the Python requests library to retrieve the latest security information from these sources. Specifically, it uses the requests.get method to send a request to the API endpoint of the CVE database and parses the JSON data received as a response.

[0603] The acquired security information is analyzed using natural language processing libraries (e.g., NLTK, spaCy). Security measures necessary for system development are extracted from the analysis results. Detailed information about vulnerabilities is extracted from the JSON data, and their severity and scope of impact are evaluated.

[0604] The server automatically generates tasks in a project management tool (e.g., JIRA or Trello) to reflect the extracted security measures in the next week's system development plan. It uses the JIRA API to create new tasks and assign them to responsible parties.

[0605] Adjusting security measures with an emotion engine

[0606] The device uses an emotion engine (e.g., IBM Watson (registered trademark), Microsoft Azure Emotion API) to analyze the user's emotions in real time. The device captures the user's facial expressions and tone of voice through the camera and microphone and sends them to the emotion engine. Specifically, the device sends image data captured by the camera to the Emotion API, and analyzes the returned emotion data.

[0607] The emotion data obtained as a result of the analysis is sent from the device to the server using an HTTP POST request.

[0608] The server proposes new relevant security measures based on the received emotion data. A generative AI model (e.g., OpenAI GPT-3) is used to generate a security measure proposal based on the user's emotion. Specifically, the generative AI model is input with a prompt such as, "If the user is excited about a new feature, please propose a security measure related to that feature," and a proposal is generated.

[0609] The user reviews the proposed security measures and applies them as necessary. The device applies the proposed security measures to the system based on the user's selection. For example, if the user selects to implement two-factor authentication, the device changes the system settings to enable two-factor authentication.

[0610] Examples of concrete examples and prompts

[0611] Checking and reflecting security trends: Every Friday, the server checks the NIST CVE database to collect information on newly discovered vulnerabilities. The collected information is analyzed and reflected in the system development plan for the following week.

[0612] Adjusting security measures using an emotion engine: If the device analyzes the user's facial expressions and recognizes that the user is excited about a new feature, the server will suggest security measures related to that feature (e.g., introducing two-factor authentication).

[0613] Example prompt sentence:

[0614] "How can I check the latest security trends and incorporate them into next week's system development plan?"

[0615] "Explain how you can adjust your security measures based on user sentiment."

[0616] In this way, the system provides security measures that take into account the user's feelings while incorporating the latest security trends. The flow of the identification process in the third embodiment will be described with reference to FIG.

[0617] Step 1:

[0618] The server accesses trusted security information sources on the Internet every Friday. Specifically, it uses the Python requests library to retrieve the latest security information from the NIST CVE database and security blogs. The input requires the URL of the security information source, and the output is JSON data of the retrieved security information.

[0619] Step 2:

[0620] The server analyzes the acquired security information. It uses a natural language processing library (e.g., NLTK, spaCy) to extract detailed vulnerability information from the JSON data and evaluate its severity and scope of impact. The input required is the JSON data acquired in step 1, and the output is a list of analyzed security measures.

[0621] Step 3:

[0622] Based on the analysis results, the server automatically generates tasks in a project management tool (e.g., JIRA, Trello) to be reflected in the next week's system development plan. Specifically, it uses the JIRA API to create new tasks and assign them to responsible parties. The list of security measures obtained in step 2 is required as input, and the tasks registered in the project management tool are obtained as output.

[0623] Step 4:

[0624] The device uses an emotion engine (e.g., IBM Watson, Microsoft Azure Emotion API) to analyze the user's emotions in real time. The device captures the user's facial expressions and tone of voice through a camera or microphone and sends them to the emotion engine. The input requires data captured by the camera or microphone, and the output is analyzed emotional data.

[0625] Step 5:

[0626] The device sends the emotion data obtained as a result of the analysis to the server. Specifically, it sends the emotion data to the server using an HTTP POST request. The emotion data obtained in step 4 is required as input, and the emotion data sent to the server is obtained as output.

[0627] Step 6:

[0628] The server proposes relevant new security measures based on the received emotion data. It uses a generative AI model (e.g., OpenAI GPT-3) to generate a security measure proposal based on the user's emotion. The input requires the emotion data obtained in step 5 and a prompt to the generative AI model, and the output is the generated security measure proposal.

[0629] Step 7:

[0630] The user reviews the proposed security measures and applies them as necessary. The terminal applies the proposed security measures to the system based on the user's selection. The inputs are the proposal generated in step 6 and the user's selection, and the output is the applied security measures.

[0631] In this way, the system provides security measures that take into account the user's feelings while incorporating the latest security trends.

[0632] (Application example 3)

[0633] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0634] Conventional security systems have difficulty quickly reflecting the latest security trends and have not proposed security measures that take user emotions into consideration. As a result, they have not been able to sufficiently improve security compliance rates or user security awareness.

[0635] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0636] In this invention, the server includes means for enabling the accuracy of security-related answers, security compliance rate, and application of security to the latest trends in system development, means for providing guidance on preparing for security design before development, means for providing server development input, means for enabling the setting of milestones based on internal and external data, means for analyzing user sentiment and proposing security measures based on the sentiment, and means for periodically checking the latest security trends and reflecting them in system development. This makes it possible to quickly reflect the latest security trends in system development and propose security measures that take user sentiment into consideration.

[0637] "Security answer accuracy" refers to the ability to provide accurate and prompt answers to security questions and problems.

[0638] "Security compliance rate" is an indicator that shows how well systems and users comply with security policies and standards.

[0639] "Applying the latest trends in security to system development" refers to incorporating the latest security information and technologies into the system development process.

[0640] "Guidance on preparing for security design before development" refers to providing guidance and advice on security design in the early stages of system development.

[0641] "Server development input" refers to providing information and data necessary for server development.

[0642] "Setting milestones based on internal and external data" refers to setting important milestones for a project based on internal and external data.

[0643] "Means for analyzing user emotions and proposing security measures based on those emotions" refers to a method for analyzing user emotions and proposing appropriate security measures based on the results.

[0644] "Means of regularly checking the latest security trends and reflecting them in system development" refers to a method of regularly collecting the latest security information and reflecting it in the system development process.

[0645] As an embodiment of the present invention, the following system can be constructed.

[0646] System configuration

[0647] The system consists of the following main components:

[0648] 1. Security Trend Check Module: Regularly check the latest security trends and reflect them in system development.

[0649] 2. Sentiment analysis module: Analyzes user emotions and suggests security measures based on those emotions.

[0650] 3. Security Design Guidance Module: Provides guidance on preparing for security design before development.

[0651] 4. Server development input module: Provides information and data necessary for server development.

[0652] 5. Milestone Setting Module: Set important milestones for your project based on internal and external data.

[0653] Hardware and software used

[0654] Hardware: Smartphones, servers

[0655] Software: Python, Requests library, EmotionRecognizer library

[0656] Data processing and calculation

[0657] 1. Security Trend Check Module:

[0658] The server retrieves the latest security information via API every Friday.

[0659] The acquired information is analyzed and reflected in system development.

[0660] 2. Sentiment Analysis Module:

[0661] Analyzes user input data (voice and text) and recognizes emotions.

[0662] Based on the recognized emotions, appropriate security measures are suggested.

[0663] 3. Security Design Guidance Module:

[0664] Provide guidance and advice on pre-development security design.

[0665] 4. Server Development Input Module:

[0666] Provides information and data necessary for server development.

[0667] 5. Milestone Setting Module:

[0668] Set key project milestones based on internal and external data.

[0669] Specific examples

[0670] For example, if a user is excited about a new app feature, the system can suggest security measures related to that feature. The system also automatically retrieves the latest security information every Friday and updates it, ensuring that users always have the latest security measures.

[0671] Prompt Sentence Examples

[0672] When a user gets excited about a new app feature, how can you suggest security measures related to that feature?

[0673] In this way, by proposing security measures that utilize the user's emotions, it is possible to improve the user's security awareness.

[0674] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0675] Step 1:

[0676] The server retrieves the latest security information through an API every Friday. The input is the API endpoint and the output is the latest security information. The server uses the Requests library to send API requests and parse the retrieved data.

[0677] Step 2:

[0678] The server analyzes the latest security information it has acquired and reflects it in system development. The input is the latest security information, and the output is updated security measures. The server updates the system's security settings based on the analysis results.

[0679] Step 3:

[0680] The device collects user input data (voice or text). The input is the user's voice or text data, and the output is the collected data. The device collects data using a microphone or keyboard.

[0681] Step 4:

[0682] The server analyzes the collected user input data using the EmotionRecognizer library to recognize the user's emotions. The input is the user's voice or text data, and the output is the recognized emotion. The server analyzes the data using an emotion analysis model.

[0683] Step 5:

[0684] The server proposes appropriate security measures based on the recognized emotion. The input is the recognized emotion, and the output is the proposed security measures. The server retrieves security measures corresponding to the emotion from the database and proposes them to the user.

[0685] Step 6:

[0686] The server provides guidance and advice on pre-development security design. The input is development project information, and the output is security design guidance. The server analyzes the project information and proposes an appropriate security design.

[0687] Step 7:

[0688] The server provides the information and data required for server development. The input is the requirements for the development project, and the output is the information and data provided. The server collects the necessary data based on the requirements and provides it to the development team.

[0689] Step 8:

[0690] The server sets key milestones for the project based on internal and external data. The input is internal and external data, and the output is the set milestones. The server analyzes the data and sets milestones to manage the progress of the project.

[0691] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0692] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0693] Another example of generative AI is Gemini (registered trademark) (Internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[0694] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0695] [Second embodiment]

[0696] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0697] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0698] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0699] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0700] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0701] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0702] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0703] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0704] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0705] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0706] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0707] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0708] "Example 1"

[0709] The system of the present invention uses algorithms based on the latest security technologies to improve the accuracy of security-related answers. Specifically, it utilizes machine learning and artificial intelligence to generate optimal answers to security-related questions.

[0710] "Example 2"

[0711] In addition, as a means of improving security compliance, the system periodically checks security and immediately notifies developers to correct any violations that are found. Specifically, the system performs security checks late at night every day, and if a violation is found, it notifies developers the following morning.

[0712] "Example 3"

[0713] Furthermore, as a means of enabling the application of security to the latest trends in system development, the system periodically checks the latest security trends and reflects them in system development. Specifically, the system checks the latest security trends every Friday and reflects them in the next week's system development.

[0714] The processing flow of each embodiment will be described below.

[0715] "Example 1"

[0716] Step 1: The system uses algorithms based on the latest security technology to generate optimal solutions to security problems.

[0717] Step 2: Specifically, we use machine learning and artificial intelligence to generate optimal answers to security problems.

[0718] Step 3: The generated answers are used to improve the accuracy of the system's security answers.

[0719] "Example 2"

[0720] Step 1: The system will periodically check your security and notify you immediately if any violations are found, prompting you to correct them.

[0721] Step 2: Specifically, the system performs a security check every night, and if a violation is found, it notifies the developer the following morning.

[0722] Step 3: This notification allows developers to immediately fix security violations, improving system security compliance.

[0723] "Example 3"

[0724] Step 1: The system will regularly check the latest security trends and reflect them in system development.

[0725] Step 2: Specifically, the system checks for the latest security trends every Friday and reflects them in the following week's system development.

[0726] Step 3: This method ensures that system development always corresponds to the latest security trends, improving system security.

[0727] Example 1

[0728] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0729] Conventional security systems have had issues with low accuracy of security-related responses, making it difficult to improve security compliance rates. Furthermore, it was difficult to apply the latest security trends to system development, resulting in insufficient preparation for security design before development. Furthermore, there were issues with the inability to effectively provide server development input or set milestones based on internal and external data.

[0730] The identification process by the identification processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means. In this invention, the server includes means for improving the accuracy of security-related answers, means for improving the security compliance rate, means for enabling security to be applied to the latest trends in system development, means for providing guidance on preparing for security design before development, means for providing server development input, means for enabling milestone setting based on internal and external data, means for inputting prompt statements, means for collecting security data, means for preprocessing the collected data, means for training a machine learning model, means for evaluating the trained model, means for generating an optimal answer, and means for providing the generated answer. This makes it possible to quickly and accurately provide optimal answers to security-related questions.

[0731] "Security Answer Accuracy" refers to the accuracy and reliability of answers provided to security questions or problems.

[0732] "Security compliance rate" is an indicator that shows the percentage of compliance with security policies and regulations.

[0733] "Applying the latest trends in security to system development" refers to incorporating the latest security technologies and trends into the system development process.

[0734] "Guidance on preparing for security design before development" refers to providing guidance and advice on security design in the early stages of system development.

[0735] "Server development input" refers to providing information and data necessary for server development.

[0736] "Setting milestones based on internal and external data" refers to using internal and external data to set important milestones that indicate the progress of a project.

[0737] A "prompt sentence" refers to a question or instruction sentence that a user inputs to a system.

[0738] "Security Data" means security-related information and data, including network traffic, log files, and system events.

[0739] "Data preprocessing" refers to the process of converting collected data into a format suitable for analysis and learning. Specifically, it includes data cleaning, normalization, feature extraction, etc.

[0740] A "machine learning model" refers to an algorithm or mathematical model that learns patterns and rules from data and makes predictions and classifications.

[0741] "Model evaluation" refers to the process of measuring the performance of a trained machine learning model and confirming its effectiveness, using metrics such as precision, recall, and F1 score.

[0742] "Generating optimal answers" refers to creating the most appropriate answer to a user's question based on collected data and a trained model.

[0743] "Providing an answer" refers to displaying or communicating the generated answer to the user.

[0744] This invention is a system for improving the accuracy of security-related answers, using algorithms based on the latest security technologies. Specifically, it utilizes machine learning and artificial intelligence to generate optimal answers to security-related questions.

[0745] First, the user inputs a prompt sentence using a terminal, for example, "What is the best way to detect unauthorized access in a company network?"

[0746] The server collects relevant security data based on prompts entered by the user, and this data is obtained from network traffic, log files, system events, etc. The hardware used is a network sensor or a log server.

[0747] The server then preprocesses the collected data, specifically cleaning the data (filling in missing values ​​and removing outliers), normalizing the data (scaling the data), and extracting features (extracting important data points). The software used is Python's Pandas and NumPy libraries.

[0748] The server uses the preprocessed data to train a machine learning model. The algorithms used are deep learning (e.g., CNN, RNN) and support vector machines (SVM). The software used is TensorFlow and Scikit-learn.

[0749] To evaluate the trained model, the server uses evaluation metrics (precision, recall, F1 score, etc.) and the software used is the evaluation function of Scikit-learn. For example, it checks whether the accuracy of the model is 90% or higher.

