System

Abstract

To provide a system.SOLUTION: The system includes: means of hearing in a dialogue format by an AI; means of diagnosing risk based on the contents of the hearing; means of diagnosing vulnerability for detecting vulnerability; emotion engine means of analyzing the emotion of a user; and means of proposing the current risk to the vulnerability detected by the vulnerability diagnosis means and the best security measures.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] As the DX / AI revolution progresses, information security measures are becoming increasingly important, but it is difficult to diagnose risks and vulnerabilities and propose optimal security measures to implement these measures appropriately. [Means for solving the problem]

[0005] We provide an information security diagnostic system that uses AI-based interactive interview methods to perform risk and vulnerability diagnosis based on the interview content, and proposes current risks and optimal security measures. [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 information security diagnostic system of the present invention has an AI-based interactive hearing means. Specifically, the system grasps the user's information security situation and concerns through dialogue with the user. This hearing covers a wide range of topics, such as the user's current information security management situation, past information leak cases, and whether or not they currently have security measures in place.

[0029] "Example 2"

[0030] The system of the present invention also includes risk assessment means and vulnerability assessment means based on the content of interviews. Specifically, information security risks are assessed based on the results of the interviews. This assessment comprehensively assesses various risks, such as the risk of information leakage, the risk of cyber attacks, and the risk of internal fraud. Furthermore, vulnerability assessment is performed to identify vulnerabilities in the system and network.

[0031] "Example 3"

[0032] Furthermore, the system of the present invention has a means for proposing current risks and optimal security measures. Specifically, optimal security measures are proposed based on the results of risk and vulnerability assessments. These proposals include specific measures such as installing a firewall, reviewing password policies, and providing security training to employees. The proposed measures are presented in a "pine, bamboo, and plum" format, allowing users to select the measures that best suit their own situation.

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

[0034] "Example 1"

[0035] Step 1: Initiate a dialogue with the user using an AI-powered conversational listening method to understand the user's information security situation and concerns.

[0036] Step 2: Based on the results of the interviews, assess information security risks. This assessment comprehensively assesses various risks, including the risk of information leaks, cyber attacks, and internal fraud.

[0037] Step 3: Further vulnerability assessment is performed to identify system and network vulnerabilities. Step 4: Based on the results of the risk and vulnerability assessment, optimal security measures are proposed. These proposals include specific measures such as the introduction of firewalls, review of password policies, and security education for employees.

[0038] "Example 2"

[0039] Step 1: Initiate a dialogue with the user using an AI-powered conversational listening method to understand the user's information security situation and concerns.

[0040] Step 2: Based on the results of the interviews, assess information security risks. This assessment comprehensively assesses various risks, including the risk of information leaks, cyber attacks, and internal fraud.

[0041] Step 3: Further vulnerability assessment is performed to identify system and network vulnerabilities. Step 4: Based on the results of the risk and vulnerability assessment, optimal security measures are proposed. These proposals include specific measures such as the introduction of firewalls, review of password policies, and security education for employees.

[0042] "Example 3"

[0043] Step 1: Initiate a dialogue with the user using an AI-powered conversational listening method to understand the user's information security situation and concerns.

[0044] Step 2: Based on the results of the interviews, assess information security risks. This assessment comprehensively assesses various risks, including the risk of information leaks, cyber attacks, and internal fraud.

[0045] Step 3: Further vulnerability assessment is performed to identify system and network vulnerabilities. Step 4: Based on the results of the risk and vulnerability assessment, optimal security measures are proposed. These proposals include specific measures such as the introduction of firewalls, review of password policies, and security education for employees.

[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 information security diagnostic systems have difficulty fully understanding users' information security situations and concerns, and often do not provide appropriate risk assessments or suggest security measures. As a result, users are unable to accurately understand their current information security situation and are unable to take appropriate measures.

[0049] 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.

[0050] In this invention, the server includes means for generating an initial question for the user using a generative AI model, means for the user to answer the question using a terminal and send the answer to the server, means for the server to analyze the user's answer and generate a next question, means for the server to analyze all answers and evaluate information security, and means to feed back the evaluation results to the user. This makes it possible to grasp the user's information security situation and concerns in detail, and to diagnose appropriate risks and propose security measures.

[0051] A "generative AI model" is an artificial intelligence technology that generates appropriate questions through dialogue with users and analyzes their answers.

[0052] The "server" is a computer system that receives input from users, generates questions using a generative AI model, analyzes the answers, and evaluates information security.

[0053] A "terminal" is a device through which a user answers questions and transmits the answers to a server.

[0054] "Means for generating questions" is a function that uses a generative AI model to create appropriate questions for users.

[0055] The "means for sending an answer" is a function for sending an answer entered by a user using a terminal to a server.

[0056] The "means for analyzing answers" is a function that allows the server to analyze the user's answers and generate the next question.

[0057] The "means for evaluating information security" is a function in which the server analyzes all responses and evaluates the current state of information security for the user.

[0058] "Means for feeding back evaluation results" is a function by which the server provides the results of information security evaluation to the user.

[0059] This invention relates to an information security diagnostic system that uses a generative AI model to understand information security situations and concerns through dialogue with users, and then performs appropriate risk diagnosis and proposes security measures.

[0060] First, the server uses a generative AI model to generate initial questions for the user. For example, OpenAI's GPT-4 (registered trademark) can be used as this generative AI model. The generated questions are intended to collect basic information about the user's information security.

[0061] The user answers questions sent from the server using a terminal. The terminal can be a PC, smartphone, tablet, etc. The answers entered by the user are sent from the terminal to the server. This communication is performed using a secure protocol (e.g., HTTPS).

[0062] The server analyzes the answers received from the user and generates the next question to gather the necessary information. This process is done automatically using a generative AI model. For example, if the user answers, "Customer data was leaked in June of last year," the server generates the next question: "What caused the data leak?"

[0063] The user then uses the device to answer the next question. This process is repeated until all the necessary information is collected. The server analyzes all the collected answers and evaluates the user's current information security status. This evaluation uses an AI model to identify risks and propose countermeasures.

[0064] The server provides feedback on the evaluation results to the user, providing specific advice such as, "Your current security measures are insufficient, and additional measures are required."

[0065] As a concrete example, the following prompt sentence could be input to a generative AI model:

[0066] Example prompt sentence:

[0067] "Has your organization experienced any data breaches in the past year? If so, please provide details."

[0068] In this way, users can objectively grasp their own information security situation and take necessary measures. This system enables effective management of information security risks and the implementation of appropriate measures.

[0069] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0070] Step 1:

[0071] A user accesses the system.

[0072] Users access the information security assessment system using a web browser or a dedicated application. They log in by entering their user ID and password. The input is the user ID and password, and the output is a message indicating whether the login was successful or failed.

[0073] Step 2:

[0074] The server generates an initial question for the user.

[0075] The server uses a generative AI model to generate an initial question for the user, for example, "Has your organization experienced a data breach in the past year?" The input is the user's login information, and the output is the generated question.

[0076] Step 3:

[0077] The user answers the question.

[0078] The user uses the terminal to answer questions sent by the server, for example, "Yes, customer data was leaked in June of last year." The input is the question from the server, and the output is the user's answer.

[0079] Step 4:

[0080] The terminal sends the user's answer to the server.

[0081] The terminal transmits the answers entered by the user to the server in real time. This communication is performed using a secure protocol (e.g., HTTPS). The input is the user's answer, and the output is a transmission completion message to the server.

[0082] Step 5:

[0083] The server analyzes the answer and generates the next question.

[0084] The server analyzes the user's answer and generates a question to gather the necessary information. For example, it generates a question like, "What was the cause of the information leak?" The input is the user's answer, and the output is the next question.

[0085] Step 6:

[0086] The user answers the following questions:

[0087] The user uses the terminal to answer the next question sent from the server. For example, the user might answer, "The cause was unauthorized access by an insider." The input is the next question from the server, and the output is the user's answer.

[0088] Step 7:

[0089] The terminal sends the user's answer to the server.

[0090] The terminal again sends the answer entered by the user to the server. This process is repeated until all the necessary information is collected. The input is the user's answer, and the output is a transmission completion message to the server.

[0091] Step 8:

[0092] The server analyzes all responses and evaluates the information security.

[0093] The server analyzes all collected responses and evaluates the current state of information security for the user. This evaluation uses an AI model to identify risks and propose countermeasures. The input is all user responses, and the output is the evaluation result.

[0094] Step 9:

[0095] The server provides feedback of the evaluation results to the user.

[0096] The server provides the evaluation results as feedback to the user, for example, providing specific advice such as "your current security measures are insufficient and additional measures are required." The input is the evaluation results, and the output is a feedback message to the user.

[0097] (Application example 1)

[0098] 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."

[0099] Conventional information security diagnostic systems were unable to fully grasp users' information security situations and concerns, making it difficult to propose appropriate security measures. Furthermore, when assessing security risks based on user responses, there was a lack of means to utilize generative AI models, resulting in low accuracy in risk assessments.

[0100] 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.

[0101] In this invention, the server includes an AI-based interactive hearing means, a risk assessment means based on the hearing content, a vulnerability assessment means, a means for proposing current risks and optimal security measures, a means for evaluating security risks using a generative AI model based on the user's answers and generating prompt sentences, and a means for proposing appropriate security measures based on the generated prompt sentences. This makes it possible to grasp the user's information security situation and concerns in detail, and to utilize the generative AI model to perform highly accurate security risk assessments and propose appropriate measures.

[0102] "AI-based interactive hearing method" is a method that uses artificial intelligence to interact with users and gather information about situations and concerns regarding information security.

[0103] "Risk diagnosis method based on interview content" is a method for evaluating information security risks based on information collected from users.

[0104] A "vulnerability assessment method" is a method for detecting and evaluating the vulnerabilities of a system or network.

[0105] "Means for proposing current risks and optimal security measures" refers to a means for analyzing the current risk situation and proposing optimal security measures to users.

[0106] "Means for assessing security risks using a generative AI model and generating prompt sentences" refers to means for utilizing a generative AI model to analyze user responses, assess security risks, and generate prompt sentences based on the results.

[0107] "Means for suggesting appropriate security measures based on generated prompt sentences" refers to means for suggesting appropriate security measures to users based on prompt sentences generated by a generative AI model.

[0108] The following system configuration is used as an embodiment of the present invention: The system is composed of three main elements: a server, a terminal, and a user.

[0109] The server includes an AI-based interactive hearing means, a risk assessment means based on the hearing content, a vulnerability assessment means, a means for proposing current risks and optimal security measures, a means for evaluating security risks using a generative AI model and generating prompt text, and a means for proposing appropriate security measures based on the generated prompt text.

[0110] The terminal is a device used by the user, such as a smartphone or PC, that communicates with the server to send and receive information. The user interacts with the server through the terminal and inputs the status and concerns regarding information security.

[0111] The server first gathers information from the user through an AI dialogue, which may include questions such as:

[0112] Please tell us about your current information security management situation.

[0113] Have there been any cases of data breaches in the past?

[0114] What security measures do you currently have in place?

[0115] The user's answers are sent from the terminal to the server, and the server performs risk diagnosis based on the answers. The risk diagnosis means analyzes the user's answers and evaluates the risk of information leakage and information incidents.

[0116] Next, the server uses vulnerability assessment tools to detect and evaluate system and network vulnerabilities, thereby clarifying the current risks.

[0117] The server then uses a generative AI model to further analyze the user's answers and assess their security risks. Based on this assessment, the server generates a prompt. For example, the following prompt might be generated:

[0118] Based on the information below, please assess the security risks and suggest appropriate countermeasures.

[0119] We have anti-virus software installed on all our computers.

[0120] Two years ago, an employee's password was leaked.

[0121] We encourage you to change your password regularly.

[0122] Based on the generated prompt, the server will suggest appropriate security measures, such as updating antivirus software, strengthening password management, and conducting regular security training.

[0123] In this way, users can receive specific and practical security measures. The server uses a generative AI model (e.g., OpenAI's GPT-3 (registered trademark)) to perform highly accurate risk assessment and propose countermeasures, thereby effectively improving users' information security.

[0124] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0125] Step 1:

[0126] The server collects information from the user in an AI-driven dialogue format. Specifically, the server sends the user questions such as, "Please tell us about the current status of information security management." The user answers these questions via their device, and the answers are sent to the server. The input is the user's answer, and the output is the collected information.

[0127] Step 2:

[0128] The server analyzes the user's answers using risk assessment means based on the interview content and evaluates information security risks. Specifically, the server analyzes the user's answers using natural language processing technology and identifies information leakage risks and information incident risks. The input is the user's answers, and the output is the risk assessment results.

[0129] Step 3:

[0130] The server uses vulnerability assessment tools to detect and assess system and network vulnerabilities. Specifically, the server compares the results with an existing vulnerability database to identify vulnerabilities that may exist in the user's system. The input is the user's system information, and the output is the vulnerability assessment results.

[0131] Step 4:

[0132] The server uses the generative AI model to further analyze the user's answers and evaluate security risks. Specifically, the server inputs the user's answers as prompts into the generative AI model to obtain risk assessment results. The inputs are the user's answers and the generative AI model, and the outputs are prompts and risk assessment results.

[0133] Step 5:

[0134] The server proposes appropriate security measures based on the generated prompt. Specifically, the server proposes specific security measures to the user based on the risk assessment results obtained from the generative AI model. Examples include updating antivirus software, strengthening password management, and conducting regular security training. The input is the prompt and the risk assessment results, and the output is proposed security measures.

[0135] Step 6:

[0136] The user receives the security measures proposed by the server and implements them. Specifically, the user checks the proposed measures through the terminal and implements the necessary measures. The input is the security measures proposed by the server, and the output is the implemented security measures.

[0137] In this way, servers, terminals, and users can work together to evaluate information security risks and propose and implement appropriate countermeasures.

[0138] Example 2

[0139] 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."

[0140] In conventional information security assessment systems, analysis of interview content, risk assessment, and vulnerability assessment are often performed manually, which is inefficient. In addition, the assessment results are often inaccurate and incomplete, making it difficult to propose optimal security measures.

[0141] 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.

[0142] In this invention, the server includes a means for a user to input the interview content, a means for the server to analyze the interview content, a means for the server to diagnose risks using a generative AI model, a means for the server to diagnose vulnerabilities using a vulnerability scanner, and a means for the server to provide the diagnosis results to the user. This enables efficient analysis of the interview content and highly accurate risk and vulnerability diagnosis, and makes it possible to propose optimal security measures.

[0143] The "means for the user to input the hearing contents" refers to the means by which the user inputs the hearing contents through the system interface.

[0144] The "means for the server to analyze the hearing content" refers to the means by which the server analyzes the hearing content sent from the user and extracts important keywords and phrases.

[0145] "Means for the server to diagnose risks using a generative AI model" refers to means for the server to use a generative AI model to diagnose information security risks based on the content of the interview.

[0146] "Means for a server to diagnose vulnerabilities using a vulnerability scanner" refers to means for a server to use a vulnerability scanner to diagnose vulnerabilities in a system or network.

[0147] The "means by which the server provides the diagnosis results to the user" refers to the means by which the server integrates the risk diagnosis results and the vulnerability diagnosis results and provides them to the user.

[0148] The present invention relates to an information security diagnostic system, in which a user inputs interview details, a server analyzes the details, performs risk diagnostics and vulnerability diagnostics, and provides the diagnostic results to the user.

[0149] System configuration

[0150] A means for users to input their hearing details

[0151] A user can use a web form or a dedicated application to describe in detail the current system status and concerns. For example, the user accesses a web form, enters information in the text boxes for "current system status" and "concerns," and clicks the "Submit" button.

[0152] The server's means of analyzing the hearing content

[0153] The server receives the audio recordings sent by the user and analyzes them using natural language processing (NLP) techniques, specifically by tokenizing the text using NLP libraries (e.g., spaCy or NLTK) and extracting important keywords and phrases.

[0154] How the server uses the generative AI model to diagnose risk

[0155] The server diagnoses risk using a generative AI model (e.g., OpenAI's GPT-4) based on the analyzed interview content. It generates prompt sentences and inputs them into the AI ​​model to obtain the results of risk diagnosis.

[0156] As a concrete example, consider the following situation where a user enters the following information:

[0157] "Our system is frequently accessed from outside, and our internal database contains confidential information. We are also concerned about whether employee access rights are being properly managed."

[0158] In this case, the server will input the following prompt into the generative AI model:

[0159] "Please assess information security risks based on the following interview content. Interview content: 'Our system is frequently accessed from outside, and our internal database contains confidential information. We are also concerned about whether employee access privileges are being properly managed.'"

[0160] The generative AI model diagnoses the risk of information leaks, cyber attacks, and internal fraud, and returns the results to the server.

[0161] How the server uses a vulnerability scanner to diagnose vulnerabilities

[0162] The server uses a vulnerability scanner (e.g., Nessus) to assess system and network vulnerabilities. The scanner scans the entire system and identifies potential vulnerabilities, such as out-of-date software or improper access privilege settings.

[0163] A means by which the server provides the diagnostic results to the user

[0164] The server integrates the results of risk assessment and vulnerability assessment and provides them to the user. The results are displayed in the form of a dashboard or report. The user can access the dashboard and check the assessment results.

[0165] In this way, it becomes possible to efficiently analyze the interview content and perform highly accurate risk and vulnerability diagnosis, thereby proposing optimal security measures.

[0166] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0167] Step 1:

[0168] The user inputs the details of the hearing.

[0169] Specifically, the user accesses a web form or dedicated application, enters information in the text boxes for "current system status" and "concerns," and clicks the "Submit" button.

[0170] Input: Hearing contents entered by the user (text data)

[0171] Output: Interview content (text data) sent to the server

[0172] Step 2:

[0173] The server analyzes the hearing content.

[0174] The server receives the audio recordings sent by the user and analyzes them using natural language processing (NLP) techniques, specifically by tokenizing the text using NLP libraries (e.g., spaCy or NLTK) and extracting important keywords and phrases.

[0175] Input: Interview details sent by the user (text data)

[0176] Output: Analyzed keywords and phrases (text data)

[0177] Step 3:

[0178] The server uses the generative AI model to diagnose risk.

[0179] The server diagnoses risk using a generative AI model (e.g., OpenAI's GPT-4) based on the analyzed interview content. The server generates prompt sentences and inputs them into the AI ​​model to obtain the results of risk diagnosis.

[0180] Specifically, the server generates a prompt sentence and inputs it into the generative AI model. It receives a response from the generative AI model and obtains the risk assessment result.

[0181] Input: Analyzed keywords and phrases (text data), generated prompt sentences

[0182] Output: Risk diagnosis results (text data)

[0183] Step 4:

[0184] The server uses a vulnerability scanner to check for vulnerabilities.

[0185] The server uses a vulnerability scanner (e.g., Nessus) to assess system and network vulnerabilities. The scanner scans the entire system and identifies potential vulnerabilities.

[0186] Specifically, the server launches a vulnerability scanner, provides information about the systems and networks to be scanned, receives the scan results, and generates a list of vulnerabilities.

[0187] Input: Information about the system or network you want to scan

[0188] Output: Vulnerability diagnosis results (text data)

[0189] Step 5:

[0190] The server provides the diagnostic results to the user.

[0191] The server integrates the results of risk assessment and vulnerability assessment and provides them to the user. The results are displayed in the form of a dashboard or report.

[0192] Specifically, the server integrates the risk assessment results and vulnerability assessment results and displays them in a user-friendly format (e.g., graphs and tables). Users can access the dashboard and check the assessment results.

[0193] Input: Risk assessment results, vulnerability assessment results

[0194] Output: Diagnostic results provided to the user (dashboard and report format)

[0195] (Application example 2)

[0196] 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."

[0197] In today's information society, information security risks are diverse and have become a serious issue for companies and individuals. In particular, risks such as information leaks, cyber attacks, and internal fraud can cause serious damage if appropriate measures are not taken. However, there are only a limited number of systems that can comprehensively diagnose these risks and propose appropriate countermeasures. In addition, there is a lack of functionality to identify specific vulnerabilities based on the diagnosis results and provide them as a report. This makes it difficult for users to grasp the full picture of risks and take appropriate countermeasures.

[0198] 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.

[0199] In this invention, the server includes an AI-based interactive hearing means, a risk diagnosis means based on the hearing content, a vulnerability diagnosis means, a means for proposing current risks and optimal security measures, a means for identifying system and network vulnerabilities based on the diagnosis results, and a means for generating a report of the risk diagnosis results and vulnerability diagnosis results, thereby enabling users to comprehensively diagnose information security risks, identify specific vulnerabilities, and take appropriate measures.

[0200] "AI-based interactive hearing method" is a method of collecting information from users in an interactive manner using artificial intelligence.

[0201] "Risk diagnosis method based on interview content" is a method for diagnosing information security risks based on collected interview content.

[0202] A "vulnerability assessment method" is a method for identifying vulnerabilities in a system or network.

[0203] "Means for proposing current risks and optimal security measures" refers to means for proposing optimal security measures for diagnosed risks.

[0204] "Means for identifying vulnerabilities in a system or network based on the results of the diagnosis" refers to means for identifying specific vulnerabilities in a system or network based on the results of the risk diagnosis.

[0205] The "means for generating a report of the risk diagnosis result and the vulnerability diagnosis result" is a means for outputting the risk diagnosis result and the vulnerability diagnosis result together in a report format.

[0206] To implement the present invention, the following system configuration and processing procedures are required.

[0207] System Configuration

[0208] 1. Hardware:

[0209] Smartphone: A device that allows users to listen.

[0210] Server: A central processing unit for performing risk and vulnerability assessments.

[0211] 2. Software:

[0212] Python: A programming language for implementing risk and vulnerability assessment logic.

[0213] JSON: A data format for outputting diagnostic results in report format.

[0214] Processing steps

[0215] 1. Hearing:

[0216] The smartphone application conducts an interactive interview with the user, who then inputs information about risks such as information leaks and cyber attacks.

[0217] 2. Risk Assessment:

[0218] The server diagnoses information security risks based on the interview results, specifically assessing the risk of information leaks, cyber attacks, and internal fraud.

[0219] 3. Vulnerability Assessment:

[0220] The server identifies system and network vulnerabilities based on the results of the risk assessment, such as insufficient data encryption or an unconfigured firewall.

[0221] 4. Proposed measures:

[0222] The server proposes the most appropriate security measures for the assessed risks and vulnerabilities, presenting multiple countermeasure options (e.g., basic, intermediate, advanced).

[0223] 5. Report Generation:

[0224] The server compiles the risk assessment results and vulnerability assessment results and outputs them in a report format. This report is generated in JSON format and provided to the user.

[0225] Specific examples

[0226] The user launches the smartphone application and inputs interview data about risks related to "information leakage" and "cyber attacks." Based on this, the server identifies vulnerabilities such as "insufficient data encryption" and "no firewall configured" and generates a report.

[0227] Prompt Sentence Examples

[0228] User: My system is at risk of data leakage and cyber attacks.

[0229] App: Information leakage risk identified. Insufficient data encryption.

[0230] App: Cyber ​​attack risk identified. Firewall not configured.

[0231] App: The results of the risk assessment and vulnerability assessment were generated as a report.

[0232] In this way, users can comprehensively diagnose information security risks, identify specific vulnerabilities, and take appropriate measures.

[0233] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0234] Step 1:

[0235] The user launches the smartphone application and begins the interview. The user enters information about risks such as information leaks and cyber attacks. The entered data is sent to the server as the interview content.

[0236] Input: Information about risks entered by users (e.g., information leaks, cyber attacks)

[0237] Output: The hearing is sent to the server

[0238] Step 2:

[0239] The server performs risk assessment based on the received interview content. Specifically, it evaluates the risk of information leakage, cyber attack risk, internal fraud risk, etc. The results of the risk assessment are saved as risk assessment results.

[0240] Input: Hearing details

[0241] Output: Risk diagnosis results

[0242] Step 3:

[0243] The server identifies system and network vulnerabilities based on the risk assessment results. For example, it identifies specific vulnerabilities such as insufficient data encryption or an unconfigured firewall. The vulnerability assessment results are saved as vulnerability assessment results.

[0244] Input: Risk diagnosis results

[0245] Output: Vulnerability assessment results

[0246] Step 4:

[0247] The server proposes optimal security measures for the diagnosed risks and vulnerabilities. It presents multiple countermeasure options (e.g., basic, intermediate, advanced). The countermeasure proposal results are saved as countermeasure proposal results.

[0248] Input: Risk assessment results, vulnerability assessment results

[0249] Output: Countermeasure proposal results

[0250] Step 5:

[0251] The server compiles the risk assessment results and vulnerability assessment results and outputs them in a report format. This report is generated in JSON format and provided to the user. The user can check the report through a smartphone application.

[0252] Input: Risk assessment results, vulnerability assessment results, countermeasure proposal results

[0253] Output: Report (JSON format)

[0254] In this way, users can comprehensively diagnose information security risks, identify specific vulnerabilities, and take appropriate measures.

[0255] Example 3

[0256] 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."

[0257] In today's information security environment, companies and organizations face a variety of risks and vulnerabilities. It is extremely important to properly assess these risks and propose optimal security measures. However, current systems face the challenge of effectively analyzing the results of risk and vulnerability assessments and presenting measures in a format that is easy for users to understand. In particular, there is a lack of support for users to select appropriate measures based on their own situation.

[0258] 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.

[0259] In this invention, the server includes an AI-based interactive hearing means, a risk assessment means based on the hearing content, a vulnerability assessment means, a means for storing the assessment results in a database, a means for analyzing the stored assessment results, a means for proposing current risks and optimal security measures, and a means for presenting the proposed measures in a pine, bamboo, and plum format. This allows the user to accurately grasp the current risks based on the assessment results and select the optimal security measures according to their own situation.

[0260] "AI-based interactive hearing means" is a means of using artificial intelligence to converse with users and collect necessary information.

[0261] "Risk diagnosis method based on interview content" is a method for evaluating the risk of a system or network based on collected information.

[0262] A "vulnerability assessment method" is a method for detecting and evaluating the vulnerabilities of a system or network.

[0263] The "means for storing the diagnosis results in a database" refers to a means for storing the results of risk diagnosis and vulnerability diagnosis in a database.

[0264] The "means for analyzing stored diagnostic results" is a means for analyzing the diagnostic results stored in the database and evaluating the current risk.

[0265] "Means for proposing current risks and optimal security measures" refers to a means for generating and proposing optimal security measures based on the analysis results.

[0266] The "means for presenting proposed measures in the form of a 'pine, bamboo, plum' system" is a means for presenting generated security measures in the form of a 'pine, bamboo, plum' system so that the user can easily select one.

[0267] MODE FOR CARRYING OUT THE INVENTION

[0268] This invention relates to an information security diagnostic system, and in particular to a system that proposes optimal security measures based on the results of risk and vulnerability diagnostics. This system operates in cooperation with three parties: a server, a terminal, and a user.

[0269] 1. Program Generation

[0270] The server generates a program that proposes optimal security measures based on the results of risk and vulnerability assessments. This program is developed using programming languages ​​such as Python and Java (registered trademark).

[0271] 2. Program processing explanation

[0272] The server collects the results of risk and vulnerability assessments and stores them in a database. The server then analyzes the stored data and evaluates the current risk. This evaluation uses machine learning models (such as Scikit-learn and TENSORFLOW (registered trademark)).

[0273] Based on the evaluation results, the server proposes optimal security measures, such as installing a firewall, reviewing password policies, and providing security training to employees. The proposed measures are presented in a "pine, bamboo, and plum" format, allowing users to select the one that best suits their situation.