[0750] Based on the evaluation results, the server generates an optimal solution to the security problem. The software used is a generative AI model (e.g., GPT-3). For example, it generates an answer such as, "It is best to use an anomaly detection algorithm to detect anomalies in network traffic."

[0751] Finally, the server provides the generated answer to the user's terminal, and the user can implement security measures based on the provided answer.

[0752] This system allows users to quickly and accurately find the best possible answers to security questions.

[0753] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0754] Step 1: Enter the prompt statement

[0755] The user inputs a prompt into the input field of the terminal. For example, the user might input, "What is the best way to detect unauthorized access in a company network?" This prompt becomes the input to the system.

[0756] Step 2: Data collection

[0757] The server collects relevant security data based on the prompts entered by the user, such as network traffic data, log files, and system events. The hardware used is a network sensor and a log server. The collected data is used as input for the next step.

[0758] Step 3: Data Preprocessing

[0759] The server preprocesses the collected data. Specifically, it performs data cleaning (filling in missing values ​​and removing outliers), normalization (scaling the data), and feature extraction (extracting important data points). The software used is Python's Pandas and NumPy libraries. The preprocessed data becomes the input for the next step.

[0760] Step 4: Model training

[0761] The server uses the preprocessed data to train a machine learning model. The algorithms used are deep learning (e.g., CNN, RNN) and support vector machines (SVM). The software used is TensorFlow or Scikit-learn. The trained model becomes the input for the next step.

[0762] Step 5: Model evaluation

[0763] The server evaluates the trained model. Evaluation uses metrics such as precision, recall, and F1 score. The software used is Scikit-learn's evaluation function. For example, it checks whether the model's accuracy is 90% or higher. The evaluation results are input to the next step.

[0764] Step 6: Generate the optimal solution

[0765] The server generates an optimal answer based on the evaluation results. This answer is for a security-related problem. The software used is a generative AI model (e.g., GPT-3). For example, it generates an answer such as, "It is good to use an anomaly detection algorithm to detect anomalies in network traffic." The generated answer becomes the input for the next step.

[0766] Step 7: Provide the answer

[0767] The server provides the generated answer to the user's terminal. The user can implement security measures based on the provided answer. The answer is output to the user.

[0768] (Application example 1)

[0769] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0770] In today's information society, security issues are becoming increasingly complex, requiring rapid and accurate responses. However, conventional systems provide low accuracy in security responses and have not achieved sufficient improvements in security compliance rates. Furthermore, systems often lack the ability to adapt to the latest trends in system development and to adequately prepare for security design prior to development. Furthermore, there is the issue of difficulty in quickly providing optimal answers when users ask security questions.

[0771] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0772] In this invention, the server includes means for enabling the accuracy of security-related answers, security compliance rates, and application of security to the latest trends in system development, means for providing guidance on preparing for security design before development, means for providing server development input, means for enabling the setting of milestones based on internal and external data, means for generating optimal answers to security-related questions entered by users using a generative AI model, means for evaluating security risks in real time and issuing warnings, and means for learning from past security incidents and predicting future risks.This enables quick and accurate responses to security issues, improves security compliance rates, and effectively applies security to system development.

[0773] "Security answer accuracy" refers to the ability to provide accurate and relevant answers to security questions or problems.

[0774] "Security compliance rate" refers to the percentage of actions taken in accordance with security policies and guidelines.

[0775] "Applying security to the latest trends in system development" refers to the appropriate implementation of security measures in system development that incorporates the latest technologies and trends.

[0776] "Guidance on Pre-Development Security Design Preparation" refers to providing guidance and advice on planning and designing security measures in the early stages of system development.

[0777] "Server development input" refers to providing information and data necessary for server design and development.

[0778] "Setting milestones based on internal and external data" refers to using internal and external data to set important milestones that indicate the progress of a project.

[0779] A "generative AI model" refers to an algorithm or system that uses artificial intelligence to learn from data and generate new information or answers.

[0780] "Means of assessing security risks and issuing warnings in real time" refers to the ability to instantly analyze ongoing situations, detect potential security risks, and issue warnings.

[0781] "Means of learning from past security incidents and predicting future risks" refers to the function of analyzing security issues that have occurred in the past and predicting future risks based on that knowledge.

[0782] As an embodiment of the present invention, the following system can be constructed.

[0783] First, the server includes the following means:

[0784] 1. A means to enable the accuracy of security responses, security compliance rates, and application of security to the latest trends in system development.

[0785] 2. A means of providing guidance for security design preparation before development.

[0786] 3. A means of providing server development input.

[0787] 4. A means to enable milestone setting based on internal and external data.

[0788] 5. A means of generating optimal answers to security questions entered by users using generative AI models.

[0789] 6. A means of assessing and alerting security risks in real time.

[0790] 7. A means of learning from past security incidents and predicting future risks.

[0791] Hardware and software used

[0792] Hardware: Smartphones, servers

[0793] Software: Python, OpenAI API

[0794] Data processing and calculation

[0795] The server receives security questions entered by the user through a smartphone application, then sends the questions to a generative AI model to generate the optimal answer, which is then displayed to the user through the smartphone application.

[0796] The server also assesses security risks in real time, issuing warnings as needed, and has the ability to learn from past security incidents and predict future risks.

[0797] Specific examples

[0798] For example, if a user asks, "How do I spot a phishing email?", the server sends the following prompt to the generative AI model:

[0799] Security Question: How do I spot a phishing email?

[0800] Generate the best answer.

[0801] The generative AI model generates an answer based on this prompt, and the server provides the answer to the user. Examples of generated answers include:

[0802] Here's how to spot a phishing email:

[0803] 1. Check the sender's email address.

[0804] 2. Don't click on links in emails.

[0805] 3. Be wary of emails requesting personal information.

[0806] 4. Check your email for grammar and spelling mistakes.

[0807] 5. Report any suspicious emails to your IT department.

[0808] In this way, users can get fast and accurate answers to their security questions, assess security risks in real time, and predict future risks by learning from past incidents.

[0809] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0810] Step 1:

[0811] A user launches an application on their smartphone and enters a security question. The entered question is sent to the server by the application. The input data is the user's question, and the output data is the question sent to the server.

[0812] Step 2:

[0813] The server analyzes the received question and generates a prompt to send to the generative AI model. The input data is the user's question, and the output data is the generated prompt. Specifically, the server analyzes the content of the question and converts it into an appropriate format.

[0814] Step 3:

[0815] The server sends the generated prompt to the generative AI model and generates the optimal answer. The input data is the prompt, and the output data is the answer returned by the generative AI model. Specifically, the server sends the prompt to the generative AI model and receives a response from the model.

[0816] Step 4:

[0817] The server analyzes the answer received from the generative AI model and converts it into a format for display to the user. The input data is the answer from the generative AI model, and the output data is the answer to be displayed to the user. Specifically, the server analyzes the content of the answer and formats it into a format that is easy for the user to understand.

[0818] Step 5:

[0819] The server sends the formatted answer to the smartphone application and displays it to the user. The input data is the formatted answer, and the output data is the answer displayed on the user's smartphone. Specifically, the answer is sent to the application, and the application displays it to the user.

[0820] Step 6:

[0821] The server evaluates security risks in real time and issues warnings if necessary. The input data is information about the current security situation, and the output data is a warning message. Specifically, it runs an algorithm to evaluate security risks and generates a warning if a risk is detected.

[0822] Step 7:

[0823] The server learns from past security incidents and predicts future risks. The input data is data about past security incidents, and the output data is a prediction of future risks. Specifically, the server analyzes past data and updates the model for predicting future risks.

[0824] Example 2

[0825] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0826] Modern system development requires improved security compliance and rapid response. However, the frequency of security checks and response after violation detection are often delayed, resulting in a risk of system vulnerabilities being left unaddressed. Furthermore, there is often a lack of means to apply the latest security trends and preparation for security design before development. To address these issues, regular security checks, rapid notification, and efficient responses using generative AI models are required.

[0827] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for enabling security response accuracy, security compliance rate, and application of security to the latest trends in system development, a means for providing guidance on preparing for security design before development, a means for providing server development input, a means for enabling milestone setting based on internal and external data, a means for periodically performing security checks, a means for generating a notification urging correction when a violation is found, a means for generating notification content using a generative AI model, and a means for sending a notification to a developer. This enables an improvement in security compliance rate and a quick response.

[0828] "Security answer accuracy" refers to the ability to provide accurate and prompt answers to security questions and problems.

[0829] "Security compliance rate" is an indicator of how well a system complies with security policies and regulations.

[0830] "Applying the latest trends in security to system development" refers to incorporating the latest security technologies and best practices into the system development process.

[0831] "Guidance on preparing for security design before development" refers to providing guidance and advice on how to design with security in mind in the early stages of system development.

[0832] "Server development input" refers to providing information and data necessary for server development.

[0833] "Setting milestones based on internal and external data" refers to setting important milestones based on internal and external data to manage the progress of a project.

[0834] "Means for performing regular security checks" refers to methods and tools for checking the security status of a system at regular intervals.

[0835] "Means for generating notifications to encourage remediation when violations are found" refers to methods or tools for creating notifications to encourage remediation when a security violation is detected.

[0836] "Means for generating notification content using generative AI models" refers to methods or tools that use artificial intelligence models to create notification content that includes details of a security breach and how to remediate it.

[0837] "Means for sending notifications to a developer" means the methods and tools used to deliver generated notifications to a developer.

[0838] The present invention is a system for improving security compliance, in which a server, a terminal, and a user work together. A specific embodiment of this system will be described below.

[0839] The server uses a scheduling tool (e.g., cron job or Windows Task Scheduler) to run a security check every night. The server also launches a security scanning tool (e.g., Nessus or OpenVAS) to perform a system-wide security check, which performs a detailed scan of the network and system configuration to detect vulnerabilities and security violations.

[0840] Once the scan is complete, the server analyzes the scan results. This involves reading the report file generated by the scanning tool and extracting a list of vulnerabilities and security violations. Specifically, the server reads the Nessus report file (e.g., in XML format) and uses XPath queries to extract vulnerability information.

[0841] The server then uses a generative AI model (e.g., GPT-4) to generate a notification based on the scan results, specifically a prompt that details the vulnerabilities or violations detected and explains how to remediate them. Here's an example prompt:

[0842] Example prompt sentence:

[0843] Outdated software has been found. Please update the following software to the latest version:

[0844] Software Name: ExampleSoftware

[0845] Current version: 1.0.0

[0846] Latest version: 1.2.0

[0847] Please see the link below for update instructions:

[0848] [Update procedure link]

[0849] The server sends the generated notification content to the developer. The notification is sent via email or a chat tool (for example, Slack or Microsoft Teams). Specifically, the server uses an SMTP server to send an email containing the generated notification content to the developer's email address, or uses the Slack API to post the notification content to the developer's Slack channel.

[0850] Users (developers) receive notifications and follow instructions to update their software, which improves system security compliance and quickly fixes vulnerabilities.

[0851] The system can improve security compliance through regular security checks and prompt notifications, and by utilizing generative AI models, it can streamline notification content generation, reducing the burden on developers.

[0852] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0853] Step 1:

[0854] The server uses a scheduling tool to set up a job to run a security check every night.

[0855] Input: Scheduling tool setting information

[0856] Data Processing: Job Scheduling

[0857] Output: A job that runs a security check every night at midnight

[0858] Specific operation: The server sets up a cron job and registers a command to run a security scanning tool at 2:00 AM every day.

[0859] Step 2:

[0860] The server launches a security scanning tool according to a set schedule and performs a security check of the entire system.

[0861] Inputs: Scheduled jobs, security scanning tools

[0862] Data Processing: Full System Scan

[0863] Output: Scan result report file

[0864] What happens: The server launches Nessus and begins a network-wide scan, which examines the configuration of each device and server in detail to detect known vulnerabilities and misconfigurations.

[0865] Step 3:

[0866] Once the scan is complete, the server analyzes the scan results.

[0867] Input: Scan result report file

[0868] Data processing: Analyzing report files and extracting vulnerability information

[0869] Output: A list of vulnerabilities and security violations

[0870] Specific operation: The server reads the Nessus report file (e.g., XML format) and extracts vulnerability information using XPath queries.

[0871] Step 4:

[0872] The server uses a generative AI model to generate notification content based on the scan results.

[0873] Input: List of vulnerabilities and security breaches

[0874] Data processing: Notification content generation

[0875] Output: Generated notification content (prompt text)

[0876] Specific behavior: The server inputs the following prompt into GPT-4 to generate the notification content:

[0877] The following vulnerabilities were discovered:

[0878] Vulnerability Name: Outdated Software

[0879] Affected: ExampleSoftware 1.0.0

[0880] To fix this vulnerability, please update ExampleSoftware to the latest version 1.2.0. Please see the following link for update instructions:

[0881] [Update procedure link]

[0882] Step 5:

[0883] The server sends the generated notification content to the developer.

[0884] Input: Generated notification content (prompt text)

[0885] Data Processing: Sending Notifications

[0886] Output: Notification sent to developer

[0887] Specific behavior: The server uses an SMTP server to send an email containing the generated notification content to the developer's email address, or uses the Slack API to post the notification content to the developer's Slack channel.

[0888] Step 6:

[0889] The user (developer) receives a notification and follows the instructions to update the software.

[0890] Input: Notification content (prompt text)

[0891] Data Processing: Software Update

[0892] Output: Updated software

[0893] Action: The user acknowledges the notification and follows the instructions to update the software to the latest version, thereby improving system security compliance.

[0894] (Application example 2)

[0895] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0896] Increasing security compliance is extremely important in modern system development. However, when security checks are performed manually, they tend to be performed infrequently, and when violations are found, responses are often delayed. Furthermore, detailed explanations of violations and the provision of remediation methods are insufficient, making it difficult for developers to respond quickly. Furthermore, the lack of a way to manage past security check results and remediation history makes continuous security improvement difficult.

[0897] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for enabling the accuracy of security-related answers, security compliance rate, and application of security to the latest trends in system development, means for providing guidance on preparing for security design before development, means for providing server development input, means for enabling the setting of milestones based on internal and external data, means for conducting regular security checks and, if a violation is found, for issuing a notification urging immediate correction, means for providing a guide detailing the violation and how to correct it, and means for saving and making available past security check results and correction history. This enables an improvement in security compliance rate and rapid response to violations.

[0898] "Security answer accuracy" refers to the ability to provide accurate and prompt answers to security questions and problems.