[0274] 3. Examples of concrete examples and prompts

[0275] For example, suppose a company conducts a risk and vulnerability assessment and finds the following risks:

[0276] High risk of external attacks on internal networks

[0277] Weak password

[0278] Employees have low security awareness

[0279] In this case, the server will suggest the following security measures:

[0280] Matsu: Implementing a high-performance firewall, setting strong password policies, and providing security training to all employees.

[0281] Take: Implementing a mid-range firewall, reviewing password policies, and providing security training to key employees.

[0282] Ume: Implementing basic firewalls, simple review of password policies, and distributing security awareness materials.

[0283] Example prompts to input to a generative AI model:

[0284] Please suggest optimal security measures based on the results of the company's risk and vulnerability assessment. The following risks have been identified:

[0285] High risk of external attacks on internal networks

[0286] Weak password

[0287] Employees have low security awareness

[0288] Please make your proposal in the form of pine, bamboo, and plum."

[0289] In this way, the user can select the most suitable security measure according to his / her own situation. The flow of the identification process in the third embodiment will be described with reference to FIG.

[0290] Step 1:

[0291] The server uses AI to conduct dialogue with the user. As input, it receives information provided by the user (such as the system configuration, software used, and security policies). As output, it generates data for risk and vulnerability assessment based on the interview content. Specifically, the server uses natural language processing technology to analyze the user's responses and extract the necessary information.

[0292] Step 2:

[0293] The server performs risk diagnosis based on the interview content. It uses the data obtained in step 1 as input. It generates risk assessment results as output. Specifically, the server uses the risk assessment model to assess the risk of information leakage and information incidents.

[0294] Step 3:

[0295] The server performs vulnerability assessment. It uses the data obtained in step 1 as input and generates vulnerability assessment results as output. Specifically, the server uses a network scanning tool (e.g., Nessus or OpenVAS) to detect system vulnerabilities.

[0296] Step 4:

[0297] The server saves the results of the risk assessment and vulnerability assessment in a database. As input, it uses the assessment results obtained in steps 2 and 3. As output, it generates the assessment results stored in the database. Specifically, the server obtains the assessment results in JSON format and inserts them into the database.

[0298] Step 5:

[0299] The server analyzes the stored diagnostic results. As input, it uses the diagnostic results stored in the database. As output, it generates a current risk assessment result. Specifically, the server uses a machine learning model (e.g., Scikit-learn or TensorFlow) to score the severity of the risk.

[0300] Step 6:

[0301] The server proposes optimal security measures based on the analysis results. The risk assessment results obtained in step 5 are used as input. Security measure proposals are generated as output. Specifically, the server prioritizes measures based on the risk score and generates specific measures such as installing a firewall, reviewing password policies, and providing security training.

[0302] Step 7:

[0303] The terminal presents the generated security measures to the user. As input, it uses the security measure proposals generated in step 6. As output, it generates the security measures selected by the user. Specifically, the terminal displays the proposed measures in the form of a pine, bamboo, and plum tree through the user interface and sends the user's selection to the server.

[0304] Step 8:

[0305] The user selects the most appropriate security measure from the presented security measures. As input, the security measures presented in step 7 are used. As output, the selected security measure is sent to the server. In concrete terms, the user operates the terminal interface to select the most appropriate measure.

[0306] (Application example 3)

[0307] 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."

[0308] In today's information society, personal and corporate devices and networks are exposed to a variety of risks. The risks of information leaks and information incidents are particularly serious, making it necessary to properly diagnose these risks and propose optimal security measures. However, current security assessment systems lack specific assessments based on the user's device information and comprehensive recommendations based on the assessment results. This makes it difficult for users to select the optimal security measures for their own situation.

[0309] 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 an AI-based interactive hearing means, a risk assessment means based on the hearing content, a vulnerability assessment means, a means for proposing current risks and optimal security measures, a means for performing risk and vulnerability assessment based on the user's device information, a means for proposing security measures in the form of a pine, bamboo, and plum system based on the assessment results, and a means for displaying the proposed security measures to the user. This enables the user to specifically assess the risks of their own device and network and select optimal security measures.

[0310] "AI-based interactive hearing method" is a method that uses artificial intelligence to interact with users and collect necessary information.

[0311] "Risk diagnosis method based on interview content" is a method for evaluating potential risks based on collected information.

[0312] A "vulnerability assessment tool" is a tool for identifying and assessing vulnerabilities in systems and devices.

[0313] "Means for proposing current risks and optimal security measures" refers to a means for analyzing the current risk situation and proposing optimal security measures for that situation.

[0314] "Means for diagnosing risks and vulnerabilities based on user device information" refers to means for diagnosing risks and vulnerabilities using information about a user's device.

[0315] "Means for proposing security measures in the form of a 'Sho, Take, Plum' hierarchy based on diagnostic results" refers to means for proposing security measures of different levels in the form of a 'Sho, Take, Plum' hierarchy based on diagnostic results.

[0316] The "means for displaying the proposed security measures to the user" refers to a means for visually displaying the proposed security measures to the user.

[0317] A system for carrying out this invention has the following configuration: The server includes means for conducting an interactive interview using AI, means for diagnosing risks and vulnerabilities based on the interview content, means for proposing current risks and optimal security measures, means for diagnosing risks and vulnerabilities based on the user's device information, means for proposing security measures in the form of a pine, bamboo, and plum system based on the results of the assessment, and means for displaying the proposed security measures to the user.

[0318] Program processing explanation

[0319] The server first collects necessary information from the user through an AI-based dialogue. This includes the user's device information (OS, version, installed apps, etc.). The collected information is then analyzed by risk assessment and vulnerability assessment methods. The risk assessment method evaluates the risk of information leakage and information incidents, while the vulnerability assessment method identifies vulnerabilities in the system and device.

[0320] Next, the server proposes the current risks and optimal security measures based on the results of the diagnosis. The proposed measures are presented in the form of a "pine, bamboo, and plum" system, allowing users to select the measures that best suit their own situation. This allows users to specifically diagnose the risks to their devices and networks and select the optimal security measures.

[0321] Hardware and software used

[0322] Hardware: Server, user's smartphone or computer

[0323] Software: AI models (e.g., natural language processing models), APIs for risk and vulnerability assessment, and applications that provide user interfaces

[0324] Specific examples

[0325] For example, if a user is using ANDROID (registered trademark) 10 and the installed apps are "App1", "App2", and "App3", the server operates as follows:

[0326] 1. Collect user device information.

[0327] 2. Use risk assessment tools and vulnerability assessment tools to assess information leakage risks and information incident risks and identify device vulnerabilities.

[0328] 3. Based on the diagnostic results, we propose security measures in the form of a 3-pronged approach.

[0329] 4. Display the proposed security measures to the user.

[0330] Prompt Sentence Examples

[0331] An example of a prompt sentence to input to the generative AI model is as follows:

[0332] Please create a program that performs risk and vulnerability assessments based on the user's device information and proposes optimal security measures. The proposed measures should be presented in a "pine, bamboo, and plum" format. Please use the following device information:

[0333] OS: Android

[0334] Version: 10

[0335] Installed Apps: ["App1", "App2", "App3"]

[0336] In this way, the detailed description provides specific measures for enhancing the security of users' devices and networks.

[0337] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0338] Step 1:

[0339] The server collects necessary information from the user through an AI-based dialogue. Specifically, it receives the user's device information (OS, version, installed apps, etc.) as input and stores it in a database. The input information serves as the basis for subsequent risk and vulnerability assessments.

[0340] Step 2:

[0341] The server analyzes the collected device information using a risk assessment method based on the interview content. Specifically, it receives the device information as input and applies a risk assessment algorithm to evaluate the risk of information leakage and information incidents. The output is a risk level (e.g., low, medium, or high).

[0342] Step 3:

[0343] The server uses a vulnerability assessment tool to identify device vulnerabilities. Specifically, it receives device information as input and compares it with a database of known vulnerabilities. The output is a list of identified vulnerabilities and their severity.

[0344] Step 4:

[0345] The server proposes the current risk and optimal security measures based on the results of risk and vulnerability assessment. Specifically, it receives the risk level and vulnerability list as input and applies a security measure proposal algorithm. The output is a list of proposed security measures in the form of a "pine, bamboo, and plum" system.

[0346] Step 5:

[0347] The server proposes security measures based on the diagnosis results in a "Sho-Take-Ume" format. Specifically, it receives a list of proposed security measures as input and generates data to visually display to the user. As an output, it obtains a selection of security measures that are displayed in the user interface.

[0348] Step 6:

[0349] The user checks the proposed security measures and selects the most appropriate one based on their own situation. Specifically, the user receives the security measure options provided by the server as input and sends the selected measure to the server. As output, the selected security measure is recorded on the server.

[0350] Step 7:

[0351] The server provides specific procedures for implementing the security measures selected by the user. Specifically, it receives the selected security measures as input and generates the execution procedures. As an output, it obtains a list of execution procedures to be provided to the user.

[0352] In this way, the server specifically diagnoses the risks to the user's device and network and assists in selecting and implementing the most appropriate security measures.

[0353] Furthermore, an emotion engine that estimates the user's emotion may be combined.

[0354] The logic unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0355] "Example 1"

[0356] One embodiment of the present invention is an information security diagnostic system that incorporates an emotion engine. This system diagnoses information security risks through dialogue with the user and proposes optimal security measures. In particular, the emotion engine recognizes the user's emotions and adjusts the risk diagnosis and proposed measures based on those emotions. For example, if the user is feeling anxious, the emotion engine recognizes that anxiety and reduces the user's anxiety by providing a more detailed explanation and proposing specific measures. Also, if the user is feeling angry, the emotion engine recognizes that anger, identifies the cause of the problem, and proposes a solution. In this way, the information security diagnostic system that incorporates an emotion engine can provide appropriate measures that correspond to the user's emotions.

[0357] "Example 2"

[0358] Another embodiment of the present invention is an information security diagnostic system that incorporates an emotion engine. This system diagnoses information security risks through dialogue with the user and proposes optimal security measures. In particular, the emotion engine recognizes the user's emotions and adjusts the risk diagnosis and proposed measures based on those emotions. For example, if the user is feeling happy, the emotion engine recognizes that joy and proposes measures that will allow the user to use the system with peace of mind. Also, if the user is feeling surprised, the emotion engine recognizes that surprise and proposes measures to address unexpected risks. In this way, the information security diagnostic system that incorporates an emotion engine can provide appropriate measures that correspond to the user's emotions.

[0359] "Example 3"

[0360] A further embodiment of the present invention is an information security diagnostic system that incorporates an emotion engine. This system diagnoses information security risks through dialogue with the user and proposes optimal security measures. In particular, the emotion engine recognizes the user's emotions and adjusts the risk diagnosis and proposed measures based on those emotions. For example, if the user is feeling sad, the emotion engine recognizes that sadness and proposes measures that take the user's feelings into consideration. Also, if the user is excited, the emotion engine recognizes that excitement and proposes measures that allow the user to use the system safely. In this way, the information security diagnostic system that incorporates an emotion engine can provide appropriate measures that correspond to the user's emotions.

[0361] The processing flow of each embodiment will be described below.

[0362] "Example 1"

[0363] Step 1: Start a dialogue with the user. This dialogue is for information security risk assessment.

[0364] Step 2: The emotion engine recognizes the user's emotions, which are inferred from the user's statements and actions.

[0365] Step 3: The emotion engine adjusts the risk assessment results and suggested measures based on the emotions it recognizes. For example, if the user feels anxious, it will suggest more detailed explanations and specific measures.

[0366] "Example 2"

[0367] Step 1: Start a dialogue with the user. This dialogue is for information security risk assessment.

[0368] Step 2: The emotion engine recognizes the user's emotions, which are inferred from the user's statements and actions.

[0369] Step 3: The emotion engine adjusts the risk assessment results and proposed measures based on the emotions it recognizes. For example, if the user is feeling happy, it will propose measures that will allow the user to use the system with peace of mind.

[0370] "Example 3"

[0371] Step 1: Start a dialogue with the user. This dialogue is for information security risk assessment.

[0372] Step 2: The emotion engine recognizes the user's emotions, which are inferred from the user's statements and actions.

[0373] Step 3: Based on the emotions recognized by the emotion engine, the risk assessment results and suggested measures are adjusted. For example, if the user is feeling sad, the system will suggest measures that take into account the user's feelings.

[0374] Example 1

[0375] 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."

[0376] Conventional information security diagnostic systems diagnose risks and propose countermeasures without considering the user's feelings, which means that they are unable to fully alleviate users' anxiety and anger. Another issue is that the countermeasure proposals are uniform, making it difficult to respond flexibly to the user's specific situation.

[0377] 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.

[0378] In this invention, the server includes an AI-based interactive hearing means, a risk diagnosis means based on the hearing content, an emotion recognition means, a means for proposing current risks and optimal security measures, and a means for adjusting the proposed measures based on the user's emotions, thereby enabling risk diagnosis and proposed measures that take the user's emotions into consideration.

[0379] "AI-based interactive hearing method" is a method that uses artificial intelligence to interact with users and gather information about situations and concerns regarding information security.

[0380] "Risk diagnosis method based on interview content" is a method for evaluating and diagnosing information security risks based on information collected from users.

[0381] The "emotion recognition means" is a means for analyzing and recognizing emotions from user input and dialogue content.

[0382] The "means for proposing current risks and optimal security measures" is a means for proposing optimal security measures to users for diagnosed risks.

[0383] The "means for adjusting the proposed measures based on the user's emotions" is a means for adjusting the content and method of the proposed measures in accordance with the recognized emotions of the user.

[0384] "Information leakage risk" refers to the risk that confidential information may be leaked to the outside.

[0385] "Information incident risk" refers to the risk that a security incident may occur in an information system.

[0386] "Providing multiple options" means presenting multiple different options for security measures to the user.

[0387] The information security diagnostic system of this invention includes an AI-based interactive hearing means, a risk diagnosis means based on the hearing content, an emotion recognition means, a means for proposing current risks and optimal security measures, and a means for adjusting the proposed measures based on the user's emotions.

[0388] Hardware and software used

[0389] Hardware: Servers, terminals (PCs, smartphones)

[0390] Software: Generative AI models, emotion engines, conversational interfaces

[0391] Program processing explanation

[0392] Start of user interaction

[0393] The user uses the interactive interface of the terminal to input questions or concerns about information security. For example, they might input, "Recently, there has been an information leak within our company. Please tell me what measures we should take."

[0394] Sending input data

[0395] The terminal receives the user's input and sends it to a server over the Internet, where the input data is encrypted using the SSL / TLS protocol.

[0396] Analyzing input data

[0397] The server receives the user's input and passes it to the generative AI model for analysis. The generative AI model understands the user's questions and concerns and extracts the necessary information.

[0398] Emotion recognition

[0399] The server uses an emotion engine to recognize emotions from the user's input, for example, if the user is feeling "anxious" or "angry," it identifies that emotion.

[0400] Risk diagnosis and countermeasure proposals

[0401] The server uses the generative AI model to diagnose the user's information security risk. Based on the results of the emotion engine, it generates countermeasures that correspond to the user's emotions. For example, for a user who feels anxious, it proposes detailed explanations and specific countermeasures.

[0402] Sending diagnostic results and proposed solutions

[0403] The server generates an encrypted version of the diagnostic results and proposes countermeasures, and sends them to the device. For example, it sends a message such as, "The cause of the information leak is inadequate access control. Please follow the steps below to strengthen access control."

[0404] Displaying the results

[0405] The device displays the diagnosis results and proposed solutions to the user, who can then check and implement the specific solutions displayed on the screen.

[0406] Specific examples

[0407] If a user types, "Recently, there has been an information leak within our company. Please tell me what measures we should take," the system will behave as follows:

[0408] 1. The terminal receives the user's input and sends it to the server.

[0409] 2. The server uses a generative AI model to analyze the input and obtain details of the data breach.

[0410] 3. The emotion engine recognizes the user's concerns and suggests detailed explanations and specific solutions.

[0411] 4. The server sends the diagnosis results and proposed solutions to the device, which then displays the results to the user.

[0412] In this way, the information security diagnostic system of the present invention diagnoses information security risks through dialogue with the user, and provides appropriate measures corresponding to the user's emotions using an emotion engine.

[0413] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0414] Step 1:

[0415] The user provides input through a dialogue interface.

[0416] The user types into the interactive interface of the terminal, "Recently, there has been an information leak within the company. Please tell me what measures should be taken."

[0417] Input: User's question or concern

[0418] Output: User input data

[0419] Step 2:

[0420] The terminal sends the user's input to the server.

[0421] The terminal receives the user's input, encrypts it using the SSL / TLS protocol, and sends it to the server.

[0422] Input: User-entered data

[0423] Output: The encrypted input data

[0424] Step 3:

[0425] The server parses the input using a generative AI model.

[0426] The server passes the received input data to the generative AI model, which analyzes it using natural language processing technology. For example, it extracts keywords such as "information leak" and "countermeasures" from the input sentence.

[0427] Input: Encrypted input data

[0428] Output: Parsed information (keywords, etc.)

[0429] Step 4:

[0430] The server uses an emotion engine to recognize the user's emotion.

[0431] The server uses an emotion engine to recognize that the user is feeling "anxiety" from the input sentence. The emotion engine identifies emotions from the context of the input sentence and the choice of words.

[0432] Input: Analyzed information (keywords, etc.)

[0433] Output: Recognized emotion information

[0434] Step 5:

[0435] The server performs risk assessment and generates countermeasures.

[0436] The server uses the generative AI model to diagnose the user's information security risk. Based on the results of the emotion engine, it generates countermeasures that correspond to the user's emotions. For example, for a user who feels anxious, it provides a detailed explanation and suggests specific countermeasures.

[0437] Input: Recognized emotion information, analyzed information (keywords, etc.)

[0438] Output: Diagnostic results and proposed solutions

[0439] Step 6:

[0440] The server sends the diagnosis results and suggested solutions to the terminal.

[0441] The server generates an encrypted version of the diagnostic results and proposed solutions, which are then sent to the device. For example, the server sends a message such as, "The cause of the information leak is inadequate access control. Please follow the steps below to strengthen access control."

[0442] Input: Diagnostic results and proposed solutions

[0443] Output: Encrypted diagnostic results and suggested solutions

[0444] Step 7:

[0445] The terminal displays the results to the user.

[0446] The device displays the diagnosis results and proposed solutions to the user, who can then check and implement the specific solutions displayed on the screen.

[0447] Input: Encrypted diagnostic results and suggested solutions

[0448] Output: Diagnostic results and suggested solutions displayed to the user

[0449] (Application example 1)

[0450] 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."

[0451] Conventional information security diagnostic systems perform risk assessments and propose countermeasures without taking user emotions into consideration, which means they are unable to respond appropriately to user emotions such as anxiety and anger. Furthermore, because they are unable to propose flexible countermeasures based on user emotions, user satisfaction may decline. This could result in a decrease in the effectiveness of information security measures.

[0452] 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.

[0453] In this invention, the server includes an AI-based interactive hearing means, a risk diagnosis means based on the hearing content, a vulnerability diagnosis means, an emotion engine means for recognizing the user's emotions, a means for adjusting the risk diagnosis and countermeasure proposals based on the emotions, and a means for proposing current risks and optimal security countermeasures. This enables flexible risk diagnosis and countermeasure proposals according to the user's emotions.

[0454] "AI-based interactive hearing method" is a method that uses artificial intelligence to interact with users and gather information about situations and concerns regarding information security.

[0455] "Risk diagnosis method based on interview content" is a method for evaluating and diagnosing information security risks based on collected interview content.

[0456] A "vulnerability assessment method" is a method for detecting and evaluating the vulnerabilities of a system or network.

[0457] The "emotion engine means for recognizing user emotions" is an engine for analyzing and recognizing emotions from the user's statements and actions.

[0458] The "means for adjusting risk diagnosis and proposed countermeasures based on emotions" refers to a means for adjusting risk diagnosis and proposed security countermeasures according to the recognized emotions of the user.

[0459] "Means for proposing current risks and optimal security measures" refers to a means for assessing the current risk situation and proposing optimal security measures to users based on that assessment.

[0460] As an embodiment of the present invention, an information security diagnostic system operates in cooperation with three entities: a server, a terminal, and a user.

[0461] System Program

[0462] The server runs programs that implement the following main functions:

[0463] 1. Interactive hearing methods

[0464] The server uses artificial intelligence (AI) to interact with users and gather information about their information security situation and concerns. Specifically, it analyzes information entered by users via their smartphones or computer terminals and asks questions in an interactive format.

[0465] 2. Risk diagnostic tools

[0466] The server evaluates and diagnoses information security risks based on the collected interview data, including information leakage risks and information incident risks.

[0467] 3. Vulnerability Assessment Methods

[0468] The server detects and assesses system and network vulnerabilities, thereby identifying potential security holes.

[0469] 4. Emotional Engine Means

[0470] The server uses an emotion engine to analyze and recognize emotions from the user's words and actions, thereby determining whether the user is feeling anxious or angry.

[0471] 5. Emotion-based regulation

[0472] The server then adjusts the risk assessment and security measures it proposes based on the user's emotions. For example, if the user feels anxious, it will provide more detailed explanations and suggest specific measures.

[0473] 6. Security Measure Proposal Method

[0474] The server evaluates the current risk situation and proposes optimal security measures to the user based on that assessment. This proposal can provide multiple options, like a "pine, bamboo, plum" system.

[0475] Hardware and software used

[0476] Hardware: Smartphones, computer terminals, servers

[0477] Software: Python, OpenAI API, Transformers library

[0478] Data processing and calculation

[0479] 1. Getting User Input

[0480] The terminal takes input from the user and sends it to the server.

[0481] 2. Emotion recognition

[0482] The server uses an emotion engine to analyze user input and recognize emotions, using the Transformers library.

[0483] 3. Risk diagnosis and generation of countermeasure proposals

[0484] The server uses OpenAI's generative AI model to generate security measures based on user input and sentiment.

[0485] Specific examples

[0486] For example, if a user types, "I'm worried because I've been receiving a lot of phishing emails lately," the server analyzes this input and recognizes the emotion "anxiety." It then uses a generative AI model to suggest the following security measures:

[0487] Example prompt sentence:

[0488] User input: I'm worried because I've been getting a lot of phishing emails lately.

[0489] Emotion: Anxiety

[0490] Please suggest appropriate security measures.

[0491] In this way, it becomes possible to flexibly diagnose risks and propose countermeasures according to the user's emotions.

[0492] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0493] Step 1:

[0494] A user uses a terminal to input information security concerns. The input data is sent from the terminal to a server. The input data includes the user's concerns and questions.

[0495] Step 2:

[0496] The server analyzes the received user input data and generates additional interactive questions to gain a more detailed understanding of the user's information security situation. The generated questions are sent to the terminal and displayed to the user.

[0497] Step 3:

[0498] The user answers questions posed by the server through the terminal, and the answer data is sent back to the server from the terminal, allowing the server to collect detailed information about the user's information security.

[0499] Step 4:

[0500] The server analyzes the user's emotions using an emotion engine based on the collected user input data and response data. The emotion engine uses the Transformers library to recognize emotions from the user's text data. The input data is the user's text, and the output data is the recognized emotion.

[0501] Step 5:

[0502] The server performs risk diagnosis based on the recognized emotion. The risk diagnosis means evaluates the risk of information leakage and information incidents based on the user's input data and emotion data. The input data is the user's text and emotion, and the output data is the risk assessment result.

[0503] Step 6:

[0504] The server uses a generative AI model to propose optimal security measures based on the risk assessment results and the user's emotions. The generative AI model uses the OpenAI API to generate specific measures based on the user's situation. The input data is the risk assessment results and emotions, and the output data is the security measure proposals.

[0505] Step 7:

[0506] The server sends the generated security measure proposals to the terminal and displays them to the user. The user can check the proposed measures and implement them as necessary. This allows the user to take concrete actions to strengthen their own information security.

[0507] Example 2

[0508] 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."

[0509] Conventional information security diagnostic systems perform risk assessments and propose countermeasures without considering the user's feelings, which can make it difficult for users to use the system with confidence. Furthermore, risk and vulnerability assessments are sometimes insufficient, which can lead to inadequate security measures. This increases the risk of information leaks and cyber attacks.

[0510] 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.

[0511] In this invention, the server includes an AI-based interactive hearing means, a risk assessment means based on the hearing content, a vulnerability assessment means, an emotion engine means for analyzing the user's emotions, and a means for proposing current risks and optimal security measures. This enables flexible risk assessment and countermeasure proposals based on the user's emotions, allowing users to use the system with peace of mind. Furthermore, by performing comprehensive risk and vulnerability assessments, security measures can be strengthened and the risk of information leaks and cyber attacks can be reduced.

[0512] "AI-based interactive hearing means" refers to a means of using artificial intelligence to converse with users and gather information about their needs and concerns.

[0513] "Risk diagnosis method based on interview content" is a method for evaluating and diagnosing risks related to information security based on interview information collected from users.

[0514] A "vulnerability assessment method" is a method for identifying vulnerabilities in a system or network and evaluating them against known vulnerability information.

[0515] The "emotion engine means for analyzing user emotions" is a means for analyzing emotions from the user's input contents and dialogue, and adjusting risk diagnosis and countermeasure proposals based on the emotions.

[0516] The "means for proposing current risks and optimal security measures" is a means for proposing optimal security measures to users based on the results of risk diagnosis and the results of user sentiment analysis.

[0517] The present invention relates to an information security diagnostic system, and in particular to a system that performs risk diagnosis and proposes countermeasures while taking into account the user's emotions. A specific embodiment of this system will be described below.

[0518] First, the user accesses the system interface using a terminal and enters the information they have gathered interactively. For example, they might say, "I would like to install new software, but I'm worried about security risks."

[0519] The server receives the hearing information sent by the user and begins risk assessment. This risk assessment uses vulnerability scanners such as Nessus and OpenVAS. The server uses these tools to scan system and network vulnerabilities and evaluate the risk of information leakage and cyber attacks.

[0520] The server then performs a vulnerability scan, looking up known vulnerabilities in the CVE database and comparing them with the scan results. For example, if a particular port is open, it checks for vulnerabilities associated with that port.

[0521] Furthermore, the server uses an emotion engine to analyze the user's emotions. The emotion engine analyzes emotions from the user's input and adjusts risk diagnosis and countermeasure suggestions based on those emotions. For example, if a user uses the keyword "worry," the emotion engine recognizes that the user is feeling anxious.

[0522] Finally, the server will suggest the most appropriate security measures based on the user's feelings along with the risk assessment results, such as "When installing new software, we recommend strengthening firewall settings and conducting regular vulnerability scans."

[0523] Examples of prompts include:

[0524] "I want to install new software, but I'm worried about security risks. What measures should I take?"

[0525] In this way, the system diagnoses risks based on user input and proposes appropriate countermeasures. By combining it with an emotion engine, it is possible to respond flexibly to the user's emotions, allowing users to use the system with peace of mind.

[0526] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0527] Step 1:

[0528] The user enters the hearing information.

[0529] The user accesses the system interface using a terminal and inputs information such as, "I want to install new software, but I'm worried about security risks." The input information is sent to the server.

[0530] Step 2:

[0531] The server receives the hearing information and starts risk diagnosis.

[0532] The server receives the information sent by the user. Based on the received information, it assesses the risk of information leakage, cyber-attacks, and internal fraud. This process uses vulnerability scanners such as Nessus and OpenVAS. The input is the information sent by the user, and the output is the risk assessment results.