[0899] "Security compliance rate" refers to the percentage of a system or organization that complies with established security policies and standards.

[0900] "Applying security to the latest trends in system development" refers to introducing appropriate security measures for the latest system development technologies and trends.

[0901] "Guidance on preparing for security design before development" refers to providing guidance and advice on how to design with security in mind in the early stages of system development.

[0902] "Server development input" refers to providing information and data necessary for server development.

[0903] "Setting milestones based on internal and external data" refers to using internal and external data to set important milestones to manage the progress of a project.

[0904] "Performing regular security checks" refers to the process of checking the security status of a system at regular intervals.

[0905] "If a violation is found, provide immediate notification to prompt correction" refers to sending a notification to promptly correct a security violation when it is detected.

[0906] A "guide detailing the breach and how to fix it" refers to a document or tool that details the specific nature of the security breach and how to fix it.

[0907] "Saving and making available the results of past security checks and correction history" means recording the results of previous security checks and the history of corrections, and making them available for reference as needed.

[0908] As an embodiment of the present invention, a security check alert system is constructed, the details of which are described below.

[0909] System configuration

[0910] This system consists of a server, a terminal (such as a smartphone), and a user (developer). The server periodically performs security checks and sends notifications if a violation is found. The terminal is a device that the user uses to receive notifications and check the details of the violation and how to fix it.

[0911] Hardware and software used

[0912] Hardware: Servers (cloud infrastructure or on-premise servers), smartphones

[0913] Software: Python, smtplib (email sending library), schedule (schedule library)

[0914] Data processing and calculation

[0915] The server processes the data as follows:

[0916] 1. Security check execution: The server automatically executes a security check every night. If a security violation is found as a result of the check, the details of the violation are recorded.

[0917] 2. Sending violation notification: If a violation is found, the server will send a push notification to the developer's device the next morning, containing details of the violation and instructions on how to fix it.

[0918] 3. History storage: The server stores past security check results and correction history in a database and makes them available for reference as needed.

[0919] Specific examples

[0920] For example, if a company's IT infrastructure performs a security check every night and an SQL injection vulnerability is found, the server will send a notification to the developer's smartphone the next morning saying, "An SQL injection vulnerability has been found. Please refer to the link below for how to fix it." The developer will receive the notification and be able to respond quickly by following the detailed fix guide.

[0921] Prompt Sentence Examples

[0922] "Implement a system that performs security checks every night and notifies developers the next morning if a violation is found. Specifically, create a program that uses Python to perform security checks and sends a notification by email if a violation is found."

[0923] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0924] Step 1:

[0925] The server runs a security check every night at midnight. As input, it takes data about the security state of the system and applies the security check algorithm. As output, it generates a list of security violations. Specifically, the server uses a scheduling library to start the security check at the specified time.

[0926] Step 2:

[0927] The server analyzes the results of the security checks to determine if any violations were found. As input, it uses the list of security violations generated in step 1. As output, it generates a notification message containing the details of any violations found. Specifically, the server loops through the list of violations to determine if any exist.

[0928] Step 3:

[0929] If a violation is found, the server will send a push notification to the developer's device the next morning. As input, it uses the notification message generated in step 2. As output, the notification will be displayed on the developer's device. Specifically, the server will use an email sending library to send the notification to the specified email address.

[0930] Step 4:

[0931] The user (developer) receives a notification on their device and checks the details of the violation and how to fix it. The notification message sent from the server is used as input. As output, the user accesses the fix guide and checks the specific fix steps. As a specific operation, the user taps the notification on their device to display the detailed fix guide.

[0932] Step 5:

[0933] The server stores past security check results and correction history in a database and makes them available for reference as needed. It uses security check results and correction history as input and generates historical data stored in the database as output. Specifically, the server connects to the database and executes queries to store the results and history.

[0934] Example 3

[0935] Next, a description will be given of Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0936] In conventional system development, it is difficult to quickly and accurately reflect the latest security trends, resulting in problems such as a decline in security compliance rates and increased security risks. Furthermore, the creation and notification of security measures is often done manually, which tends to be inefficient and result in long lead times. Furthermore, there is insufficient preparation for security design before development and insufficient setting of milestones based on internal and external data, making it difficult to improve the security level of the entire system.

[0937] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0938] In this invention, the server includes means for periodically collecting the latest security trends, means for saving the collected security information in a database, means for analyzing the saved security information, means for generating security measures based on the analysis results, means for notifying the generated security measures, means for enabling the accuracy of security-related answers, security compliance rate, and application of security to the latest trends in system development, means for providing guidance for preparing security design before development, means for providing server development input, and means for enabling the setting of milestones based on internal and external data. This allows the latest security trends to be reflected in system development quickly and accurately, improving security compliance rate and reducing security risks.

[0939] "Latest security trends" refers to the latest information on ongoing security threats and vulnerabilities.

[0940] "Methods of collection" refers to the technologies and methods used to obtain data from reliable sources on the Internet.

[0941] "Means of database storage" refers to the systems and technologies used to store collected information in a structured format.

[0942] "Means for analysis" refers to techniques and methods for analyzing stored data and extracting useful information.

[0943] "Means for generating security measures" refers to the techniques and methods for creating specific security measures based on the analysis results.

[0944] "Means of notification" refers to the techniques and methods for communicating the security measures that have been created to relevant parties.

[0945] "Security answer accuracy" refers to the ability to accurately answer security questions or problems.

[0946] "Security compliance rate" refers to the percentage of actions taken in accordance with security policies and guidelines.

[0947] "Applying security based on the latest trends to system development" refers to reflecting the latest security information in the system development process.

[0948] "Guidance on preparing for security design before development" refers to guidance and advice on planning and preparing necessary security measures in the early stages of system development.

[0949] "Server development input" refers to providing information and data necessary for server development.

[0950] "Setting milestones based on internal and external data" refers to setting important milestones to manage project progress based on internal and external data.

[0951] The present invention provides a system for quickly and accurately reflecting the latest security trends in system development. A specific embodiment of this system will be described below.

[0952] Every Friday, the server collects the latest security trends from trusted security sources on the internet using the Python libraries BeautifulSoup and Requests, for example by scraping information from CVE databases and security blogs.

[0953] The collected security information is stored in a database on the server using MySQL or PostgreSQL. The stored information includes the title, content, and publication date of the security information.

[0954] Next, the server analyzes the stored security information. For this analysis, it uses a generative AI model (e.g., OpenAI's GPT-4). Specifically, the security information is input to the generative AI model as a prompt sentence, and the analysis result is obtained.

[0955] Based on the analysis results, the server generates specific security measures, such as "performing escaping of input data" and "using prepared statements" to protect against SQL injection vulnerabilities.

[0956] The server notifies the development team of the generated security measures. Notifications are sent via email or chat tools (e.g., Slack, Microsoft Teams). The notification includes details of the measures and how to implement them.

[0957] The terminal (developer's PC) implements the notified security measures. The developer then modifies the code based on the notified measures to strengthen the security of the system. For example, to counter SQL injection, the developer can change the SQL query part in the code to a prepared statement.

[0958] As a concrete example, the following prompt sentence is input to the generative AI model:

[0959] "According to the information gathered from the latest CVE database, an SQL injection vulnerability has been reported. Please tell me the specific measures to prevent this vulnerability."

[0960] In this way, the server, terminal, and user cooperate to reflect the latest security trends in system development, thereby enhancing security. The flow of the identification process in the third embodiment will be described with reference to FIG.

[0961] Step 1:

[0962] The server collects the latest security trends from reliable security sources on the Internet every Friday. It uses a list of URLs from security sources as input. Specifically, it uses the Python libraries BeautifulSoup and Requests to scrape information from CVE databases and security blogs. The collected security information is then returned as output.

[0963] Step 2:

[0964] The server stores the collected security information in a database. It uses the security information collected in step 1 as input. Specifically, it uses MySQL or PostgreSQL to store the title, content, publication date, etc. of the security information as fields. The output is the security information stored in the database.

[0965] Step 3:

[0966] The server analyzes the stored security information. As input, it uses the security information stored in the database. Specifically, it uses a generative AI model (e.g., OpenAI's GPT-4) and inputs the security information into the generative AI model as a prompt sentence. As output, it obtains the analysis results obtained from the generative AI model.

[0967] Step 4:

[0968] The server generates security measures based on the analysis results. The analysis results obtained in step 3 are used as input. Specific operations include creating specific security measures based on the information obtained from the generative AI model. The generated security measures are obtained as output.

[0969] Step 5:

[0970] The server notifies the development team of the generated security measures. The security measures generated in step 4 are used as input. Specific operations include notifying the development team of the measures via email or chat tools (e.g., Slack, Microsoft Teams). The output is the security measures notified to the development team.

[0971] Step 6:

[0972] The terminal (developer's PC) implements the notified security measures. It uses the security measures notified in step 5 as input. Specific actions include modifying the code based on the notified measures to strengthen the security of the system. The output is a system with the security measures implemented.

[0973] (Application example 3)

[0974] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0975] In modern system development, it is extremely important to quickly and accurately reflect the latest security trends. However, manually checking security trends and incorporating them into systems is time-consuming and labor-intensive, and inefficient. Furthermore, there are a wide range of security-related challenges, such as improving security compliance rates, preparing security designs before development, providing server development input, and setting milestones based on internal and external data. A comprehensive system to solve these challenges is needed.

[0976] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means. In this invention, the server includes means for enabling security response accuracy, security compliance rate, and application of security to the latest trends in system development, means for providing guidance on preparation for security design before development, means for providing server development input, means for enabling milestone setting based on internal and external data, means for periodically checking the latest security trends and reflecting them in system development, and means for acquiring security trends through an application installed on a smart device and updating system settings. This makes it possible to quickly and efficiently reflect the latest security trends in system development.

[0977] "Security response accuracy" refers to the ability to provide accurate and prompt responses to security questions and problems.

[0978] "Security compliance rate" is an indicator that shows the extent to which a system or organization complies with established security policies and standards.

[0979] "Applying security to the latest trends in system development" refers to incorporating the latest security trends and technologies into the system development process.

[0980] "Guidance on preparing for security design before development" refers to providing guidance and advice on how to properly design security in the early stages of system development.

[0981] "Server development input" refers to providing information and data necessary for server development.

[0982] "Setting milestones based on internal and external data" refers to setting important milestones to manage the progress of a project based on internal and external data.

[0983] "Means of regularly checking the latest security trends and reflecting them in system development" refers to a method of regularly collecting the latest security information and applying it to the system development process.

[0984] "Means of obtaining security trends through applications installed on smart devices and updating system settings" refers to a method of obtaining security information using applications installed on devices such as smartphones and tablets, and updating system settings based on that information.

[0985] As an embodiment of the present invention, a security management system is constructed, which is composed of a server, smart devices, and software that links them together.

[0986] The server is equipped with a means to ensure the accuracy of security-related responses, security compliance rates, and the application of security to the latest trends in system development. Specifically, the server periodically checks the latest security trends and collects data to reflect them in system development. The server obtains the latest security information using a security trend API, analyzes the information, and updates the system settings.

[0987] An application for acquiring security trends and updating system settings is installed on smart devices. This application acquires the latest security information from a server every Friday and notifies the user. The user checks the notification and updates the system settings as necessary.

[0988] In terms of specific hardware, the server is a computer equipped with a high-performance processor and large-capacity storage, and the smart device is a mobile information terminal such as a smartphone or tablet. In terms of software, Python and the requests library are used on the server side, and a dedicated security management application is used on the smart device side.

[0989] For data processing and calculation, the server analyzes the data obtained from the security trend API in JSON format and extracts the necessary information. The extracted information is used to update system settings. On the smart device side, the obtained information is notified to the user and the system settings are updated according to the user's operations.

[0990] As a concrete example, by inputting the following prompt sentence into a generative AI model, a program for a security control system can be generated.

[0991] Example prompt sentence:

[0992] "Write a Python program that checks the latest security trends and provides recommendations for the next week's system development. Include a process to retrieve data from an API every Friday and update the system configuration."

[0993] Using this prompt, the generative AI model can generate the necessary programs to assist in building a security control system.

[0994] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0995] Step 1:

[0996] The server sends a request to the security trend API every Friday. The API endpoint URL and authentication information are required as input. The server retrieves the latest security trend data from the API and receives it in JSON format. The retrieved security trend data is returned as output.

[0997] Step 2:

[0998] The server analyzes the acquired JSON-formatted security trend data. The JSON data acquired in step 1 is required as input. The server extracts the necessary information (e.g., new vulnerability information, recommended security patches, etc.) and stores it in an internal database. The analyzed security information is obtained as output.

[0999] Step 3:

[1000] The server determines whether the system settings need to be updated based on the analyzed security information. The analyzed security information obtained in step 2 is required as input. If the system settings need to be updated, the server generates the necessary content and creates an update script. The output is a system settings update script.

[1001] Step 4:

[1002] An application installed on a smart device retrieves the latest security information from a server every Friday. The server's endpoint URL and authentication information are required as input. The smart device notifies the user of the security information retrieved from the server. The output is the security information notified to the user.

[1003] Step 5:

[1004] The user checks the notification from the smart device and approves the update of the system settings as necessary. The input requires the notification content from the smart device. The user performs the operation of approving or rejecting the notification. The output is the result of the user's approval or rejection.

[1005] Step 6:

[1006] After receiving user approval, the smart device instructs the server to update the system settings. The user's approval result is required as input. The smart device requests the server to execute an update script. The output is an update request to the server.

[1007] Step 7:

[1008] The server receives an update request from the smart device and executes the system settings update script. The input requires an update request from the smart device. The server updates the system settings and sends the update results back to the smart device. The output is the updated system settings and the results.

[1009] Step 8:

[1010] The smart device receives the update results from the server and notifies the user. The input requires the update results from the server. The smart device displays the update results to the user and prompts the user to perform additional operations as necessary. The output is the update results notified to the user.

[1011] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1012] "Example 1"

[1013] As one embodiment of the present invention, a system incorporating an emotion engine is provided. This system recognizes a user's emotions and adjusts the accuracy of security-related answers based on those emotions. Specifically, if the system recognizes that the user is feeling anxious or scared, it provides more detailed security information to enhance the user's sense of security. Also, if the system recognizes that the user is feeling safe, it provides only the minimum necessary security information, allowing the user to perform operations smoothly.

[1014] "Example 2"

[1015] In another embodiment of the present invention, a system is provided in which an emotion engine improves security compliance based on a user's emotions. Specifically, if the system recognizes that the user is feeling anger or frustration, it identifies the security violation that caused it and suggests specific actions to improve it. In this way, by providing security measures that take the user's emotions into consideration, the system improves the user's security compliance.