[0533] Step 3:

[0534] The server performs vulnerability assessment.

[0535] The server performs vulnerability assessment to identify system and network vulnerabilities. Specifically, it references the CVE database and compares the scan results to identify known vulnerabilities. For example, if a specific port is open, it checks for vulnerabilities associated with that port. The input is the scan results, and the output is the vulnerability assessment results.

[0536] Step 4:

[0537] The server uses an emotion engine to analyze the user's emotions.

[0538] The server uses an emotion engine to analyze emotions from user input. For example, if a user inputs "I want to install new software, but I'm worried about security risks," the emotion engine recognizes that the user is feeling anxious. The input is the user's hearing information, and the output is the emotion analysis result.

[0539] Step 5:

[0540] The server presents the risk assessment results and countermeasures to the user.

[0541] The server presents the risk assessment results along with countermeasures according to the user's emotions. For example, if the user feels anxious, the server will suggest reassuring countermeasures such as strengthening firewall settings or conducting regular vulnerability scans. The inputs are the risk assessment results and the emotion analysis results, and the output is countermeasure suggestions.

[0542] (Application example 2)

[0543] 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."

[0544] Conventional information security diagnostic systems diagnose risks and propose countermeasures without considering the user's feelings, which can lead to users feeling anxious or dissatisfied with the proposed countermeasures. Furthermore, the accuracy of risk and vulnerability diagnostics is low, making it difficult to provide optimal security measures.

[0545] 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.

[0546] In this invention, the server includes an AI-based interactive hearing means, a risk diagnosis means based on the hearing content, a vulnerability diagnosis means, an emotion engine means for recognizing the user's emotions, a means for adjusting the risk diagnosis and countermeasure proposals based on the user's emotions, and a means for proposing current risks and optimal security countermeasures. This makes it possible to perform appropriate risk diagnosis and countermeasure proposals according to the user's emotions.

[0547] "AI-based interactive hearing method" is a method that uses artificial intelligence to interact with users and collect information.

[0548] "Risk diagnosis means based on interview content" is a means of analyzing collected interview content and diagnosing risks related to information security.

[0549] A "vulnerability assessment method" is a method for identifying and assessing vulnerabilities in a system or network.

[0550] The "emotion engine means for recognizing user's emotions" is a means using an engine for analyzing and recognizing user's emotions.

[0551] "Means for adjusting risk diagnosis and proposed measures based on user emotions" refers to means for adjusting risk diagnosis and proposed security measures based on the user's recognized emotions.

[0552] "Means for proposing current risks and optimal security measures" refers to means for proposing optimal security measures to users in response to diagnosed risks.

[0553] As an embodiment of the present invention, an information security diagnostic system is configured as follows.

[0554] System configuration

[0555] Hardware

[0556] Server: Performs the central processing of the information security diagnostic system.

[0557] Smartphone: A device that allows users to conduct interactive hearings.

[0558] Network: Infrastructure for communication between the server and smartphones.

[0559] software

[0560] emotion_recognition library: Software for recognizing user emotions.

[0561] risk_assessment library: Software for diagnosing risks based on interview content.

[0562] vulnerability_scanner library: Software for scanning systems and networks for vulnerabilities.

[0563] Program processing

[0564] Interviews and risk assessments

[0565] The server receives the interview content from the user via smartphone, which is then analyzed by a risk assessment tool to determine the risk of information leakage and information incidents.

[0566] emotion recognition

[0567] The server uses the emotion_recognition library to recognize the user's emotions. For example, if a user types, "I feel like there have been an increasing number of phishing emails lately," the server will recognize the user's emotion as "surprise" based on the content.

[0568] Vulnerability Assessment

[0569] The server uses the vulnerability_scanner library to scan and identify system and network vulnerabilities.

[0570] Countermeasure proposals

[0571] The server adjusts the results of risk and vulnerability assessments based on the user's emotions and suggests optimal security measures. For example, if the user's emotion is "surprise," the server will emphasize and suggest measures against unexpected risks.

[0572] Specific examples

[0573] If a user enters "I feel like there have been an increasing number of phishing emails lately," the server will process the following:

[0574] 1. Receive hearing: Receive user input.

[0575] 2. Emotion Recognition: Use the emotion_recognition library to recognize the user's emotion as "surprise."

[0576] 3. Risk assessment: Use the risk_assessment library to assess the risk of phishing emails.

[0577] 4. Vulnerability Assessment: Scan for email filtering vulnerabilities using the vulnerability_scanner library.

[0578] 5. Propose countermeasures: Based on the perceived emotions, propose countermeasures for unexpected risks.

[0579] Prompt Sentence Examples

[0580] User Input: I feel like there's been an increase in phishing emails lately.

[0581] Emotion Recognition: Surprise

[0582] Risk Assessment: Risk of Phishing Emails

[0583] Vulnerability Assessment: Email Filtering Vulnerabilities

[0584] Countermeasure proposals: Countermeasures against unexpected risks: Risks of phishing emails, vulnerabilities in email filtering

[0585] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0586] Step 1:

[0587] A user launches an information security assessment application on their smartphone. The user inputs security concerns and questions through the application's interactive interface. The input data is then sent to the server.

[0588] Input: User security concerns or questions

[0589] Output: User input data sent to the server

[0590] Step 2:

[0591] The server analyzes the received user input data and recognizes the user's emotion using the emotion_recognition library. For example, if a user inputs "I feel like there have been an increasing number of phishing emails lately," the server will recognize the user's emotion as "surprise" from the content.

[0592] Input: User-entered data

[0593] Output: Recognized user emotion (e.g., surprise)

[0594] Step 3:

[0595] The server uses the risk_assessment library to diagnose information security risks based on user input data, for example, identifying risks to phishing emails and assessing their risk level.

[0596] Input: User-entered data

[0597] Output: Assessed risk (e.g., risk of phishing emails)

[0598] Step 4:

[0599] The server uses the vulnerability_scanner library to scan and identify system and network vulnerabilities, for example to detect email filtering vulnerabilities.

[0600] Input: User input data and system state

[0601] Output: Identified vulnerabilities (e.g. email filtering vulnerabilities)

[0602] Step 5:

[0603] The server then proposes optimal security measures based on the user's perceived emotions and the diagnosed risks and vulnerabilities. For example, if the user's emotion is "surprise," the server will propose measures that emphasize unexpected risks.

[0604] Input: Perceived user sentiment, diagnosed risks, identified vulnerabilities

[0605] Output: Proposed security measures (e.g., measures against unexpected risks)

[0606] Step 6:

[0607] The server sends the proposed security measures to the user's smartphone and displays them to the user, who can then review the measures and implement them as necessary.

[0608] Input: Proposed security measures

[0609] Output: Security measures displayed on the user's smartphone

[0610] Through the above steps, the information security diagnostic system can perform appropriate risk diagnosis and propose countermeasures according to the user's emotions.

[0611] Example 3

[0612] 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."

[0613] While conventional information security diagnostic systems can perform risk and vulnerability assessments, they have the problem of being unable to propose countermeasures that take into account the user's feelings. Another issue is that when proposing optimal security measures, it is difficult to provide flexible options that correspond to the user's situation. Furthermore, due to a lack of functionality to automatically generate specific countermeasures using generative AI models, there are limitations to the quality and applicability of the countermeasures.

[0614] 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.

[0615] In this invention, the server includes an AI-based interactive hearing means, a risk assessment means based on the hearing content, a vulnerability assessment means, a means for proposing current risks and optimal security measures, a means for generating security measures by inputting prompt sentences into a generative AI model, a means for recognizing user emotions and adjusting measures based on those emotions, and a means for presenting measures in the form of a "pine, bamboo, plum" system. This makes it possible to propose flexible and appropriate security measures that take user emotions into consideration.

[0616] "AI-based interactive hearing method" is a method of collecting information through dialogue with users using artificial intelligence.

[0617] "Risk diagnosis means based on interview content" is a means of evaluating the risk of a system based on collected information.

[0618] A "vulnerability assessment means" is a means for detecting potential vulnerabilities within a system and assessing their impact.

[0619] "Means for proposing current risks and optimal security measures" refers to means for evaluating current risks based on the results of the diagnosis and proposing optimal security measures.

[0620] "Means for generating security measures by inputting prompt statements into a generative AI model" refers to means for inputting specific instruction statements into a generative AI model and automatically generating specific security measures based on those statements.

[0621] "Means for recognizing user emotions and adjusting countermeasures based on those emotions" refers to means for analyzing user emotions and adjusting the security countermeasures proposed based on the results.

[0622] "Means for presenting measures in the form of a 'pine, bamboo, plum' system" refers to a means for presenting multiple options in the form of a 'pine, bamboo, plum' system, allowing the user to select the most appropriate measure according to their own situation.

[0623] MODE FOR CARRYING OUT THE INVENTION

[0624] The information security diagnostic system of the present invention has the following configuration and processing procedure.

[0625] System configuration

[0626] This system consists of three main components: a server, a terminal, and a user. The server performs risk assessment, vulnerability assessment, security countermeasure generation using a generative AI model, and emotion recognition and countermeasure adjustment using an emotion engine. The terminal provides an interface for users to access the system and select countermeasures. Users log in to the system, provide information interactively, and select proposed countermeasures.

[0627] Hardware and software used

[0628] A server is a computer system with a powerful processor and large memory capacity. Specifically, it uses the following software:

[0629] Vulnerability scanners: Nessus, OpenVAS

[0630] Emotion engine: Microsoft® Azure® Emotion API, IBM Watson® Tone Analyzer

[0631] Generative AI models: Advanced generative AI models such as GPT-3

[0632] The terminal is a personal computer or smartphone that a user accesses, and a web browser is installed on it, allowing the user to access the server via the Internet.

[0633] Program processing

[0634] The server first conducts an interactive interview when the user logs in to the system, collecting information about the user's system. Next, it performs risk and vulnerability assessment based on the collected information. This is done using vulnerability scanners such as Nessus and OpenVAS.

[0635] After analyzing the diagnostic results, the server inputs prompts into the generative AI model to generate optimal security measures. For example, the following prompts can be used:

[0636] "After conducting a risk assessment of our company's systems, we discovered vulnerabilities such as port 80 being open, passwords being too easy, and employees having low security awareness. Please propose the optimal security measures based on a balanced approach."

[0637] The generative AI model suggests specific countermeasures based on this prompt.

[0638] Furthermore, the server uses an emotion engine to recognize the user's emotions. For example, if the user is feeling sad, the server recognizes that emotion and adjusts the proposed measures accordingly. This enables the server to propose flexible and appropriate security measures that take the user's emotions into consideration.

[0639] The terminal displays suggested security measures to the user. The measures are presented in a "pine, bamboo, and plum" format, and the user can select the most appropriate one based on their own situation. The selected measure is then sent to the server and implemented.

[0640] Specific examples

[0641] For example, suppose a risk assessment of a company's systems reveals the following vulnerabilities:

[0642] Port 80 is open

[0643] The password is too simple

[0644] Employees have low security awareness

[0645] In this case, the server will suggest the following solutions:

[0646] Matsu: Change firewall settings and close port 80

[0647] Bamboo: Strengthen password policies and require complex passwords

[0648] Ume: Provide security training to employees

[0649] The user selects the most appropriate countermeasure from the presented three countermeasures, and the server executes the selected countermeasure. The flow of the identification process in the third embodiment will be described with reference to FIG.

[0650] Step 1:

[0651] A user logs in to the system.

[0652] The user opens a browser on the terminal and accesses the system's login page. He enters his username and password and clicks the "Login" button. The entered username and password are sent to the server, and the server performs authentication. If authentication is successful, the user can access the dashboard. The user's authentication status and the dashboard screen are displayed as output.

[0653] Step 2:

[0654] The server performs risk assessment and vulnerability assessment.

[0655] The server uses vulnerability scanners such as Nessus and OpenVAS to perform risk and vulnerability assessments. As input, the user's system information is used. This scans for potential vulnerabilities in the system and assesses the risk level. For example, it checks whether a specific port is open or whether the password is strong enough. As output, the assessment results are generated and stored in the server's database.

[0656] Step 3:

[0657] The server analyzes the diagnostic results.

[0658] The server analyzes the results of risk and vulnerability assessments. The assessment result data is used as input. The analysis includes storing the assessment result data in a database and identifying the type of risk and its severity. For example, if port 80 is open, it evaluates how much risk it poses. The analysis result is generated as output.

[0659] Step 4:

[0660] The server generates optimal security measures by inputting a prompt into the AI ​​model to generate suggestions.

[0661] The server inputs a prompt sentence into the generative AI model based on the analysis results to generate the optimal security measures. The following prompt sentences are used as input:

[0662] "After conducting a risk assessment of our company's systems, we discovered vulnerabilities such as port 80 being open, passwords being too easy, and employees having low security awareness. Please propose the optimal security measures based on a balanced approach."

[0663] The generative AI model then proposes specific countermeasures based on this prompt, and generates the proposed security countermeasures as an output.

[0664] Step 5:

[0665] The server uses an emotion engine to recognize the user's emotion.

[0666] When a user accesses the dashboard, the server uses an emotion engine to analyze the user's emotions in real time. The inputs are the user's facial expressions and text input. For example, when the user shows an expression through the camera, the emotion engine analyzes the expression and determines whether the user is sad or not. The output is the user's emotional state.

[0667] Step 6:

[0668] The server adjusts the measures based on the emotion.

[0669] The server adjusts the generated security measures based on the user's recognized emotions. As input, the user's emotional state and the generated security measures are used. For example, if the user is sad, the explanation of the measures is presented in a gentler, more understandable way. As output, the adjusted security measures are generated.

[0670] Step 7:

[0671] The device presents countermeasures to the user.

[0672] The terminal displays the proposed security measures to the user. The adjusted security measures are used as input. The measures are displayed in a Pine, Bamboo, and Plum format, and a detailed description of each measure is also included. The user can select a measure by comparing its advantages and disadvantages. As output, a selection screen for measures is displayed.

[0673] Step 8:

[0674] The user selects a measure.

[0675] The user selects the most appropriate countermeasure from the presented three options. The user's choice is used as input. The selected countermeasure is sent to the server, which starts the procedure to implement the countermeasure. For example, if a firewall setting change is selected, the server automatically executes a script to change the settings. The output is that the selected countermeasure is executed.

[0676] (Application example 3)

[0677] 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."

[0678] Conventional information security diagnostic systems can perform risk and vulnerability assessments, but they have the problem of not being able to propose countermeasures that take into account the user's emotions. In particular, when a user is feeling anxious or excited, there is a need for the system to propose appropriate countermeasures that take into account those emotions.

[0679] 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 an AI-based interactive hearing means, a risk assessment means based on the hearing content, a vulnerability assessment means, a means for proposing current risks and optimal security measures, an emotion recognition means, and a means for adjusting the proposed measures based on the emotion. This makes it possible to propose optimal security measures according to the user's emotions.

[0680] "AI-based interactive hearing method" is a method that uses artificial intelligence to interact with users and collect information.

[0681] "Risk diagnosis method based on interview content" is a method for diagnosing information security risks based on the collected interview content.

[0682] A "vulnerability assessment tool" is a tool for identifying and assessing vulnerabilities in systems and devices.

[0683] "Means for proposing current risks and optimal security measures" refers to means for evaluating current risks based on the results of the diagnosis and proposing optimal security measures.

[0684] The "emotion recognition means" is a means for recognizing and analyzing the user's emotions.

[0685] The "means for adjusting proposed measures based on emotions" is a means for adjusting the content of proposed security measures according to the recognized emotions of the user.

[0686] The system for implementing the present invention uses the following hardware and software.

[0687] Hardware

[0688] Smartphone (camera, microphone, sensor)

[0689] software

[0690] Emotion Recognition Library (emotion_recognition)

[0691] Risk and vulnerability assessment library (security_diagnosis)

[0692] Conversational AI engine

[0693] System configuration

[0694] The server includes an AI-based interactive hearing means, a risk diagnosis means based on the hearing content, a vulnerability diagnosis means, a means for proposing current risks and optimal security measures, an emotion recognition means, and a means for adjusting proposed measures based on emotions.

[0695] Processing flow

[0696] 1. Interactive interview: The user uses a smartphone to interact with the AI ​​engine, which answers the user's security-related questions and collects the necessary information.

[0697] 2. Risk Assessment and Vulnerability Assessment: Based on the collected information, the risk assessment and vulnerability assessment library assesses security risks.

[0698] 3. Emotion Recognition: Using the smartphone's camera and microphone, the emotion recognition library analyzes the user's emotions.

[0699] 4. Adjustment of suggested measures: Based on the results of emotion recognition, the suggested measures are adjusted. For example, if the user is feeling anxious, the system will prioritize suggesting measures that will give the user a sense of security.

[0700] 5. Proposal of optimal security measures: Finally, the current risks and optimal security measures are presented to the user in the form of a "Sho-Take-Ume" system.

[0701] Specific examples

[0702] When a user performs a security check on their smartphone and some vulnerabilities are discovered, the emotion engine recognizes the user's anxiety and suggests measures to reassure them (e.g., simple security setting changes).

[0703] Prompt Sentence Examples

[0704] "Perform a security diagnosis on the smartphone and suggest the best solution based on the user's emotions. If the user is feeling anxious, suggest a solution that will reassure them."

[0705] In this way, it becomes possible to propose optimal security measures according to the user's feelings.

[0706] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0707] Step 1:

[0708] The user interacts with the AI ​​engine using a smartphone. The user answers security questions and provides the required information. The input is the user's answer, and the output is the collected information. The server stores this information in a database.

[0709] Step 2:

[0710] The server performs risk and vulnerability assessment based on the interview content. The input is the interview content, and the output is a risk assessment report and a vulnerability assessment report. The risk and vulnerability assessment library generates these reports.

[0711] Step 3:

[0712] The user's smartphone collects the user's emotional data using a camera and microphone. The input is the user's facial expression and voice data, and the output is emotional data analyzed by an emotion recognition library. The server receives this emotional data and stores the analysis results.

[0713] Step 4:

[0714] The server proposes optimal security measures based on the risk assessment report, vulnerability assessment report, and emotion data. The inputs are the risk assessment report, vulnerability assessment report, and emotion data, and the output is proposed measures. A means for adjusting the proposed measures based on emotion selects measures that correspond to the user's emotion.

[0715] Step 5:

[0716] The server presents the optimal security measures to the user in the form of a "Sho-Take-Ume" system. The input is the proposed measures, and the output is the options presented to the user. The user selects the measures that best suit their own situation from the presented measures.

[0717] Step 6:

[0718] The server provides specific instructions for implementing the measures selected by the user. The input is the user's selection, and the output is specific implementation instructions. The server sends this to the user's smartphone and helps the user implement the measures.

[0719] In this way, it becomes possible to propose and implement optimal security measures according to the user's feelings.

[0720] 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.

[0721] 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.

[0722] Another example of generative AI is Gemini (registered trademark) (Internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[0723] 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.

[0724] [Second embodiment]

[0725] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0726] 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.

[0727] 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).

[0728] 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.

[0729] 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.

[0730] 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).

[0731] 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.

[0732] 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.

[0733] 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.

[0734] 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.

[0735] In the smart glasses 214, the processor 46 performs the reception output process. 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.

[0736] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0737] "Example 1"

[0738] The information security diagnostic system of the present invention has an AI-based interactive hearing means. Specifically, the system grasps the user's information security situation and concerns through dialogue with the user. This hearing covers a wide range of topics, such as the user's current information security management situation, past information leak cases, and whether or not they currently have security measures in place.

[0739] "Example 2"

[0740] The system of the present invention also includes risk assessment means and vulnerability assessment means based on the content of interviews. Specifically, information security risks are assessed based on the results of the interviews. This assessment comprehensively assesses various risks, such as the risk of information leakage, the risk of cyber attacks, and the risk of internal fraud. Furthermore, vulnerability assessment is performed to identify vulnerabilities in the system and network.

[0741] "Example 3"

[0742] Furthermore, the system of the present invention has a means for proposing current risks and optimal security measures. Specifically, optimal security measures are proposed based on the results of risk and vulnerability assessments. These proposals include specific measures such as installing a firewall, reviewing password policies, and providing security training to employees. The proposed measures are presented in a "pine, bamboo, and plum" format, allowing users to select the measures that best suit their own situation.

[0743] The processing flow of each embodiment will be described below.

[0744] "Example 1"

[0745] Step 1: Initiate a dialogue with the user using an AI-powered conversational listening method to understand the user's information security situation and concerns.

[0746] Step 2: Based on the results of the interviews, assess information security risks. This assessment comprehensively assesses various risks, including the risk of information leaks, cyber attacks, and internal fraud.

[0747] Step 3: Further vulnerability assessment is performed to identify system and network vulnerabilities. Step 4: Based on the results of the risk and vulnerability assessment, optimal security measures are proposed. These proposals include specific measures such as the introduction of firewalls, review of password policies, and security education for employees.

[0748] "Example 2"

[0749] Step 1: Initiate a dialogue with the user using an AI-powered conversational listening method to understand the user's information security situation and concerns.

[0750] Step 2: Based on the results of the interviews, assess information security risks. This assessment comprehensively assesses various risks, including the risk of information leaks, cyber attacks, and internal fraud.

[0751] Step 3: Further vulnerability assessment is performed to identify system and network vulnerabilities. Step 4: Based on the results of the risk and vulnerability assessment, optimal security measures are proposed. These proposals include specific measures such as the introduction of firewalls, review of password policies, and security education for employees.

[0752] "Example 3"

[0753] Step 1: Initiate a dialogue with the user using an AI-powered conversational listening method to understand the user's information security situation and concerns.

[0754] Step 2: Based on the results of the interviews, assess information security risks. This assessment comprehensively assesses various risks, including the risk of information leaks, cyber attacks, and internal fraud.

[0755] Step 3: Further vulnerability assessment is performed to identify system and network vulnerabilities. Step 4: Based on the results of the risk and vulnerability assessment, optimal security measures are proposed. These proposals include specific measures such as the introduction of firewalls, review of password policies, and security education for employees.

[0756] Example 1

[0757] 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."

[0758] Conventional information security diagnostic systems have difficulty fully understanding users' information security situations and concerns, and often do not provide appropriate risk assessments or suggest security measures. As a result, users are unable to accurately understand their current information security situation and are unable to take appropriate measures.

[0759] 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.

[0760] In this invention, the server includes means for generating an initial question for the user using a generative AI model, means for the user to answer the question using a terminal and send the answer to the server, means for the server to analyze the user's answer and generate a next question, means for the server to analyze all answers and evaluate information security, and means to feed back the evaluation results to the user. This makes it possible to grasp the user's information security situation and concerns in detail, and to diagnose appropriate risks and propose security measures.

[0761] A "generative AI model" is an artificial intelligence technology that generates appropriate questions through dialogue with users and analyzes their answers.

[0762] The "server" is a computer system that receives input from users, generates questions using a generative AI model, analyzes the answers, and evaluates information security.

[0763] A "terminal" is a device through which a user answers questions and transmits the answers to a server.

[0764] "Means for generating questions" is a function that uses a generative AI model to create appropriate questions for users.

[0765] The "means for sending an answer" is a function for sending an answer entered by a user using a terminal to a server.

[0766] The "means for analyzing answers" is a function that allows the server to analyze the user's answers and generate the next question.

[0767] The "means for evaluating information security" is a function in which the server analyzes all responses and evaluates the current state of information security for the user.

[0768] "Means for feeding back evaluation results" is a function by which the server provides the results of information security evaluation to the user.

[0769] This invention relates to an information security diagnostic system that uses a generative AI model to understand information security situations and concerns through dialogue with users, and then performs appropriate risk diagnosis and proposes security measures.

[0770] First, the server uses a generative AI model to generate initial questions for the user. For example, OpenAI's GPT-4 can be used as this generative AI model. The generated questions are intended to collect basic information about the user's information security.

[0771] The user answers questions sent from the server using a terminal. The terminal can be a PC, smartphone, tablet, etc. The answers entered by the user are sent from the terminal to the server. This communication is performed using a secure protocol (e.g., HTTPS).

[0772] The server analyzes the answers received from the user and generates the next question to gather the necessary information. This process is done automatically using a generative AI model. For example, if the user answers, "Customer data was leaked in June of last year," the server generates the next question: "What caused the data leak?"

[0773] The user then uses the device to answer the next question. This process is repeated until all the necessary information is collected. The server analyzes all the collected answers and evaluates the user's current information security status. This evaluation uses an AI model to identify risks and propose countermeasures.

[0774] The server provides feedback on the evaluation results to the user, providing specific advice such as, "Your current security measures are insufficient, and additional measures are required."

[0775] As a concrete example, the following prompt sentence could be input to a generative AI model:

[0776] Example prompt sentence:

[0777] "Has your organization experienced any data breaches in the past year? If so, please provide details."

[0778] In this way, users can objectively grasp their own information security situation and take necessary measures. This system enables effective management of information security risks and the implementation of appropriate measures.

[0779] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0780] Step 1:

[0781] A user accesses the system.

[0782] Users access the information security assessment system using a web browser or a dedicated application. They log in by entering their user ID and password. The input is the user ID and password, and the output is a message indicating whether the login was successful or failed.

[0783] Step 2:

[0784] The server generates an initial question for the user.

[0785] The server uses a generative AI model to generate an initial question for the user, for example, "Has your organization experienced a data breach in the past year?" The input is the user's login information, and the output is the generated question.

[0786] Step 3:

[0787] The user answers the question.

[0788] The user uses the terminal to answer questions sent by the server, for example, "Yes, customer data was leaked in June of last year." The input is the question from the server, and the output is the user's answer.

[0789] Step 4:

[0790] The terminal sends the user's answer to the server.

[0791] The terminal transmits the answers entered by the user to the server in real time. This communication is performed using a secure protocol (e.g., HTTPS). The input is the user's answer, and the output is a transmission completion message to the server.

[0792] Step 5:

[0793] The server analyzes the answer and generates the next question.

[0794] The server analyzes the user's answer and generates a question to gather the necessary information. For example, it generates a question like, "What was the cause of the information leak?" The input is the user's answer, and the output is the next question.

[0795] Step 6:

[0796] The user answers the following questions:

[0797] The user uses the terminal to answer the next question sent from the server. For example, the user might answer, "The cause was unauthorized access by an insider." The input is the next question from the server, and the output is the user's answer.

[0798] Step 7:

[0799] The terminal sends the user's answer to the server.

[0800] The terminal again sends the answer entered by the user to the server. This process is repeated until all the necessary information is collected. The input is the user's answer, and the output is a transmission completion message to the server.

[0801] Step 8:

[0802] The server analyzes all responses and evaluates the information security.

[0803] The server analyzes all collected responses and evaluates the current state of information security for the user. This evaluation uses an AI model to identify risks and propose countermeasures. The input is all user responses, and the output is the evaluation result.

[0804] Step 9:

[0805] The server provides feedback of the evaluation results to the user.

[0806] The server provides the evaluation results as feedback to the user, for example, providing specific advice such as "your current security measures are insufficient and additional measures are required." The input is the evaluation results, and the output is a feedback message to the user.

[0807] (Application example 1)

[0808] 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."

[0809] Conventional information security diagnostic systems were unable to fully grasp users' information security situations and concerns, making it difficult to propose appropriate security measures. Furthermore, when assessing security risks based on user responses, there was a lack of means to utilize generative AI models, resulting in low accuracy in risk assessments.

[0810] 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.