[1016] "Example 3"

[1017] Furthermore, as another embodiment of the present invention, a system is provided in which an emotion engine adjusts the system's security measures based on the user's emotions. Specifically, if the system recognizes that the user is feeling happy or excited, it uses that emotion to suggest new security measures. For example, if the system recognizes that the user is excited about a new function, it suggests new security measures related to that function. In this way, by suggesting security measures that utilize the user's emotions, it is possible to improve the user's security awareness.

[1018] The processing flow of each embodiment will be described below.

[1019] "Example 1"

[1020] Step 1: The system recognizes the user's emotions using the emotion engine.

[1021] Step 2: If the emotion engine recognizes the user's emotion as anxiety or fear, the system will provide more detailed security information.

[1022] Step 3: If the emotion engine recognizes the user's emotion as safe, the system provides only the minimum necessary security information.

[1023] "Example 2"

[1024] Step 1: The system recognizes the user's emotions using the emotion engine.

[1025] Step 2: If the emotion engine identifies the user's emotion as anger or frustration, the system identifies the underlying security breach.

[1026] Step 3: The system suggests specific actions to remediate the identified security violations.

[1027] "Example 3"

[1028] Step 1: The system recognizes the user's emotions using the emotion engine.

[1029] Step 2: If the emotion engine recognizes the user's emotion as joy or excitement, the system will use that emotion to suggest new security measures.

[1030] Step 3: The system presents the proposed new security measures to the user.

[1031] Example 1

[1032] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1033] Conventional security systems provide uniform security information without considering users' feelings, which has the problem of not being able to fully alleviate users' anxiety and fear. Furthermore, the accuracy of security-related responses is low, making it difficult to improve security compliance rates. Furthermore, it is difficult to apply the latest security trends in system development, and pre-development security design preparation is often insufficient.

[1034] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1035] In this invention, the server includes means for improving the accuracy of security-related answers, means for improving the security compliance rate, means for enabling the application of security to the latest trends in system development, means for providing guidance for preparing security design before development, means for providing server development input, means for enabling the setting of milestones based on internal and external data, means for recognizing user emotions and adjusting the accuracy of security-related answers based on the emotions, means for generating optimal security answers using a machine learning model, means for adjusting the generated answers based on the emotion analysis results, and means for providing the adjusted security answers to the user. This makes it possible to provide optimal security information according to the user's emotions, reduce user anxiety and fear, and improve the security compliance rate.

[1036] "Security Answer Accuracy" refers to the accuracy and appropriateness of the answers provided to security questions.

[1037] "Security compliance rate" refers to the percentage of systems and users that comply with security policies and guidelines.

[1038] "Applying the latest trends in security to system development" refers to incorporating the latest security technologies and trends into the system development process.

[1039] "Guidance on preparing for security design before development" refers to providing guidance and advice on how to properly design security in the early stages of system development.

[1040] "Server development input" refers to providing information and data necessary for server development.

[1041] "Setting milestones based on internal and external data" refers to using internal and external data to set important milestones for a project.

[1042] "Recognizing user emotions" refers to analyzing and understanding the user's emotional state from their input and behavior.

[1043] "Adjusting the accuracy of security-related answers based on emotions" refers to changing the detail and content of the security information provided depending on the user's emotional state.

[1044] "Generating optimal security answers using machine learning models" refers to using machine learning algorithms to generate optimal security answers to user questions.

[1045] "Adjusting the generated answer based on the result of sentiment analysis" refers to appropriately changing the content of the generated security answer to reflect the result of sentiment analysis.

[1046] "Providing a tailored security answer to a user" refers to conveying tailored security information to a user according to the user's emotional state.

[1047] The present invention provides a system for improving the accuracy of security-related answers, which is characterized by recognizing a user's emotions and adjusting the accuracy of security-related answers based on those emotions. Specific embodiments of this system are described below.

[1048] System configuration

[1049] This system mainly consists of three elements: a server, a terminal, and a user. The server generates security-related answers and analyzes the user's emotions. The terminal receives input from the user and communicates with the server. The user enters security-related questions into the terminal.

[1050] Hardware and software used

[1051] Server: A server with high-performance computing power is required. Specifically, a cloud-based server (e.g., Amazon Web Services or Google Cloud Platform) can be used.

[1052] Emotion Engine: Uses Google Cloud Natural Language API and Microsoft Azure Emotion API to analyze user emotions.

[1053] Machine learning models: Use TensorFlow and PyTorch to generate security answers.

[1054] Data processing and calculation

[1055] 1. Data Collection:

[1056] The user enters a security question into the terminal, for example, "Tell me about recent phishing scams."

[1057] The terminal transmits this input data to the server.

[1058] 2. Emotion recognition:

[1059] The server passes the received user input data to an emotion engine and analyzes the emotion. For example, it uses the Google Cloud Natural Language API to analyze whether the user is feeling "anxious."

[1060] 3. Security Answer Generation:

[1061] The server uses machine learning models (TensorFlow and PyTorch) to generate optimal security answers to user questions, including detailed information on the latest phishing scams and how to prevent them.

[1062] 4. Adjusting your answers:

[1063] The server adjusts the generated answer based on the results of the sentiment analysis. If the user feels anxious, the answer is adjusted to provide detailed security information. Conversely, if the user feels reassured, the answer is adjusted to provide only the minimum necessary information.

[1064] 5. Providing answers:

[1065] The server sends the adjusted security answer to the terminal, and the terminal displays the received security answer to the user, who then confirms the answer through the terminal.

[1066] Specific examples

[1067] If a user asks, "Tell me about recent phishing scams," the system proceeds as follows:

[1068] 1. The user types "Tell me about recent phishing scams" into the terminal.

[1069] 2. The terminal sends this input data to the server.

[1070] 3. The server uses the Google Cloud Natural Language API to analyze the user's emotions and recognizes that they are feeling "anxiety."

[1071] 4. The server uses TensorFlow to generate detailed security answers including the latest phishing scam techniques and countermeasures.

[1072] 5. The server tailors its response to provide more security information because the user is concerned.

[1073] 6. The server sends the adjusted security answer to the terminal.

[1074] 7. The terminal displays the received security answer to the user, who then checks the detailed security information through the terminal.

[1075] Prompt Sentence Examples

[1076] "Users are asking about recent phishing scams. Analyze their sentiment and provide them with relevant security information."

[1077] In this way, the server realizes a system that provides optimal security answers according to the user's emotions.

[1078] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1079] Step 1:

[1080] Collecting user input data

[1081] The user enters a security question into the terminal, for example, "Tell me about recent phishing scams."

[1082] The terminal transmits this input data to the server.

[1083] Input: User question text

[1084] Output: User question data sent to the server

[1085] Step 2:

[1086] emotion recognition

[1087] The server passes the received user input data to an emotion engine and analyzes the emotion. For example, it uses the Google Cloud Natural Language API to analyze whether the user is feeling "anxious."

[1088] Input: User question data

[1089] Data processing: Emotion analysis using an emotion engine

[1090] Output: Sentiment analysis results (e.g., anxiety, relief, etc.)

[1091] Step 3:

[1092] Security Answer Generation

[1093] The server uses machine learning models (TensorFlow and PyTorch) to generate optimal security answers to user questions, including detailed information on the latest phishing scams and how to prevent them.

[1094] Input: User question data, sentiment analysis results

[1095] Data Computation: Answer Generation with Machine Learning Models

[1096] Output: Initial security answer

[1097] Step 4:

[1098] Adjusting your answers

[1099] The server adjusts the generated answer based on the results of the sentiment analysis. If the user feels anxious, the answer is adjusted to provide detailed security information. Conversely, if the user feels reassured, the answer is adjusted to provide only the minimum necessary information.

[1100] Input: Initial security answer, sentiment analysis results

[1101] Data processing: Adjusting responses based on sentiment analysis results

[1102] Output: Adjusted security answers

[1103] Step 5:

[1104] Providing answers

[1105] The server sends the adjusted security answer to the terminal.

[1106] The terminal displays the received security answer to the user, who then confirms the answer through the terminal.

[1107] Input: Tailored security answer

[1108] Output: The security answers displayed to the user

[1109] In this way, the server realizes a system that provides optimal security answers according to the user's emotions.

[1110] (Application example 1)

[1111] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1112] Conventional security systems provide uniform security information without considering the user's emotional state, which means that they may not be able to provide appropriate information even when the user is feeling anxious or scared.In addition, while there is a demand for improved accuracy in security-related responses and security compliance rates, there has been a lack of effective means to achieve this.

[1113] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. In this invention, the server includes means for enabling security response accuracy, security compliance rate, and application of security to the latest trends in system development, means for providing guidance for preparation of security design before development, means for providing server development input, means for enabling milestone setting based on internal and external data, means for recognizing user emotions and adjusting the method of providing security information based on those emotions, and means for generating optimal security answers using machine learning and artificial intelligence. This makes it possible to provide appropriate security information according to the user's emotional state, thereby improving the accuracy of security response and security compliance rate.

[1114] "Security answer accuracy" refers to the ability to provide accurate and relevant answers to security questions or problems.

[1115] "Security compliance rate" refers to the percentage of actions taken in accordance with security policies and guidelines.

[1116] "Applying security to the latest trends in system development" refers to applying security measures in system development that incorporates the latest technological trends and developments.

[1117] "Guide to Preparing for Security Design Before Development" provides guidance and advice for designing security in the early stages of system development.

[1118] "Server development input" means providing information and data necessary for server development.

[1119] "Setting milestones based on internal and external data" means using data from both inside and outside the company to set important milestones that indicate the progress of a project.

[1120] "Means for recognizing a user's emotions and adjusting the way security information is provided based on those emotions" refers to technology that detects a user's emotional state and changes the content and level of detail of the security information provided depending on that state.

[1121] "Means for generating optimal answers to security-related questions and problems using machine learning and artificial intelligence" refers to technology that uses machine learning and artificial intelligence techniques to automatically generate optimal answers to security-related questions and problems.

[1122] As an embodiment of the present invention, we will describe a system in which an emotion-aware security assistant is installed on a smartphone, which recognizes the user's emotions in real time and adjusts the way security information is provided based on the user's emotions.

[1123] The server uses the following hardware and software. The hardware uses a smartphone camera, and the software uses OpenCV for face detection and Keras for emotion recognition. This makes it possible to capture a user's facial image and recognize their emotions.

[1124] Specifically, the smartphone camera captures the user's face and detects it using OpenCV. The detected facial region is input into an emotion recognition model using Keras to predict the user's emotion. Based on the predicted emotion, the server adjusts how security information is provided. For example, if the user feels anxious, the server provides detailed security information to increase the user's sense of security. On the other hand, if the user feels reassured, the server provides only the minimum necessary information to ensure smooth operation.

[1125] This system makes it possible to provide appropriate security information according to the user's emotional state, thereby improving the accuracy of security-related responses and security compliance rates.

[1126] For example, when a user uses a smartphone, the camera captures the user's face and recognizes their emotions. For example, if the user feels anxious, the server displays a message saying, "We will provide you with detailed security information."

[1127] Examples of prompts to be input to a generative AI model include:

[1128] "If the user feels uneasy, create an application that provides detailed security information. The user's emotions are recognized from the facial images captured by the camera."

[1129] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1130] Step 1:

[1131] The smartphone camera captures the user's face. The input is the camera image, and the output is the captured facial image. Specifically, the camera captures the image in real time and cuts out the facial part from it.

[1132] Step 2:

[1133] The device uses OpenCV to detect faces. The input is a captured face image, and the output is the face's location (coordinates). Specifically, the face detection algorithm identifies the face's location in the image and obtains its coordinates.

[1134] Step 3:

[1135] The device inputs the detected face area into an emotion recognition model using Keras. The input is a face image extracted based on the face's position information, and the output is the emotion prediction result. Specifically, the face image is preprocessed and input into the emotion recognition model to predict the emotion.

[1136] Step 4:

[1137] The server receives the prediction results of the emotion recognition model and identifies the user's emotion. The input is the emotion prediction result, and the output is the identified emotion (e.g., anxiety, relief). Specifically, the server analyzes the prediction results and selects the emotion with the highest probability.

[1138] Step 5:

[1139] The server adjusts the way it provides security information based on the identified emotion. The input is the identified emotion, and the output is adjusted security information. Specifically, if the user feels anxious, detailed security information is generated, and if the user feels reassured, only the minimum necessary information is generated.

[1140] Step 6:

[1141] The terminal displays the adjusted security information to the user. The input is the adjusted security information, and the output is the information displayed to the user. Specifically, the generated security information is displayed on the screen and provided to the user.

[1142] Through the above processing steps, appropriate security information is provided according to the user's emotional state.

[1143] Example 2

[1144] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1145] Conventional systems have made it difficult to improve security compliance rates or quickly correct security violations. Furthermore, they do not provide security measures that take user emotions into account, potentially increasing user dissatisfaction and stress. Furthermore, automation of security checks and notification of violations are insufficient, resulting in situations where developers are unable to respond quickly. To solve these issues, a system that integrates regular security checks with the ability to analyze user emotions is needed.

[1146] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1147] In this invention, the server includes a means for enabling the accuracy of security-related answers, security compliance rate, and application of security to the latest trends in system development, a means for providing guidance on preparing for security design before development, a means for providing server development input, a means for enabling the setting of milestones based on internal and external data, a means for periodically performing security checks and, if a violation is found, for immediately notifying the user to make a correction, and a means for analyzing user emotions, identifying the cause of the security violation, and proposing remedial measures. This enables an improvement in security compliance rate and rapid security measures that take user emotions into consideration.

[1148] "Security answer accuracy" refers to the ability to provide accurate and prompt answers to security questions and problems.

[1149] "Security compliance rate" refers to the percentage of systems and users that comply with security policies and guidelines.

[1150] "Applying security to the latest trends in system development" refers to applying security measures in system development based on the latest technologies and trends.

[1151] "Guidance on preparing for security design before development" refers to providing guidance and advice on how to design with security in mind in the early stages of system development.

[1152] "Server development input" refers to providing information and data necessary for server development.

[1153] "Setting milestones based on internal and external data" refers to setting important milestones that indicate the progress of a project based on internal and external data.

[1154] "A means of conducting regular security checks and immediately notifying the user to make corrections if a violation is found" refers to a function that periodically checks the security status of the system and notifies the user to make corrections promptly if a problem is found.