[0811] In this invention, the server includes an AI-based interactive hearing means, a risk assessment means based on the hearing content, a vulnerability assessment means, a means for proposing current risks and optimal security measures, a means for evaluating security risks using a generative AI model based on the user's answers and generating prompt sentences, and a means for proposing appropriate security measures based on the generated prompt sentences. This makes it possible to grasp the user's information security situation and concerns in detail, and to utilize the generative AI model to perform highly accurate security risk assessments and propose appropriate measures.

[0812] "AI-based interactive hearing method" is a method that uses artificial intelligence to interact with users and gather information about situations and concerns regarding information security.

[0813] "Risk diagnosis method based on interview content" is a method for evaluating information security risks based on information collected from users.

[0814] A "vulnerability assessment method" is a method for detecting and evaluating the vulnerabilities of a system or network.

[0815] "Means for proposing current risks and optimal security measures" refers to a means for analyzing the current risk situation and proposing optimal security measures to users.

[0816] "Means for assessing security risks using a generative AI model and generating prompt sentences" refers to means for utilizing a generative AI model to analyze user responses, assess security risks, and generate prompt sentences based on the results.

[0817] "Means for suggesting appropriate security measures based on generated prompt sentences" refers to means for suggesting appropriate security measures to users based on prompt sentences generated by a generative AI model.

[0818] The following system configuration is used as an embodiment of the present invention: The system is composed of three main elements: a server, a terminal, and a user.

[0819] The server includes an AI-based interactive hearing means, a risk assessment means based on the hearing content, a vulnerability assessment means, a means for proposing current risks and optimal security measures, a means for evaluating security risks using a generative AI model and generating prompt text, and a means for proposing appropriate security measures based on the generated prompt text.

[0820] The terminal is a device used by the user, such as a smartphone or PC, that communicates with the server to send and receive information. The user interacts with the server through the terminal and inputs the status and concerns regarding information security.

[0821] The server first gathers information from the user through an AI dialogue, which may include questions such as:

[0822] Please tell us about your current information security management situation.

[0823] Have there been any cases of data breaches in the past?

[0824] What security measures do you currently have in place?

[0825] The user's answers are sent from the terminal to the server, and the server performs risk diagnosis based on the answers. The risk diagnosis means analyzes the user's answers and evaluates the risk of information leakage and information incidents.

[0826] Next, the server uses vulnerability assessment tools to detect and evaluate system and network vulnerabilities, thereby clarifying the current risks.

[0827] The server then uses a generative AI model to further analyze the user's answers and assess their security risks. Based on this assessment, the server generates a prompt. For example, the following prompt might be generated:

[0828] Based on the information below, please assess the security risks and suggest appropriate countermeasures.

[0829] We have anti-virus software installed on all of our company's computers.

[0830] Two years ago, an employee's password was leaked.

[0831] We encourage you to change your password regularly.

[0832] Based on the generated prompt, the server will suggest appropriate security measures, such as updating antivirus software, strengthening password management, and conducting regular security training.

[0833] In this way, users can receive specific and practical security measures. The server uses generative AI models (e.g., OpenAI's GPT-3) to perform highly accurate risk assessments and propose countermeasures, effectively improving users' information security.

[0834] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0835] Step 1:

[0836] The server collects information from the user in an AI-driven dialogue format. Specifically, the server sends the user questions such as, "Please tell us about the current status of information security management." The user answers these questions via their device, and the answers are sent to the server. The input is the user's answer, and the output is the collected information.

[0837] Step 2:

[0838] The server analyzes the user's answers using risk assessment means based on the interview content and evaluates information security risks. Specifically, the server analyzes the user's answers using natural language processing technology and identifies information leakage risks and information incident risks. The input is the user's answers, and the output is the risk assessment results.

[0839] Step 3:

[0840] The server uses vulnerability assessment tools to detect and assess system and network vulnerabilities. Specifically, the server compares the results with an existing vulnerability database to identify vulnerabilities that may exist in the user's system. The input is the user's system information, and the output is the vulnerability assessment results.

[0841] Step 4:

[0842] The server uses the generative AI model to further analyze the user's answers and evaluate security risks. Specifically, the server inputs the user's answers as prompts into the generative AI model to obtain risk assessment results. The inputs are the user's answers and the generative AI model, and the outputs are prompts and risk assessment results.

[0843] Step 5:

[0844] The server proposes appropriate security measures based on the generated prompt. Specifically, the server proposes specific security measures to the user based on the risk assessment results obtained from the generative AI model. Examples include updating antivirus software, strengthening password management, and conducting regular security training. The input is the prompt and the risk assessment results, and the output is proposed security measures.

[0845] Step 6:

[0846] The user receives the security measures proposed by the server and implements them. Specifically, the user checks the proposed measures through the terminal and implements the necessary measures. The input is the security measures proposed by the server, and the output is the implemented security measures.

[0847] In this way, servers, terminals, and users can work together to evaluate information security risks and propose and implement appropriate countermeasures.

[0848] Example 2

[0849] 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."

[0850] In conventional information security assessment systems, analysis of interview content, risk assessment, and vulnerability assessment are often performed manually, which is inefficient. In addition, the assessment results are often inaccurate and incomplete, making it difficult to propose optimal security measures.

[0851] 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.

[0852] In this invention, the server includes a means for a user to input the interview content, a means for the server to analyze the interview content, a means for the server to diagnose risks using a generative AI model, a means for the server to diagnose vulnerabilities using a vulnerability scanner, and a means for the server to provide the diagnosis results to the user. This enables efficient analysis of the interview content and highly accurate risk and vulnerability diagnosis, and makes it possible to propose optimal security measures.

[0853] The "means for the user to input the hearing contents" refers to the means by which the user inputs the hearing contents through the system interface.

[0854] The "means for the server to analyze the hearing content" refers to the means by which the server analyzes the hearing content sent from the user and extracts important keywords and phrases.

[0855] "Means for the server to diagnose risks using a generative AI model" refers to means for the server to use a generative AI model to diagnose information security risks based on the content of the interview.

[0856] "Means for a server to diagnose vulnerabilities using a vulnerability scanner" refers to means for a server to use a vulnerability scanner to diagnose vulnerabilities in a system or network.

[0857] The "means by which the server provides the diagnosis results to the user" refers to the means by which the server integrates the risk diagnosis results and the vulnerability diagnosis results and provides them to the user.

[0858] The present invention relates to an information security diagnostic system, in which a user inputs interview details, a server analyzes the details, performs risk diagnostics and vulnerability diagnostics, and provides the diagnostic results to the user.

[0859] System configuration

[0860] A means for users to input their hearing details

[0861] A user can use a web form or a dedicated application to describe in detail the current system status and concerns. For example, the user accesses a web form, enters information in the text boxes for "current system status" and "concerns," and clicks the "Submit" button.

[0862] The server's means of analyzing the hearing content

[0863] The server receives the audio recordings sent by the user and analyzes them using natural language processing (NLP) techniques, specifically by tokenizing the text using NLP libraries (e.g., spaCy or NLTK) and extracting important keywords and phrases.

[0864] How the server uses the generative AI model to diagnose risk

[0865] The server diagnoses risk using a generative AI model (e.g., OpenAI's GPT-4) based on the analyzed interview content. It generates prompt sentences and inputs them into the AI ​​model to obtain the results of risk diagnosis.

[0866] As a concrete example, consider the following situation where a user enters the following information:

[0867] "Our system is frequently accessed from outside, and our internal database contains confidential information. We are also concerned about whether employee access rights are being properly managed."

[0868] In this case, the server will input the following prompt into the generative AI model:

[0869] "Please assess information security risks based on the following interview content. Interview content: 'Our system is frequently accessed from outside, and our internal database contains confidential information. We are also concerned about whether employee access privileges are being properly managed.'"

[0870] The generative AI model diagnoses the risk of information leaks, cyber attacks, and internal fraud, and returns the results to the server.

[0871] How the server uses a vulnerability scanner to diagnose vulnerabilities

[0872] The server uses a vulnerability scanner (e.g., Nessus) to assess system and network vulnerabilities. The scanner scans the entire system and identifies potential vulnerabilities, such as out-of-date software or improper access privilege settings.

[0873] A means by which the server provides the diagnostic results to the user

[0874] The server integrates the results of risk assessment and vulnerability assessment and provides them to the user. The results are displayed in the form of a dashboard or report. The user can access the dashboard and check the assessment results.

[0875] In this way, it becomes possible to efficiently analyze the interview content and perform highly accurate risk and vulnerability diagnosis, thereby proposing optimal security measures.

[0876] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0877] Step 1:

[0878] The user inputs the details of the hearing.

[0879] Specifically, the user accesses a web form or dedicated application, enters information in the text boxes for "current system status" and "concerns," and clicks the "Submit" button.

[0880] Input: Hearing contents entered by the user (text data)

[0881] Output: Interview content (text data) sent to the server

[0882] Step 2:

[0883] The server analyzes the hearing content.

[0884] The server receives the audio recordings sent by the user and analyzes them using natural language processing (NLP) techniques, specifically by tokenizing the text using NLP libraries (e.g., spaCy or NLTK) and extracting important keywords and phrases.

[0885] Input: Interview details sent by the user (text data)

[0886] Output: Analyzed keywords and phrases (text data)

[0887] Step 3:

[0888] The server uses generative AI models to diagnose risks.

[0889] The server diagnoses risk using a generative AI model (e.g., OpenAI's GPT-4) based on the analyzed interview content. The server generates prompt sentences and inputs them into the AI ​​model to obtain the results of risk diagnosis.

[0890] Specifically, the server generates a prompt sentence and inputs it into the generative AI model. It receives a response from the generative AI model and obtains the risk assessment result.

[0891] Input: Analyzed keywords and phrases (text data), generated prompt sentences

[0892] Output: Risk diagnosis results (text data)

[0893] Step 4:

[0894] The server uses a vulnerability scanner to check for vulnerabilities.

[0895] The server uses a vulnerability scanner (e.g., Nessus) to assess system and network vulnerabilities. The scanner scans the entire system and identifies potential vulnerabilities.

[0896] Specifically, the server launches a vulnerability scanner, provides information about the systems and networks to be scanned, receives the scan results, and generates a list of vulnerabilities.

[0897] Input: Information about the system or network you want to scan

[0898] Output: Vulnerability diagnosis results (text data)

[0899] Step 5:

[0900] The server provides the diagnostic results to the user.

[0901] The server integrates the results of risk assessment and vulnerability assessment and provides them to the user. The results are displayed in the form of a dashboard or report.

[0902] Specifically, the server integrates the risk assessment results and vulnerability assessment results and displays them in a user-friendly format (e.g., graphs and tables). Users can access the dashboard and check the assessment results.

[0903] Input: Risk assessment results, vulnerability assessment results

[0904] Output: Diagnostic results provided to the user (dashboard and report format)

[0905] (Application example 2)

[0906] 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."

[0907] In today's information society, information security risks are diverse and have become a serious issue for companies and individuals. In particular, risks such as information leaks, cyber attacks, and internal fraud can cause serious damage if appropriate measures are not taken. However, there are only a limited number of systems that can comprehensively diagnose these risks and propose appropriate countermeasures. In addition, there is a lack of functionality to identify specific vulnerabilities based on the diagnosis results and provide them as a report. This makes it difficult for users to grasp the full picture of risks and take appropriate countermeasures.

[0908] 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.

[0909] In this invention, the server includes an AI-based interactive hearing means, a risk diagnosis means based on the hearing content, a vulnerability diagnosis means, a means for proposing current risks and optimal security measures, a means for identifying system and network vulnerabilities based on the diagnosis results, and a means for generating a report of the risk diagnosis results and vulnerability diagnosis results, thereby enabling users to comprehensively diagnose information security risks, identify specific vulnerabilities, and take appropriate measures.

[0910] "AI-based interactive hearing method" is a method of collecting information from users in an interactive manner using artificial intelligence.

[0911] "Risk diagnosis method based on interview content" is a method for diagnosing information security risks based on collected interview content.

[0912] A "vulnerability assessment method" is a method for identifying vulnerabilities in a system or network.

[0913] "Means for proposing current risks and optimal security measures" refers to means for proposing optimal security measures for diagnosed risks.

[0914] "Means for identifying vulnerabilities in a system or network based on the results of the diagnosis" refers to means for identifying specific vulnerabilities in a system or network based on the results of the risk diagnosis.

[0915] The "means for generating a report of the risk diagnosis result and the vulnerability diagnosis result" is a means for outputting the risk diagnosis result and the vulnerability diagnosis result together in a report format.

[0916] To implement the present invention, the following system configuration and processing procedures are required.

[0917] System Configuration

[0918] 1. Hardware:

[0919] Smartphone: A device that allows users to listen.

[0920] Server: A central processing unit for performing risk and vulnerability assessments.

[0921] 2. Software:

[0922] Python: A programming language for implementing risk and vulnerability assessment logic.

[0923] JSON: A data format for outputting diagnostic results in report format.

[0924] Processing steps

[0925] 1. Hearing:

[0926] The smartphone application conducts an interactive interview with the user, who then inputs information about risks such as information leaks and cyber attacks.

[0927] 2. Risk Assessment:

[0928] The server diagnoses information security risks based on the interview results, specifically assessing the risk of information leaks, cyber attacks, and internal fraud.

[0929] 3. Vulnerability Assessment:

[0930] The server identifies system and network vulnerabilities based on the results of the risk assessment, such as insufficient data encryption or an unconfigured firewall.

[0931] 4. Proposed measures:

[0932] The server proposes the most appropriate security measures for the assessed risks and vulnerabilities, presenting multiple countermeasure options (e.g., basic, intermediate, advanced).

[0933] 5. Report Generation:

[0934] The server compiles the risk assessment results and vulnerability assessment results and outputs them in a report format. This report is generated in JSON format and provided to the user.

[0935] Specific examples

[0936] The user launches the smartphone application and inputs interview data about risks related to "information leakage" and "cyber attacks." Based on this, the server identifies vulnerabilities such as "insufficient data encryption" and "no firewall configured" and generates a report.