[1155] "Means of analyzing user emotions, identifying the cause of a security breach, and proposing remedial measures" refers to a function that analyzes user emotional data to identify the cause of a security breach and proposes specific remedial measures.

[1156] The present invention provides a system for improving security compliance and providing security measures that take into consideration user emotions. Specific embodiments of this system will be described below.

[1157] Security Check Function

[1158] 1. The server starts a security check every day at midnight. This check is triggered automatically using a cron job.

[1159] 2. The server launches a security scanner, such as an open-source or commercial security scanner, to scan the entire system, specifically targeting web application endpoints, database configuration files, etc.

[1160] 3. The server analyzes the scan results and logs any violations found. For example, if a SQL injection vulnerability is found, it identifies the line number and file name of the code and logs a message saying, "A SQL injection vulnerability was found on a specific line in a specific file."

[1161] 4. At 8:00 AM the next morning, the server notifies the developer with details of the detected security violation. This notification is sent via email or chat tool. For example, a notification with the following content may be sent: "Yesterday's security check found an SQL injection vulnerability. File: Specific file, Line number: Specific line. How to fix: Use a parameterized query."

[1162] Emotion engine for improving security compliance

[1163] 1. While the user is operating the system, the device analyzes facial expressions and tone of voice in real time through a camera and microphone, using "emotion recognition software" and "cloud-based emotion analysis API."

[1164] 2. The device then sends the analyzed emotional data to the server. For example, if a user experiences an error while trying to change security settings and feels angry, the device will send data about their facial expression and tone of voice at that moment.

[1165] 3. The server analyzes the received sentiment data to identify the cause of the user's dissatisfaction. For example, if a particular security setting is complex and difficult to understand, it identifies the security violation related to that setting.

[1166] 4. The server suggests specific actions to the user to remediate the identified security violations. These suggestions are made via pop-up notifications or emails, providing specific advice such as "Try the following steps to simplify this configuration."

[1167] Examples of concrete examples and prompts

[1168] Specific examples

[1169] Security check function: Every night at midnight, an "open source security scanner" is used to perform security scans of web applications to detect SQL injection vulnerabilities. The next morning, a notification is sent to the developer stating, "SQL injection vulnerability found. File: specific file, line number: specific line. How to fix: Use a parameterized query."

[1170] Emotion engine for improved security compliance: If a user experiences an error while trying to change a security setting and feels angry, emotion recognition software can detect that emotion. The server then uses this information to identify the security setting that is causing the error and suggests specific actions, such as "Try these steps to simplify this setting."

[1171] Prompt Sentence Examples

[1172] "Please explain in detail the steps of the system that runs security checks every night and notifies developers the next morning if a violation is found."

[1173] Please explain in detail the process steps of a system that analyzes user sentiment, identifies the cause of a security breach, and suggests remediation measures.

[1174] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1175] Security Check Function

[1176] Step 1:

[1177] The server begins security checks every day at midnight.

[1178] Input: Cron job trigger

[1179] What it does: A cron job runs the security check script at the scheduled time.

[1180] Output: Security scanner launch

[1181] Step 2:

[1182] The server uses a security scanner to scan the entire system.

[1183] Input: Security scanner configuration file and list of scan targets

[1184] How it works: The security scanner scans web application endpoints and database configuration files.

[1185] Output: Report of scan results

[1186] Step 3:

[1187] The server analyzes the scan results and logs details of any violations found.

[1188] Input: Scan results report

[1189] What it does: Analyzes scan results and extracts detailed information about each violation (e.g., type of vulnerability, scope of impact, and how to remediate it).

[1190] Output: Violation details recorded in a log file

[1191] Step 4:

[1192] The server will notify the developer at 8am the next morning with details of the detected security breach.

[1193] Input: Violation details recorded in the log file

[1194] How it works: The notification system sends notifications to developers via email and chat tools.

[1195] Output: Notification sent to developer

[1196] Emotion engine for improving security compliance

[1197] Step 1:

[1198] While the user is operating the system, the device analyzes facial expressions and tone of voice in real time through a camera and microphone.

[1199] Input: Camera video, microphone audio

[1200] How it works: Emotion recognition software analyzes facial expressions and tone of voice to identify a user's emotional state.

[1201] Output: Emotion data

[1202] Step 2:

[1203] The device transmits the analyzed emotion data to the server.

[1204] Input: Emotion data

[1205] How it works: Uses a communication protocol to send emotion data to a server.

[1206] Output: Emotion data sent to the server

[1207] Step 3:

[1208] The server analyzes the received emotional data and identifies the cause of the user's dissatisfaction.

[1209] Input: Emotion data

[1210] Behavior: Analyzes emotional data to determine whether a particular action or setting is causing user frustration.

[1211] Output: Information about the cause of the complaint

[1212] Step 4:

[1213] The server suggests specific actions to the user to remedy identified security violations.

[1214] Input: Information about the cause of the complaint

[1215] What it does: Generates suggestions and sends them to the user via a popup notification and / or email.

[1216] Output: Proposal notification sent to user

[1217] (Application example 2)

[1218] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1219] In modern system development, it is difficult to simultaneously improve security compliance and user satisfaction. In particular, there is a need for systems that can respond quickly when security violations occur and suggest specific improvement actions to reduce user frustration and anger. Furthermore, security compliance must be improved by conducting regular security checks and providing immediate notification when violations are found.

[1220] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1221] In this invention, the server includes: a means for enabling the accuracy of security-related answers, the security compliance rate, and the application of security to the latest trends in system development; a means for providing guidance on preparing for security design before development; a means for providing server development input; a means for enabling the setting of milestones based on internal and external data; a means for periodically checking security and immediately notifying if a violation is found; and a means for analyzing user emotions and, if a security violation causes user dissatisfaction or anger, identifying the cause and proposing specific improvement actions. This makes it possible to simultaneously improve the security compliance rate and increase user satisfaction.

[1222] "Security answer accuracy" refers to the ability to provide accurate and prompt answers to security questions and problems.

[1223] "Security compliance rate" is an index that indicates the extent to which systems and users comply with security policies and guidelines.

[1224] "Applying security to the latest trends in system development" refers to introducing appropriate security measures for the latest system development technologies and trends.

[1225] "Guidance on Pre-Development Security Design Preparation" refers to providing guidance and advice on planning and designing necessary security measures in the early stages of system development.

[1226] "Server development input" refers to providing information and data necessary for server development.

[1227] "Setting milestones based on internal and external data" refers to using internal and external data to set important milestones to manage project progress.

[1228] "Means for performing regular security checks" refers to methods or processes for checking the security status of a system at regular intervals and inspecting for any problems.

[1229] "Means for immediate notification if a breach is detected" refers to a method or system for quickly communicating information to relevant parties when a security breach is detected.

[1230] "Means for analyzing a user's emotions" refers to techniques or methods for analyzing a user's facial expressions, tone of voice, etc. to determine their emotional state.

[1231] "Means for identifying the cause and proposing specific remedial actions when a security violation causes user dissatisfaction or anger" refers to a method or system that clarifies the cause and presents solutions when a security violation causes negative emotions in users.

[1232] As an embodiment of the present invention, a system is provided that simultaneously improves security compliance and increases user satisfaction. Specific embodiments of the system are described below.

[1233] System Program

[1234] The system includes a program that integrates security checks, sentiment analysis, and notification functions. The program consists of the following main components:

[1235] 1. Security check module: Periodically checks the system's security status and checks for any violations. If any violations are found, the module returns a list of the violations.

[1236] 2. Emotion Analysis Module: Analyzes the user's facial expressions and tone of voice to determine their emotional state. Data is collected using the camera and microphone, and analyzed using the emotion engine.

[1237] 3. Notification module: Sends appropriate notifications when a security breach is detected or when the user's sentiment is negative. Notifications can be sent via email or in-app notifications.

[1238] Hardware and Software

[1239] Hardware: Server, user device (smartphone, tablet, PC), camera, microphone

[1240] Software: Security check software, Emotion Engine, Notification Service

[1241] Data processing and calculation

[1242] Security Check: The server periodically scans system logs and configuration files to detect security violations. If any violations are found, the information is saved as a list.

[1243] Emotion analysis: The camera and microphone on the user's device are used to collect data on the user's facial expressions and voice, which are then sent to the emotion engine for analysis.

[1244] Notification: If a security breach or negative sentiment is detected, the notification module generates an appropriate message and sends it to the relevant parties.

[1245] Specific examples

[1246] For example, if a security breach is detected while a user is using their smartphone and the user is unhappy, the system will send a notification like this:

[1247] Developer notification: "Security breach detected: weak password"

[1248] User notification: "We suggest the following improvement action: Strengthen your password."

[1249] Prompt Sentence Examples

[1250] Examples of prompts to input to a generative AI model might include:

[1251] If a security breach is detected while a user is using their smartphone and the user is frustrated, what notification should the system send?

[1252] In this way, a system can be provided that simultaneously improves security compliance and increases user satisfaction.

[1253] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1254] Step 1:

[1255] The server periodically performs security checks. As input, it obtains system logs and configuration files and scans these data using security check software. As data processing, it analyzes the logs and configuration files to detect security violations. As output, if any violations are found, it saves the information as a list.

[1256] Step 2:

[1257] When a security violation is detected, the server launches a notification module. As input, it receives a list of security violations and generates a notification message. As data processing, it creates an appropriate message based on the violation details. As output, it sends an email or an in-app notification to the developer.

[1258] Step 3:

[1259] The user device periodically activates the emotion analysis module. As input, it uses a camera and microphone to collect data on the user's facial expressions and voice. As data processing, it sends this data to the emotion engine for analysis. As output, it determines the user's emotional state.

[1260] Step 4:

[1261] If the user's emotional state is negative, the server re-launches the notification module. It receives the analysis results from the emotion engine as input and generates a message suggesting an improvement action. It processes the data and selects an appropriate improvement action based on the user's emotional state. It outputs an email or in-app notification to the user.

[1262] Step 5:

[1263] The server uses a generative AI model to generate prompt sentences based on the user's emotional state and security breaches. As input, it receives the user's emotional data and security breach data and inputs them into the generative AI model. As data processing, the generative AI model generates prompt sentences. As output, it sends the prompt sentences to the notification module, which issues appropriate notifications.

[1264] Step 6:

[1265] The user receives the notification and performs the suggested improvement action. As input, the user receives the notification message and confirms the suggested action. As data processing, the user performs the suggested action and improves security compliance. As output, the security status is improved.

[1266] Example 3

[1267] Next, a description will be given of Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1268] In conventional system development, it was difficult to quickly reflect the latest security trends, making it difficult to improve security compliance. Furthermore, security measures were not proposed taking user emotions into consideration, and efforts to improve user security awareness were insufficient. Furthermore, there was a lack of means to utilize generative AI models in proposing security measures.

[1269] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1270] In this invention, the server includes a means for periodically checking the latest security trends and reflecting them in system development, a means for adjusting security measures based on user sentiment, and a means for proposing security measures using a generative AI model. This allows the latest security trends to be quickly reflected in system development and security measures that take user sentiment into consideration. Furthermore, by utilizing the generative AI model, more effective security measures can be proposed.

[1271] "Security answer accuracy" refers to the ability to provide accurate and prompt answers to security questions and problems.

[1272] "Security compliance rate" is an indicator that shows the extent to which a system or organization complies with established security policies and standards.

[1273] "Applying the latest trends in security to system development" refers to incorporating the latest security information and technologies into the system development process.

[1274] "Guidance on preparing for security design before development" refers to providing guidance and advice on security design in the early stages of system development.

[1275] "Server development input" refers to providing information and data necessary for server development.

[1276] "Setting milestones based on internal and external data" refers to setting important milestones for a project based on internal and external data.

[1277] "Means of regularly checking the latest security trends and reflecting them in system development" refers to a method of regularly collecting the latest security information and incorporating it into the system development process.

[1278] "Means for adjusting security measures based on user emotions" refers to a method for analyzing a user's emotional state and proposing or implementing security measures accordingly.

[1279] "Means for proposing security measures using generative AI models" refers to a method that utilizes generative AI technology to automatically suggest appropriate security measures.

[1280] "Means for analyzing user emotion data" refers to techniques and methods for analyzing a user's emotions, which allow the user's emotional state to be understood.

[1281] This invention is a system that periodically checks the latest security trends, reflects the results in system development, and adjusts security measures based on user sentiment. A specific embodiment of this system will be described below.

[1282] Check and reflect security trends

[1283] The server accesses reliable security information sources on the Internet (e.g., NIST's CVE database, security blogs) every Friday. It uses the Python requests library to retrieve the latest security information from these sources. Specifically, it uses the requests.get method to send a request to the API endpoint of the CVE database and parses the JSON data received as a response.

[1284] The acquired security information is analyzed using natural language processing libraries (e.g., NLTK, spaCy). Security measures necessary for system development are extracted from the analysis results. Detailed information about vulnerabilities is extracted from the JSON data, and their severity and scope of impact are evaluated.

[1285] The server automatically generates tasks in a project management tool (e.g., JIRA or Trello) to reflect the extracted security measures in the next week's system development plan. It uses the JIRA API to create new tasks and assign them to responsible parties.

[1286] Adjusting security measures with an emotion engine

[1287] The device uses an emotion engine (e.g., IBM Watson or Microsoft Azure Emotion API) to analyze the user's emotions in real time. The device captures the user's facial expressions and tone of voice through the camera and microphone and sends them to the emotion engine. Specifically, the device sends the image data captured by the camera to the Emotion API, and analyzes the returned emotion data.

[1288] The emotion data obtained as a result of the analysis is sent from the device to the server using an HTTP POST request.

[1289] The server proposes relevant new security measures based on the received emotion data. A generative AI model (e.g., OpenAI GPT-3) is used to generate a security measure proposal based on the user's emotion. Specifically, the generative AI model is input with a prompt such as, "If the user is excited about a new feature, please propose a security measure related to that feature," and a proposal is generated.

[1290] The user reviews the proposed security measures and applies them as necessary. The device applies the proposed security measures to the system based on the user's selection. For example, if the user selects to implement two-factor authentication, the device changes the system settings to enable two-factor authentication.

[1291] Examples of concrete examples and prompts

[1292] Checking and reflecting security trends: Every Friday, the server checks the NIST CVE database to collect information on newly discovered vulnerabilities. The collected information is analyzed and reflected in the system development plan for the following week.

[1293] Adjusting security measures using an emotion engine: If the device analyzes the user's facial expressions and recognizes that the user is excited about a new feature, the server will suggest security measures related to that feature (e.g., introducing two-factor authentication).

[1294] Example prompt sentence:

[1295] "How can I check the latest security trends and incorporate them into next week's system development plan?"

[1296] "Explain how you can adjust your security measures based on user sentiment."

[1297] In this way, the system provides security measures that take into account the user's feelings while incorporating the latest security trends. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1298] Step 1:

[1299] The server accesses trusted security information sources on the Internet every Friday. Specifically, it uses the Python requests library to retrieve the latest security information from the NIST CVE database and security blogs. The input requires the URL of the security information source, and the output is JSON data of the retrieved security information.

[1300] Step 2:

[1301] The server analyzes the acquired security information. It uses a natural language processing library (e.g., NLTK, spaCy) to extract detailed vulnerability information from the JSON data and evaluate its severity and scope of impact. The input required is the JSON data acquired in step 1, and the output is a list of analyzed security measures.

[1302] Step 3:

[1303] Based on the analysis results, the server automatically generates tasks in a project management tool (e.g., JIRA, Trello) to be reflected in the next week's system development plan. Specifically, it uses the JIRA API to create new tasks and assign them to responsible parties. The list of security measures obtained in step 2 is required as input, and the tasks registered in the project management tool are obtained as output.

[1304] Step 4:

[1305] The device uses an emotion engine (e.g., IBM Watson, Microsoft Azure Emotion API) to analyze the user's emotions in real time. The device captures the user's facial expressions and tone of voice through a camera or microphone and sends them to the emotion engine. The input requires data captured by the camera or microphone, and the output is analyzed emotional data.

[1306] Step 5:

[1307] The device sends the emotion data obtained as a result of the analysis to the server. Specifically, it sends the emotion data to the server using an HTTP POST request. The emotion data obtained in step 4 is required as input, and the emotion data sent to the server is obtained as output.

[1308] Step 6:

[1309] The server proposes relevant new security measures based on the received emotion data. It uses a generative AI model (e.g., OpenAI GPT-3) to generate a security measure proposal based on the user's emotion. The input requires the emotion data obtained in step 5 and a prompt to the generative AI model, and the output is the generated security measure proposal.

[1310] Step 7:

[1311] The user reviews the proposed security measures and applies them as necessary. The terminal applies the proposed security measures to the system based on the user's selection. The inputs are the proposal generated in step 6 and the user's selection, and the output is the applied security measures.

[1312] In this way, the system provides security measures that take into account the user's feelings while incorporating the latest security trends.

[1313] (Application example 3)

[1314] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1315] Conventional security systems have difficulty quickly reflecting the latest security trends and have not proposed security measures that take user feelings into consideration. As a result, they have not been able to fully improve security compliance rates or user security awareness.

[1316] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1317] In this invention, the server includes means for enabling the accuracy of security-related answers, security compliance rate, and application of security to the latest trends in system development, means for providing guidance on preparing for security design before development, means for providing server development input, means for enabling the setting of milestones based on internal and external data, means for analyzing user sentiment and proposing security measures based on the sentiment, and means for periodically checking the latest security trends and reflecting them in system development. This makes it possible to quickly reflect the latest security trends in system development and propose security measures that take user sentiment into consideration.

[1318] "Security answer accuracy" refers to the ability to provide accurate and prompt answers to security questions and problems.

[1319] "Security compliance rate" is an indicator that shows how well systems and users comply with security policies and standards.

[1320] "Applying the latest trends in security to system development" refers to incorporating the latest security information and technologies into the system development process.

[1321] "Guidance on preparing for security design before development" refers to providing guidance and advice on security design in the early stages of system development.

[1322] "Server development input" refers to providing information and data necessary for server development.

[1323] "Setting milestones based on internal and external data" refers to setting important milestones for a project based on internal and external data.

[1324] "Means for analyzing user emotions and proposing security measures based on those emotions" refers to a method for analyzing user emotions and proposing appropriate security measures based on the results.

[1325] "Means of regularly checking the latest security trends and reflecting them in system development" refers to a method of regularly collecting the latest security information and reflecting it in the system development process.

[1326] As an embodiment of the present invention, the following system can be constructed.

[1327] System configuration

[1328] The system consists of the following main components:

[1329] 1. Security Trend Check Module: Regularly check the latest security trends and reflect them in system development.

[1330] 2. Sentiment analysis module: Analyzes user emotions and suggests security measures based on those emotions.

[1331] 3. Security Design Guidance Module: Provides guidance on preparing for security design before development.

[1332] 4. Server development input module: Provides information and data necessary for server development.

[1333] 5. Milestone Setting Module: Set important milestones for your project based on internal and external data.

[1334] Hardware and software used

[1335] Hardware: Smartphones, servers

[1336] Software: Python, Requests library, EmotionRecognizer library

[1337] Data processing and calculation

[1338] 1. Security Trend Check Module:

[1339] The server retrieves the latest security information via API every Friday.

[1340] The acquired information is analyzed and reflected in system development.

[1341] 2. Sentiment Analysis Module:

[1342] Analyzes user input data (voice and text) and recognizes emotions.

[1343] Based on the recognized emotions, appropriate security measures are suggested.

[1344] 3. Security Design Guidance Module:

[1345] Provide guidance and advice on pre-development security design.

[1346] 4. Server Development Input Module:

[1347] Provides information and data necessary for server development.

[1348] 5. Milestone Setting Module:

[1349] Set key project milestones based on internal and external data.

[1350] Specific examples

[1351] For example, if a user is excited about a new app feature, the system can suggest security measures related to that feature. The system also automatically retrieves the latest security information every Friday and updates it, ensuring that users always have the latest security measures.

[1352] Prompt Sentence Examples

[1353] When a user gets excited about a new app feature, how can you suggest security measures related to that feature?

[1354] In this way, by proposing security measures that utilize the user's emotions, it is possible to improve the user's security awareness.

[1355] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1356] Step 1:

[1357] The server retrieves the latest security information through an API every Friday. The input is the API endpoint and the output is the latest security information. The server uses the Requests library to send API requests and parse the retrieved data.

[1358] Step 2:

[1359] The server analyzes the latest security information it has acquired and reflects it in system development. The input is the latest security information, and the output is updated security measures. The server updates the system's security settings based on the analysis results.

[1360] Step 3:

[1361] The device collects user input data (voice or text). The input is the user's voice or text data, and the output is the collected data. The device collects data using a microphone or keyboard.

[1362] Step 4:

[1363] The server analyzes the collected user input data using the EmotionRecognizer library to recognize the user's emotions. The input is the user's voice or text data, and the output is the recognized emotion. The server analyzes the data using an emotion analysis model.

[1364] Step 5:

[1365] The server proposes appropriate security measures based on the recognized emotion. The input is the recognized emotion, and the output is the proposed security measures. The server retrieves security measures corresponding to the emotion from the database and proposes them to the user.

[1366] Step 6:

[1367] The server provides guidance and advice on pre-development security design. The input is development project information, and the output is security design guidance. The server analyzes the project information and proposes an appropriate security design.

[1368] Step 7:

[1369] The server provides the information and data required for server development. The input is the requirements for the development project, and the output is the information and data provided. The server collects the necessary data based on the requirements and provides it to the development team.

[1370] Step 8:

[1371] The server sets key milestones for the project based on internal and external data. The input is internal and external data, and the output is the set milestones. The server analyzes the data and sets milestones to manage the progress of the project.

[1372] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1373] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1374] Another example of generative AI is Gemini (internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[1375] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1376] [Third embodiment]

[1377] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1378] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1379] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1380] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1381] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1382] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1383] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1384] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1385] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1386] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1387] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1388] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1389] "Example 1"

[1390] The system of the present invention uses algorithms based on the latest security technologies to improve the accuracy of security-related answers. Specifically, it utilizes machine learning and artificial intelligence to generate optimal answers to security-related questions.

[1391] "Example 2"

[1392] In addition, as a means of improving security compliance, the system periodically checks security and immediately notifies developers to correct any violations that are found. Specifically, the system performs security checks late at night every day, and if a violation is found, it notifies developers the following morning.

[1393] "Example 3"

[1394] Furthermore, as a means of enabling the application of security to the latest trends in system development, the system periodically checks the latest security trends and reflects them in system development. Specifically, the system checks the latest security trends every Friday and reflects them in the next week's system development.

[1395] The processing flow of each embodiment will be described below.

[1396] "Example 1"

[1397] Step 1: The system uses algorithms based on the latest security technology to generate optimal solutions to security problems.

[1398] Step 2: Specifically, we use machine learning and artificial intelligence to generate optimal answers to security problems.

[1399] Step 3: The generated answers are used to improve the accuracy of the system's security answers.

[1400] "Example 2"

[1401] Step 1: The system will periodically check your security and notify you immediately if any violations are found, prompting you to correct them.

[1402] Step 2: Specifically, the system performs a security check every night, and if a violation is found, it notifies the developer the following morning.

[1403] Step 3: This notification allows developers to immediately fix security violations, improving system security compliance.

[1404] "Example 3"

[1405] Step 1: The system will regularly check the latest security trends and reflect them in system development.

[1406] Step 2: Specifically, the system checks for the latest security trends every Friday and reflects them in the following week's system development.

[1407] Step 3: This method ensures that system development always corresponds to the latest security trends, improving system security.

[1408] Example 1

[1409] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1410] Conventional security systems have had issues with low accuracy of security-related responses, making it difficult to improve security compliance rates. Furthermore, it was difficult to apply the latest security trends to system development, resulting in insufficient preparation for security design before development. Furthermore, there were issues with the inability to effectively provide server development input or set milestones based on internal and external data.

[1411] The identification process by the identification processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means. In this invention, the server includes means for improving the accuracy of security-related answers, means for improving the security compliance rate, means for enabling security to be applied to the latest trends in system development, means for providing guidance on preparing for security design before development, means for providing server development input, means for enabling milestone setting based on internal and external data, means for inputting prompt statements, means for collecting security data, means for preprocessing the collected data, means for training a machine learning model, means for evaluating the trained model, means for generating an optimal answer, and means for providing the generated answer. This makes it possible to quickly and accurately provide optimal answers to security-related questions.

[1412] "Security Answer Accuracy" refers to the accuracy and reliability of answers provided to security questions or problems.

[1413] "Security compliance rate" is an indicator that shows the percentage of compliance with security policies and regulations.

[1414] "Applying the latest trends in security to system development" refers to incorporating the latest security technologies and trends into the system development process.

[1415] "Guidance on preparing for security design before development" refers to providing guidance and advice on security design in the early stages of system development.

[1416] "Server development input" refers to providing information and data necessary for server development.

[1417] "Setting milestones based on internal and external data" refers to using internal and external data to set important milestones that indicate the progress of a project.

[1418] A "prompt sentence" refers to a question or instruction sentence that a user inputs to a system.

[1419] "Security Data" means security-related information and data, including network traffic, log files, and system events.

[1420] "Data preprocessing" refers to the process of converting collected data into a format suitable for analysis and learning. Specifically, it includes data cleaning, normalization, feature extraction, etc.

[1421] A "machine learning model" refers to an algorithm or mathematical model that learns patterns and rules from data and makes predictions and classifications.

[1422] "Model evaluation" refers to the process of measuring the performance of a trained machine learning model and confirming its effectiveness, using metrics such as precision, recall, and F1 score.

[1423] "Generating optimal answers" refers to creating the most appropriate answer to a user's question based on collected data and a trained model.

[1424] "Providing an answer" refers to displaying or communicating the generated answer to the user.

[1425] This invention is a system for improving the accuracy of security-related answers, using algorithms based on the latest security technologies. Specifically, it utilizes machine learning and artificial intelligence to generate optimal answers to security-related questions.

[1426] First, the user inputs a prompt sentence using a terminal, for example, "What is the best way to detect unauthorized access in a company network?"

[1427] The server collects relevant security data based on prompts entered by the user, and this data is obtained from network traffic, log files, system events, etc. The hardware used is a network sensor or a log server.

[1428] The server then preprocesses the collected data, specifically cleaning the data (filling in missing values ​​and removing outliers), normalizing the data (scaling the data), and extracting features (extracting important data points). The software used is Python's Pandas and NumPy libraries.

[1429] The server uses the preprocessed data to train a machine learning model. The algorithms used are deep learning (e.g., CNN, RNN) and support vector machines (SVM). The software used is TensorFlow and Scikit-learn.

[1430] To evaluate the trained model, the server uses evaluation metrics (precision, recall, F1 score, etc.) and the software used is the evaluation function of Scikit-learn. For example, it checks whether the accuracy of the model is 90% or higher.

[1431] Based on the evaluation results, the server generates an optimal solution to the security problem. The software used is a generative AI model (e.g., GPT-3). For example, it generates an answer such as, "It is best to use an anomaly detection algorithm to detect anomalies in network traffic."

[1432] Finally, the server provides the generated answer to the user's terminal, and the user can implement security measures based on the provided answer.

[1433] This system allows users to quickly and accurately find the best possible answers to security questions.

[1434] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1435] Step 1: Enter the prompt statement

[1436] The user inputs a prompt into the input field of the terminal. For example, the user might input, "What is the best way to detect unauthorized access in a company network?" This prompt becomes the input to the system.

[1437] Step 2: Data collection

[1438] The server collects relevant security data based on the prompts entered by the user, such as network traffic data, log files, and system events. The hardware used is a network sensor and a log server. The collected data is used as input for the next step.

[1439] Step 3: Data Preprocessing

[1440] The server preprocesses the collected data. Specifically, it performs data cleaning (filling in missing values ​​and removing outliers), normalization (scaling the data), and feature extraction (extracting important data points). The software used is Python's Pandas and NumPy libraries. The preprocessed data becomes the input for the next step.

[1441] Step 4: Model training

[1442] The server uses the preprocessed data to train a machine learning model. The algorithms used are deep learning (e.g., CNN, RNN) and support vector machines (SVM). The software used is TensorFlow or Scikit-learn. The trained model becomes the input for the next step.

[1443] Step 5: Model evaluation

[1444] The server evaluates the trained model. Evaluation uses metrics such as precision, recall, and F1 score. The software used is Scikit-learn's evaluation function. For example, it checks whether the model's accuracy is 90% or higher. The evaluation results are input to the next step.

[1445] Step 6: Generate the optimal solution

[1446] The server generates an optimal answer based on the evaluation results. This answer is for a security-related problem. The software used is a generative AI model (e.g., GPT-3). For example, it generates an answer such as, "It is good to use an anomaly detection algorithm to detect anomalies in network traffic." The generated answer becomes the input for the next step.

[1447] Step 7: Provide the answer

[1448] The server provides the generated answer to the user's terminal. The user can implement security measures based on the provided answer. The answer is output to the user.

[1449] (Application example 1)

[1450] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1451] In today's information society, security issues are becoming increasingly complex, requiring rapid and accurate responses. However, conventional systems provide low accuracy in security responses and have not achieved sufficient improvements in security compliance rates. Furthermore, systems often lack the ability to adapt to the latest trends in system development and to adequately prepare for security design prior to development. Furthermore, there is the issue of difficulty in quickly providing optimal answers when users ask security questions.

[1452] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1453] In this invention, the server includes means for enabling the accuracy of security-related answers, security compliance rates, and application of security to the latest trends in system development, means for providing guidance on preparing for security design before development, means for providing server development input, means for enabling the setting of milestones based on internal and external data, means for generating optimal answers to security-related questions entered by users using a generative AI model, means for evaluating security risks in real time and issuing warnings, and means for learning from past security incidents and predicting future risks.This enables quick and accurate responses to security issues, improves security compliance rates, and effectively applies security to system development.

[1454] "Security answer accuracy" refers to the ability to provide accurate and relevant answers to security questions or problems.

[1455] "Security compliance rate" refers to the percentage of actions taken in accordance with security policies and guidelines.

[1456] "Applying security to the latest trends in system development" refers to the appropriate implementation of security measures in system development that incorporates the latest technologies and trends.

[1457] "Guidance on Pre-Development Security Design Preparation" refers to providing guidance and advice on planning and designing security measures in the early stages of system development.

[1458] "Server development input" refers to providing information and data necessary for server design and development.

[1459] "Setting milestones based on internal and external data" refers to using internal and external data to set important milestones that indicate the progress of a project.

[1460] A "generative AI model" refers to an algorithm or system that uses artificial intelligence to learn from data and generate new information or answers.

[1461] "Means of assessing security risks and issuing warnings in real time" refers to the ability to instantly analyze ongoing situations, detect potential security risks, and issue warnings.

[1462] "Means of learning from past security incidents and predicting future risks" refers to the function of analyzing security issues that have occurred in the past and predicting future risks based on that knowledge.

[1463] As an embodiment of the present invention, the following system can be constructed.

[1464] First, the server includes the following means:

[1465] 1. A means to enable the accuracy of security responses, security compliance rates, and application of security to the latest trends in system development.

[1466] 2. A means of providing guidance for security design preparation before development.

[1467] 3. A means of providing server development input.

[1468] 4. A means to enable milestone setting based on internal and external data.

[1469] 5. A means of generating optimal answers to security questions entered by users using generative AI models.

[1470] 6. A means of assessing and alerting security risks in real time.

[1471] 7. A means of learning from past security incidents and predicting future risks.

[1472] Hardware and software used

[1473] Hardware: Smartphones, servers

[1474] Software: Python, OpenAI API

[1475] Data processing and calculation

[1476] The server receives security questions entered by the user through a smartphone application, then sends the questions to a generative AI model to generate the optimal answer, which is then displayed to the user through the smartphone application.

[1477] In addition, the server assesses security risks in real time, issues warnings as needed, and has the ability to learn from past security incidents and predict future risks.

[1478] Specific examples

[1479] For example, if a user asks, "How do I spot a phishing email?", the server sends the following prompt to the generative AI model:

[1480] Security Question: How do I spot a phishing email?

[1481] Generate the best answer.

[1482] The generative AI model generates an answer based on this prompt, and the server provides the answer to the user. Examples of generated answers include:

[1483] Here's how to spot a phishing email:

[1484] 1. Check the sender's email address.

[1485] 2. Don't click on links in emails.

[1486] 3. Be wary of emails requesting personal information.

[1487] 4. Check your email for grammar and spelling mistakes.

[1488] 5. Report any suspicious emails to your IT department.

[1489] In this way, users can get fast and accurate answers to their security questions, assess security risks in real time, and predict future risks by learning from past incidents.

[1490] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1491] Step 1:

[1492] A user launches an application on their smartphone and enters a security question. The entered question is sent to the server by the application. The input data is the user's question, and the output data is the question sent to the server.

[1493] Step 2:

[1494] The server analyzes the received question and generates a prompt to send to the generative AI model. The input data is the user's question, and the output data is the generated prompt. Specifically, the server analyzes the content of the question and converts it into an appropriate format.

[1495] Step 3:

[1496] The server sends the generated prompt to the generative AI model and generates the optimal answer. The input data is the prompt, and the output data is the answer returned by the generative AI model. Specifically, the server sends the prompt to the generative AI model and receives a response from the model.

[1497] Step 4:

[1498] The server analyzes the answer received from the generative AI model and converts it into a format for display to the user. The input data is the answer from the generative AI model, and the output data is the answer to be displayed to the user. Specifically, the server analyzes the content of the answer and formats it into a format that is easy for the user to understand.

[1499] Step 5:

[1500] The server sends the formatted answer to the smartphone application and displays it to the user. The input data is the formatted answer, and the output data is the answer displayed on the user's smartphone. Specifically, the answer is sent to the application, and the application displays it to the user.

[1501] Step 6:

[1502] The server evaluates security risks in real time and issues warnings if necessary. The input data is information about the current security situation, and the output data is a warning message. Specifically, it runs an algorithm to evaluate security risks and generates a warning if a risk is detected.

[1503] Step 7:

[1504] The server learns from past security incidents and predicts future risks. The input data is data about past security incidents, and the output data is a prediction of future risks. Specifically, the server analyzes past data and updates the model for predicting future risks.

[1505] Example 2

[1506] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1507] Modern system development requires improved security compliance and rapid response. However, the frequency of security checks and response after violation detection are often delayed, resulting in a risk of system vulnerabilities being left unaddressed. Furthermore, there is often a lack of means to apply the latest security trends and preparation for security design before development. To address these issues, regular security checks, rapid notification, and efficient responses using generative AI models are required.

[1508] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for enabling security response accuracy, security compliance rate, and application of security to the latest trends in system development, a means for providing guidance on preparing for security design before development, a means for providing server development input, a means for enabling milestone setting based on internal and external data, a means for periodically performing security checks, a means for generating a notification urging correction when a violation is found, a means for generating notification content using a generative AI model, and a means for sending a notification to a developer. This enables an improvement in security compliance rate and a quick response.

[1509] "Security answer accuracy" refers to the ability to provide accurate and prompt answers to security questions and problems.

[1510] "Security compliance rate" is an indicator of how well a system complies with security policies and regulations.

[1511] "Applying the latest trends in security to system development" refers to incorporating the latest security technologies and best practices into the system development process.

[1512] "Guidance on preparing for security design before development" refers to providing guidance and advice on how to design with security in mind in the early stages of system development.

[1513] "Server development input" refers to providing information and data necessary for server development.

[1514] "Setting milestones based on internal and external data" refers to setting important milestones based on internal and external data to manage the progress of a project.

[1515] "Means for performing regular security checks" refers to methods and tools for checking the security status of a system at regular intervals.

[1516] "Means for generating notifications to encourage remediation when violations are found" refers to methods or tools for creating notifications to encourage remediation when a security violation is detected.

[1517] "Means for generating notification content using generative AI models" refers to methods or tools that use artificial intelligence models to create notification content that includes details of a security breach and how to remediate it.

[1518] "Means for sending notifications to a developer" means the methods and tools used to deliver generated notifications to a developer.

[1519] The present invention is a system for improving security compliance, in which a server, a terminal, and a user work together. A specific embodiment of this system will be described below.

[1520] The server uses a scheduling tool (e.g., cron job or Windows Task Scheduler) to run a security check every night. The server also launches a security scanning tool (e.g., Nessus or OpenVAS) to perform a system-wide security check, which performs a detailed scan of the network and system configuration to detect vulnerabilities and security violations.

[1521] Once the scan is complete, the server analyzes the scan results. This involves reading the report file generated by the scanning tool and extracting a list of vulnerabilities and security violations. Specifically, the server reads the Nessus report file (e.g., in XML format) and uses XPath queries to extract vulnerability information.

[1522] The server then uses a generative AI model (e.g., GPT-4) to generate a notification based on the scan results, specifically a prompt that details the vulnerabilities or violations detected and explains how to remediate them. Here's an example prompt:

[1523] Example prompt sentence:

[1524] Outdated software has been found. Please update the following software to the latest version:

[1525] Software Name: ExampleSoftware

[1526] Current version: 1.0.0

[1527] Latest version: 1.2.0

[1528] Please see the link below for update instructions:

[1529] [Update procedure link]

[1530] The server sends the generated notification content to the developer. The notification is sent via email or a chat tool (for example, Slack or Microsoft Teams). Specifically, the server uses an SMTP server to send an email containing the generated notification content to the developer's email address, or uses the Slack API to post the notification content to the developer's Slack channel.

[1531] Users (developers) receive notifications and follow instructions to update their software, which improves system security compliance and quickly fixes vulnerabilities.

[1532] The system can improve security compliance through regular security checks and prompt notifications, and by utilizing generative AI models, it can streamline notification content generation, reducing the burden on developers.

[1533] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1534] Step 1:

[1535] The server uses a scheduling tool to set up a job to run a security check every night.

[1536] Input: Scheduling tool setting information

[1537] Data Processing: Job Scheduling

[1538] Output: A job that runs a security check every night at midnight

[1539] Specific operation: The server sets up a cron job and registers a command to run a security scanning tool at 2:00 AM every day.

[1540] Step 2:

[1541] The server launches a security scanning tool according to a set schedule and performs a security check of the entire system.

[1542] Inputs: Scheduled jobs, security scanning tools

[1543] Data Processing: Full System Scan

[1544] Output: Scan result report file

[1545] What happens: The server launches Nessus and initiates a network-wide scan, which examines the configuration of each device and server in detail to detect known vulnerabilities and misconfigurations.

[1546] Step 3:

[1547] Once the scan is complete, the server analyzes the scan results.

[1548] Input: Scan result report file

[1549] Data processing: Analyzing report files and extracting vulnerability information

[1550] Output: A list of vulnerabilities and security violations

[1551] Specific operation: The server reads the Nessus report file (e.g., XML format) and extracts vulnerability information using XPath queries.

[1552] Step 4:

[1553] The server uses a generative AI model to generate notification content based on the scan results.

[1554] Input: List of vulnerabilities and security breaches

[1555] Data processing: Notification content generation

[1556] Output: Generated notification content (prompt text)

[1557] Specific behavior: The server inputs the following prompt into GPT-4 to generate the notification content:

[1558] The following vulnerabilities were discovered:

[1559] Vulnerability Name: Outdated Software

[1560] Affected: ExampleSoftware 1.0.0

[1561] To fix this vulnerability, please update ExampleSoftware to the latest version 1.2.0. Please see the following link for update instructions:

[1562] [Update procedure link]

[1563] Step 5:

[1564] The server sends the generated notification content to the developer.

[1565] Input: Generated notification content (prompt text)

[1566] Data Processing: Sending Notifications

[1567] Output: Notification sent to developer

[1568] Specific behavior: The server uses an SMTP server to send an email containing the generated notification content to the developer's email address, or uses the Slack API to post the notification content to the developer's Slack channel.

[1569] Step 6:

[1570] The user (developer) receives a notification and follows the instructions to update the software.

[1571] Input: Notification content (prompt text)

[1572] Data Processing: Software Update

[1573] Output: Updated software

[1574] Action: The user acknowledges the notification and follows the instructions to update the software to the latest version, thereby improving system security compliance.

[1575] (Application example 2)

[1576] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1577] Increasing security compliance is extremely important in modern system development. However, when security checks are performed manually, they tend to be performed infrequently, and when violations are found, responses are often delayed. Furthermore, detailed explanations of violations and the provision of remediation methods are insufficient, making it difficult for developers to respond quickly. Furthermore, the lack of a way to manage past security check results and remediation history makes continuous security improvement difficult.

[1578] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for enabling the accuracy of security-related answers, security compliance rate, and application of security to the latest trends in system development, means for providing guidance on preparing for security design before development, means for providing server development input, means for enabling the setting of milestones based on internal and external data, means for conducting regular security checks and, if a violation is found, for issuing a notification urging immediate correction, means for providing a guide detailing the violation and how to correct it, and means for saving and making available past security check results and correction history. This enables an improvement in security compliance rate and rapid response to violations.

[1579] "Security answer accuracy" refers to the ability to provide accurate and prompt answers to security questions and problems.

[1580] "Security compliance rate" refers to the percentage of a system or organization that complies with established security policies and standards.

[1581] "Applying security to the latest trends in system development" refers to introducing appropriate security measures for the latest system development technologies and trends.

[1582] "Guidance on preparing for security design before development" refers to providing guidance and advice on how to design with security in mind in the early stages of system development.

[1583] "Server development input" refers to providing information and data necessary for server development.

[1584] "Setting milestones based on internal and external data" refers to using internal and external data to set important milestones to manage the progress of a project.

[1585] "Performing regular security checks" refers to the process of checking the security status of a system at regular intervals.

[1586] "If a violation is found, provide immediate notification to prompt correction" refers to sending a notification to promptly correct a security violation when it is detected.

[1587] A "guide detailing the breach and how to fix it" refers to a document or tool that details the specific nature of the security breach and how to fix it.

[1588] "Saving and making available the results of past security checks and correction history" means recording the results of previous security checks and the history of corrections, and making them available for reference as needed.

[1589] As an embodiment of the present invention, a security check alert system is constructed, the details of which are described below.

[1590] System configuration

[1591] This system consists of a server, a terminal (such as a smartphone), and a user (developer). The server periodically performs security checks and sends notifications if a violation is found. The terminal is a device that the user uses to receive notifications and check the details of the violation and how to fix it.

[1592] Hardware and software used

[1593] Hardware: Servers (cloud infrastructure or on-premise servers), smartphones

[1594] Software: Python, smtplib (email sending library), schedule (schedule library)

[1595] Data processing and calculation

[1596] The server processes the data as follows:

[1597] 1. Security check execution: The server automatically executes a security check every night. If a security violation is found as a result of the check, the details of the violation are recorded.

[1598] 2. Sending violation notification: If a violation is found, the server will send a push notification to the developer's device the next morning, containing details of the violation and instructions on how to fix it.

[1599] 3. History storage: The server stores past security check results and correction history in a database and makes them available for reference as needed.

[1600] Specific examples

[1601] For example, if a company's IT infrastructure performs a security check every night and an SQL injection vulnerability is found, the server will send a notification to the developer's smartphone the next morning saying, "An SQL injection vulnerability has been found. Please refer to the link below for how to fix it." The developer will receive the notification and be able to respond quickly by following the detailed fix guide.

[1602] Prompt Sentence Examples

[1603] "Implement a system that performs security checks every night and notifies developers the next morning if a violation is found. Specifically, create a program that uses Python to perform security checks and sends a notification by email if a violation is found."

[1604] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1605] Step 1:

[1606] The server runs a security check every night at midnight. As input, it takes data about the security state of the system and applies the security check algorithm. As output, it generates a list of security violations. Specifically, the server uses a scheduling library to start the security check at the specified time.

[1607] Step 2:

[1608] The server analyzes the results of the security checks to determine if any violations were found. As input, it uses the list of security violations generated in step 1. As output, it generates a notification message containing the details of any violations found. Specifically, the server loops through the list of violations to determine if any exist.

[1609] Step 3:

[1610] If a violation is found, the server will send a push notification to the developer's device the next morning. As input, it uses the notification message generated in step 2. As output, the notification will be displayed on the developer's device. Specifically, the server will use an email sending library to send the notification to the specified email address.

[1611] Step 4:

[1612] The user (developer) receives a notification on their device and checks the details of the violation and how to fix it. The notification message sent from the server is used as input. As output, the user accesses the fix guide and checks the specific fix steps. As a specific operation, the user taps the notification on their device to display the detailed fix guide.

[1613] Step 5:

[1614] The server stores past security check results and correction history in a database and makes them available for reference as needed. It uses security check results and correction history as input and generates historical data stored in the database as output. Specifically, the server connects to the database and executes queries to store the results and history.

[1615] Example 3

[1616] Next, a third embodiment of the third embodiment will be described. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1617] In traditional system development, it was difficult to quickly and accurately reflect the latest security trends, resulting in problems such as a decline in security compliance rates and increased security risks. Furthermore, the creation and notification of security measures was often done manually, which tended to be inefficient and require long lead times. Furthermore, insufficient preparation for security design before development and the setting of milestones based on internal and external data made it difficult to improve the security level of the entire system.

[1618] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1619] In this invention, the server includes means for periodically collecting the latest security trends, means for saving the collected security information in a database, means for analyzing the saved security information, means for generating security measures based on the analysis results, means for notifying the generated security measures, means for enabling the accuracy of security-related answers, security compliance rate, and application of security to the latest trends in system development, means for providing guidance for preparing security design before development, means for providing server development input, and means for enabling the setting of milestones based on internal and external data. This allows the latest security trends to be reflected in system development quickly and accurately, improving security compliance rate and reducing security risks.

[1620] "Latest security trends" refers to the latest information on ongoing security threats and vulnerabilities.

[1621] "Methods of collection" refers to the technologies and methods used to obtain data from reliable sources on the Internet.

[1622] "Means of database storage" refers to the systems and technologies used to store collected information in a structured format.

[1623] "Means for analysis" refers to techniques and methods for analyzing stored data and extracting useful information.

[1624] "Means for generating security measures" refers to the techniques and methods for creating specific security measures based on the analysis results.

[1625] "Means of notification" refers to the techniques and methods for communicating the security measures that have been created to relevant parties.

[1626] "Security answer accuracy" refers to the ability to accurately answer security questions or problems.

[1627] "Security compliance rate" refers to the percentage of actions taken in accordance with security policies and guidelines.

[1628] "Applying security based on the latest trends to system development" refers to reflecting the latest security information in the system development process.

[1629] "Guidance on preparing for security design before development" refers to guidance and advice on planning and preparing necessary security measures in the early stages of system development.

[1630] "Server development input" refers to providing information and data necessary for server development.

[1631] "Setting milestones based on internal and external data" refers to setting important milestones to manage project progress based on internal and external data.

[1632] The present invention provides a system for quickly and accurately reflecting the latest security trends in system development. A specific embodiment of this system will be described below.

[1633] Every Friday, the server collects the latest security trends from trusted security sources on the internet using the Python libraries BeautifulSoup and Requests, for example by scraping information from CVE databases and security blogs.

[1634] The collected security information is stored in a database on the server using MySQL or PostgreSQL. The stored information includes the title, content, and publication date of the security information.

[1635] Next, the server analyzes the stored security information. For this analysis, it uses a generative AI model (e.g., OpenAI's GPT-4). Specifically, the security information is input to the generative AI model as a prompt sentence, and the analysis result is obtained.

[1636] Based on the analysis results, the server generates specific security measures, such as "performing escaping of input data" and "using prepared statements" to protect against SQL injection vulnerabilities.

[1637] The server notifies the development team of the generated security measures. Notifications are sent via email or chat tools (e.g., Slack, Microsoft Teams). The notification includes details of the measures and how to implement them.

[1638] The terminal (developer's PC) implements the notified security measures. The developer then modifies the code based on the notified measures to strengthen the security of the system. For example, to counter SQL injection, the developer can change the SQL query part in the code to a prepared statement.

[1639] As a concrete example, the following prompt sentence is input to the generative AI model:

[1640] "According to the information gathered from the latest CVE database, an SQL injection vulnerability has been reported. Please tell me the specific measures to prevent this vulnerability."

[1641] In this way, the server, terminal, and user cooperate to reflect the latest security trends in system development, thereby enhancing security. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1642] Step 1:

[1643] The server collects the latest security trends from reliable security sources on the Internet every Friday. It uses a list of URLs from security sources as input. Specifically, it uses the Python libraries BeautifulSoup and Requests to scrape information from CVE databases and security blogs. The collected security information is then returned as output.

[1644] Step 2:

[1645] The server stores the collected security information in a database. It uses the security information collected in step 1 as input. Specifically, it uses MySQL or PostgreSQL to store the title, content, publication date, etc. of the security information as fields. The output is the security information stored in the database.

[1646] Step 3:

[1647] The server analyzes the stored security information. As input, it uses the security information stored in the database. Specifically, it uses a generative AI model (e.g., OpenAI's GPT-4) and inputs the security information into the generative AI model as a prompt sentence. As output, it obtains the analysis results obtained from the generative AI model.

[1648] Step 4:

[1649] The server generates security measures based on the analysis results. The analysis results obtained in step 3 are used as input. Specific operations include creating specific security measures based on the information obtained from the generative AI model. The generated security measures are obtained as output.

[1650] Step 5:

[1651] The server notifies the development team of the generated security measures. The security measures generated in step 4 are used as input. Specific operations include notifying the development team of the measures via email or chat tools (e.g., Slack, Microsoft Teams). The output is the security measures notified to the development team.

[1652] Step 6:

[1653] The terminal (developer's PC) implements the notified security measures. It uses the security measures notified in step 5 as input. Specific actions include modifying the code based on the notified measures to strengthen the security of the system. The output is a system with the security measures implemented.

[1654] (Application example 3)

[1655] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1656] In modern system development, it is extremely important to quickly and accurately reflect the latest security trends. However, manually checking security trends and incorporating them into systems is time-consuming and labor-intensive, and inefficient. Furthermore, there are a wide range of security-related challenges, such as improving security compliance rates, preparing security designs before development, providing server development input, and setting milestones based on internal and external data. A comprehensive system to solve these challenges is needed.

[1657] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means. In this invention, the server includes: means for enabling security response accuracy, security compliance rate, and application of security to the latest trends in system development; means for providing guidance on preparing for security design before development; means for providing server development input; means for enabling milestone setting based on internal and external data; means for periodically checking the latest security trends and reflecting them in system development; and means for acquiring security trends through an application installed on a smart device and updating system settings. This makes it possible to quickly and efficiently reflect the latest security trends in system development.

[1658] "Security response accuracy" refers to the ability to provide accurate and prompt responses to security questions and problems.

[1659] "Security compliance rate" is an indicator that shows the extent to which a system or organization complies with established security policies and standards.

[1660] "Applying security to the latest trends in system development" refers to incorporating the latest security trends and technologies into the system development process.

[1661] "Guidance on preparing for security design before development" refers to providing guidance and advice on how to properly design security in the early stages of system development.

[1662] "Server development input" refers to providing information and data necessary for server development.

[1663] "Setting milestones based on internal and external data" refers to setting important milestones to manage the progress of a project based on internal and external data.

[1664] "Means of regularly checking the latest security trends and reflecting them in system development" refers to a method of regularly collecting the latest security information and applying it to the system development process.

[1665] "Means of obtaining security trends through applications installed on smart devices and updating system settings" refers to a method of obtaining security information using applications installed on devices such as smartphones and tablets, and updating system settings based on that information.

[1666] As an embodiment of the present invention, a security management system is constructed, which is composed of a server, smart devices, and software that links them together.

[1667] The server is equipped with a means to ensure the accuracy of security-related responses, security compliance rates, and the application of security to the latest trends in system development. Specifically, the server periodically checks the latest security trends and collects data to reflect them in system development. The server obtains the latest security information using a security trend API, analyzes the information, and updates the system settings.

[1668] An application for acquiring security trends and updating system settings is installed on smart devices. This application acquires the latest security information from a server every Friday and notifies the user. The user checks the notification and updates the system settings as necessary.

[1669] In terms of specific hardware, the server is a computer equipped with a high-performance processor and large-capacity storage, and the smart device is a mobile information terminal such as a smartphone or tablet. In terms of software, Python and the requests library are used on the server side, and a dedicated security management application is used on the smart device side.

[1670] For data processing and calculation, the server analyzes the data obtained from the security trend API in JSON format and extracts the necessary information. The extracted information is used to update system settings. On the smart device side, the obtained information is notified to the user and the system settings are updated according to the user's operations.

[1671] As a concrete example, by inputting the following prompt sentence into a generative AI model, a program for a security control system can be generated.

[1672] Example prompt sentence:

[1673] "Write a Python program that checks the latest security trends and provides advice to be applied to the next week's system development. Include a process to retrieve data from an API and update the system configuration every Friday."

[1674] Using this prompt, the generative AI model can generate the necessary programs to assist in building a security control system.

[1675] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1676] Step 1:

[1677] The server sends a request to the security trend API every Friday. The API endpoint URL and authentication information are required as input. The server retrieves the latest security trend data from the API and receives it in JSON format. The retrieved security trend data is returned as output.

[1678] Step 2:

[1679] The server analyzes the acquired JSON-formatted security trend data. The JSON data acquired in step 1 is required as input. The server extracts the necessary information (e.g., new vulnerability information, recommended security patches, etc.) and stores it in an internal database. The analyzed security information is obtained as output.

[1680] Step 3:

[1681] The server determines whether the system settings need to be updated based on the analyzed security information. The analyzed security information obtained in step 2 is required as input. If the system settings need to be updated, the server generates the necessary content and creates an update script. The output is a system settings update script.

[1682] Step 4:

[1683] An application installed on a smart device retrieves the latest security information from a server every Friday. The server's endpoint URL and authentication information are required as input. The smart device notifies the user of the security information retrieved from the server. The output is the security information notified to the user.

[1684] Step 5:

[1685] The user checks the notification from the smart device and approves the update of the system settings as necessary. The input requires the notification content from the smart device. The user performs the operation of approving or rejecting the notification. The output is the result of the user's approval or rejection.

[1686] Step 6:

[1687] After receiving user approval, the smart device instructs the server to update the system settings. The user's approval result is required as input. The smart device requests the server to execute an update script. The output is an update request to the server.

[1688] Step 7:

[1689] The server receives an update request from the smart device and executes the system settings update script. The input requires an update request from the smart device. The server updates the system settings and sends the update results back to the smart device. The output is the updated system settings and the results.

[1690] Step 8:

[1691] The smart device receives the update results from the server and notifies the user. The input requires the update results from the server. The smart device displays the update results to the user and prompts the user to perform additional operations as necessary. The output is the update results notified to the user.

[1692] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1693] "Example 1"

[1694] As one embodiment of the present invention, a system incorporating an emotion engine is provided. This system recognizes a user's emotions and adjusts the accuracy of security-related answers based on those emotions. Specifically, if the system recognizes that the user is feeling anxious or scared, it provides more detailed security information to enhance the user's sense of security. Also, if the system recognizes that the user is feeling safe, it provides only the minimum necessary security information, allowing the user to perform operations smoothly.

[1695] "Example 2"

[1696] In another embodiment of the present invention, a system is provided in which an emotion engine improves security compliance based on a user's emotions. Specifically, if the system recognizes that the user is feeling anger or frustration, it identifies the security violation that caused it and suggests specific actions to improve it. In this way, by providing security measures that take the user's emotions into consideration, the system improves the user's security compliance.

[1697] "Example 3"

[1698] Furthermore, as another embodiment of the present invention, a system is provided in which an emotion engine adjusts the system's security measures based on the user's emotions. Specifically, if the system recognizes that the user is feeling happy or excited, it uses that emotion to suggest new security measures. For example, if the system recognizes that the user is excited about a new function, it suggests new security measures related to that function. In this way, by suggesting security measures that utilize the user's emotions, it is possible to improve the user's security awareness.

[1699] The processing flow of each embodiment will be described below.

[1700] "Example 1"

[1701] Step 1: The system recognizes the user's emotions using the emotion engine.

[1702] Step 2: If the emotion engine recognizes the user's emotion as anxiety or fear, the system will provide more detailed security information.

[1703] Step 3: If the emotion engine recognizes the user's emotion as safe, the system provides only the minimum necessary security information.

[1704] "Example 2"

[1705] Step 1: The system recognizes the user's emotions using the emotion engine.

[1706] Step 2: If the emotion engine identifies the user's emotion as anger or frustration, the system identifies the underlying security breach.

[1707] Step 3: The system suggests specific actions to remediate the identified security violations.

[1708] "Example 3"

[1709] Step 1: The system recognizes the user's emotions using the emotion engine.

[1710] Step 2: If the emotion engine recognizes the user's emotion as joy or excitement, the system will use that emotion to suggest new security measures.

[1711] Step 3: The system presents the proposed new se...

Claims

1. means for transmitting security questions entered by the user to a server; means for generating a prompt sentence for instructing the server to generate an optimal answer to the question; a generative AI model that receives the prompt sentence from the server and generates an optimal answer to the question; A means for the server to analyze the answer received from the generative AI model and present it to the user; means for the server to assess security risks in real time and generate an alert if a security risk is detected; A means for the server to learn from past security incidents and predict future risks; A system including:

2. A means for improving the accuracy of answers regarding security based on the answers; The system of claim 1 further comprising:

3. The system according to claim 1 , further comprising means for realizing an improvement in a security compliance rate, which represents a rate of behavior in accordance with a security policy or guideline, based on the answer.

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