[0937] Prompt Sentence Examples

[0938] User: My system is at risk of data leakage and cyber attacks.

[0939] App: Information leakage risk identified. Insufficient data encryption.

[0940] App: Cyber ​​attack risk identified. Firewall not configured.

[0941] App: The results of the risk assessment and vulnerability assessment were generated as a report.

[0942] In this way, users can comprehensively diagnose information security risks, identify specific vulnerabilities, and take appropriate measures.

[0943] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0944] Step 1:

[0945] The user launches the smartphone application and begins the interview. The user enters information about risks such as information leaks and cyber attacks. The entered data is sent to the server as the interview content.

[0946] Input: Information about risks entered by users (e.g., information leaks, cyber attacks)

[0947] Output: The hearing is sent to the server

[0948] Step 2:

[0949] The server performs risk assessment based on the received interview content. Specifically, it evaluates the risk of information leakage, cyber attack risk, internal fraud risk, etc. The results of the risk assessment are saved as risk assessment results.

[0950] Input: Hearing details

[0951] Output: Risk diagnosis results

[0952] Step 3:

[0953] The server identifies system and network vulnerabilities based on the risk assessment results. For example, it identifies specific vulnerabilities such as insufficient data encryption or an unconfigured firewall. The vulnerability assessment results are saved as vulnerability assessment results.

[0954] Input: Risk diagnosis results

[0955] Output: Vulnerability assessment results

[0956] Step 4:

[0957] The server proposes optimal security measures for the diagnosed risks and vulnerabilities. It presents multiple countermeasure options (e.g., basic, intermediate, advanced). The countermeasure proposal results are saved as countermeasure proposal results.

[0958] Input: Risk assessment results, vulnerability assessment results

[0959] Output: Countermeasure proposal results

[0960] Step 5:

[0961] The server compiles the risk assessment results and vulnerability assessment results and outputs them in a report format. This report is generated in JSON format and provided to the user. The user can check the report through a smartphone application.

[0962] Input: Risk assessment results, vulnerability assessment results, countermeasure proposal results

[0963] Output: Report (JSON format)

[0964] In this way, users can comprehensively diagnose information security risks, identify specific vulnerabilities, and take appropriate measures.

[0965] Example 3

[0966] 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."

[0967] In today's information security environment, companies and organizations face a variety of risks and vulnerabilities. It is extremely important to properly assess these risks and propose optimal security measures. However, current systems face the challenge of effectively analyzing the results of risk and vulnerability assessments and presenting measures in a format that is easy for users to understand. In particular, there is a lack of support for users to select appropriate measures based on their own situation.

[0968] 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.

[0969] In this invention, the server includes an AI-based interactive hearing means, a risk assessment means based on the hearing content, a vulnerability assessment means, a means for storing the assessment results in a database, a means for analyzing the stored assessment results, a means for proposing current risks and optimal security measures, and a means for presenting the proposed measures in a pine, bamboo, and plum format. This allows the user to accurately grasp the current risks based on the assessment results and select the optimal security measures according to their own situation.

[0970] "AI-based interactive hearing means" is a means of using artificial intelligence to converse with users and collect necessary information.

[0971] "Risk diagnosis method based on interview content" is a method for evaluating the risk of a system or network based on collected information.

[0972] A "vulnerability assessment method" is a method for detecting and evaluating the vulnerabilities of a system or network.

[0973] The "means for storing the diagnosis results in a database" refers to a means for storing the results of risk diagnosis and vulnerability diagnosis in a database.

[0974] The "means for analyzing stored diagnostic results" is a means for analyzing the diagnostic results stored in the database and evaluating the current risk.

[0975] "Means for proposing current risks and optimal security measures" refers to a means for generating and proposing optimal security measures based on the analysis results.

[0976] The "means for presenting proposed measures in the form of a 'pine, bamboo, plum' system" is a means for presenting generated security measures in the form of a 'pine, bamboo, plum' system so that the user can easily select one.

[0977] MODE FOR CARRYING OUT THE INVENTION

[0978] This invention relates to an information security diagnostic system, and in particular to a system that proposes optimal security measures based on the results of risk and vulnerability diagnostics. This system operates in cooperation with three parties: a server, a terminal, and a user.

[0979] 1. Program Generation

[0980] The server generates a program that proposes optimal security measures based on the results of risk and vulnerability assessments. This program is developed using programming languages ​​such as Python and Java.

[0981] 2. Program processing explanation

[0982] The server collects the results of risk and vulnerability assessments and stores them in a database. The server then analyzes the stored data and evaluates the current risk. This evaluation uses machine learning models (such as Scikit-learn and TensorFlow).

[0983] Based on the evaluation results, the server proposes optimal security measures, such as installing a firewall, reviewing password policies, and providing security training to employees. The proposed measures are presented in a "pine, bamboo, and plum" format, allowing users to select the one that best suits their situation.

[0984] 3. Examples of concrete examples and prompts

[0985] For example, suppose a company conducts a risk and vulnerability assessment and finds the following risks:

[0986] High risk of external attacks on internal networks

[0987] Weak password

[0988] Employees have low security awareness

[0989] In this case, the server will suggest the following security measures:

[0990] Matsu: Implementing a high-performance firewall, setting strong password policies, and providing security training to all employees.

[0991] Take: Implementing a mid-range firewall, reviewing password policies, and providing security training to key employees.

[0992] Ume: Implementing basic firewalls, simple review of password policies, and distributing security awareness materials.

[0993] Example prompts to input to a generative AI model:

[0994] Please suggest optimal security measures based on the results of the company's risk and vulnerability assessment. The following risks have been identified:

[0995] High risk of external attacks on internal networks

[0996] Weak password

[0997] Employees have low security awareness

[0998] Please make your proposal in the form of pine, bamboo, and plum."

[0999] In this way, the user can select the most suitable security measure according to his / her own situation. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1000] Step 1:

[1001] The server uses AI to conduct dialogue with the user. As input, it receives information provided by the user (such as the system configuration, software used, and security policies). As output, it generates data for risk and vulnerability assessment based on the interview content. Specifically, the server uses natural language processing technology to analyze the user's responses and extract the necessary information.

[1002] Step 2:

[1003] The server performs risk diagnosis based on the interview content. It uses the data obtained in step 1 as input. It generates risk assessment results as output. Specifically, the server uses the risk assessment model to assess the risk of information leakage and information incidents.

[1004] Step 3:

[1005] The server performs vulnerability assessment. It uses the data obtained in step 1 as input and generates vulnerability assessment results as output. Specifically, the server uses a network scanning tool (e.g., Nessus or OpenVAS) to detect system vulnerabilities.

[1006] Step 4:

[1007] The server saves the results of the risk assessment and vulnerability assessment in a database. As input, it uses the assessment results obtained in steps 2 and 3. As output, it generates the assessment results stored in the database. Specifically, the server obtains the assessment results in JSON format and inserts them into the database.

[1008] Step 5:

[1009] The server analyzes the stored diagnostic results. As input, it uses the diagnostic results stored in the database. As output, it generates a current risk assessment result. Specifically, the server uses a machine learning model (e.g., Scikit-learn or TensorFlow) to score the severity of the risk.

[1010] Step 6:

[1011] The server proposes optimal security measures based on the analysis results. The risk assessment results obtained in step 5 are used as input. Security measure proposals are generated as output. Specifically, the server prioritizes measures based on the risk score and generates specific measures such as installing a firewall, reviewing password policies, and providing security training.

[1012] Step 7:

[1013] The terminal presents the generated security measures to the user. As input, it uses the security measure proposals generated in step 6. As output, it generates the security measures selected by the user. Specifically, the terminal displays the proposed measures in the form of a pine, bamboo, and plum tree through the user interface and sends the user's selection to the server.

[1014] Step 8:

[1015] The user selects the most appropriate security measure from the presented security measures. As input, the security measures presented in step 7 are used. As output, the selected security measure is sent to the server. In concrete terms, the user operates the terminal interface to select the most appropriate measure.

[1016] (Application example 3)

[1017] 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."

[1018] In today's information society, personal and corporate devices and networks are exposed to a variety of risks. The risks of information leaks and information incidents are particularly serious, making it necessary to properly diagnose these risks and propose optimal security measures. However, current security assessment systems lack specific assessments based on the user's device information and comprehensive recommendations based on the assessment results. This makes it difficult for users to select the optimal security measures for their own situation.

[1019] 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 an AI-based interactive hearing means, a risk assessment means based on the hearing content, a vulnerability assessment means, a means for proposing current risks and optimal security measures, a means for performing risk and vulnerability assessment based on the user's device information, a means for proposing security measures in the form of a pine, bamboo, and plum system based on the assessment results, and a means for displaying the proposed security measures to the user. This enables the user to specifically assess the risks of their own device and network and select optimal security measures.

[1020] "AI-based interactive hearing method" is a method that uses artificial intelligence to interact with users and collect necessary information.

[1021] "Risk diagnosis method based on interview content" is a method for evaluating potential risks based on collected information.

[1022] A "vulnerability assessment tool" is a tool for identifying and assessing vulnerabilities in systems and devices.

[1023] "Means for proposing current risks and optimal security measures" refers to a means for analyzing the current risk situation and proposing optimal security measures for that situation.

[1024] "Means for diagnosing risks and vulnerabilities based on user device information" refers to means for diagnosing risks and vulnerabilities using information about a user's device.

[1025] "Means for proposing security measures in the form of a 'Sho, Take, Plum' hierarchy based on diagnostic results" refers to means for proposing security measures of different levels in the form of a 'Sho, Take, Plum' hierarchy based on diagnostic results.

[1026] The "means for displaying the proposed security measures to the user" refers to a means for visually displaying the proposed security measures to the user.

[1027] A system for carrying out this invention has the following configuration: The server includes means for conducting an interactive interview using AI, means for diagnosing risks and vulnerabilities based on the interview content, means for proposing current risks and optimal security measures, means for diagnosing risks and vulnerabilities based on the user's device information, means for proposing security measures in the form of a pine, bamboo, and plum system based on the results of the assessment, and means for displaying the proposed security measures to the user.

[1028] Program processing explanation

[1029] The server first collects necessary information from the user through an AI-based dialogue. This includes the user's device information (OS, version, installed apps, etc.). The collected information is then analyzed by risk assessment and vulnerability assessment methods. The risk assessment method evaluates the risk of information leakage and information incidents, while the vulnerability assessment method identifies vulnerabilities in the system and device.

[1030] Next, the server proposes the current risks and optimal security measures based on the results of the diagnosis. The proposed measures are presented in the form of a "pine, bamboo, and plum" system, allowing users to select the measures that best suit their own situation. This allows users to specifically diagnose the risks to their devices and networks and select the optimal security measures.

[1031] Hardware and software used

[1032] Hardware: Server, user's smartphone or computer

[1033] Software: AI models (e.g., natural language processing models), APIs for risk and vulnerability assessment, and applications that provide user interfaces

[1034] Specific examples

[1035] For example, if a user is using Android 10 and has installed apps "App1", "App2", and "App3", the server will do the following:

[1036] 1. Collect user device information.

[1037] 2. Use risk assessment tools and vulnerability assessment tools to assess information leakage risks and information incident risks and identify device vulnerabilities.

[1038] 3. Based on the diagnostic results, we propose security measures in the form of a 3-pronged approach.

[1039] 4. Display the proposed security measures to the user.

[1040] Prompt Sentence Examples

[1041] An example of a prompt sentence to input to the generative AI model is as follows:

[1042] Please create a program that performs risk and vulnerability assessments based on the user's device information and proposes optimal security measures. The proposed measures should be presented in a "pine, bamboo, and plum" format. Please use the following device information:

[1043] OS: Android

[1044] Version: 10

[1045] Installed Apps: ["App1", "App2", "App3"]

[1046] In this way, the detailed description provides specific measures for enhancing the security of users' devices and networks.

[1047] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1048] Step 1:

[1049] The server collects necessary information from the user through an AI-based dialogue. Specifically, it receives the user's device information (OS, version, installed apps, etc.) as input and stores it in a database. The input information serves as the basis for subsequent risk and vulnerability assessments.

[1050] Step 2:

[1051] The server analyzes the collected device information using a risk assessment method based on the interview content. Specifically, it receives the device information as input and applies a risk assessment algorithm to evaluate the risk of information leakage and information incidents. The output is a risk level (e.g., low, medium, or high).

[1052] Step 3:

[1053] The server uses a vulnerability assessment tool to identify device vulnerabilities. Specifically, it receives device information as input and compares it with a database of known vulnerabilities. The output is a list of identified vulnerabilities and their severity.

[1054] Step 4:

[1055] The server proposes the current risk and optimal security measures based on the results of risk and vulnerability assessment. Specifically, it receives the risk level and vulnerability list as input and applies a security measure proposal algorithm. The output is a list of proposed security measures in the form of a "pine, bamboo, and plum" system.

[1056] Step 5:

[1057] The server proposes security measures based on the diagnosis results in a "Sho-Take-Ume" format. Specifically, it receives a list of proposed security measures as input and generates data to visually display to the user. As an output, it obtains a selection of security measures that are displayed in the user interface.

[1058] Step 6:

[1059] The user checks the proposed security measures and selects the most appropriate one based on their own situation. Specifically, the user receives the security measure options provided by the server as input and sends the selected measure to the server. As output, the selected security measure is recorded on the server.

[1060] Step 7:

[1061] The server provides specific procedures for implementing the security measures selected by the user. Specifically, it receives the selected security measures as input and generates the execution procedures. As an output, it obtains a list of execution procedures to be provided to the user.

[1062] In this way, the server specifically diagnoses the risks to the user's device and network and assists in selecting and implementing the most appropriate security measures.

[1063] 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.

[1064] "Example 1"

[1065] One embodiment of the present invention is an information security diagnostic system that incorporates an emotion engine. This system diagnoses information security risks through dialogue with the user and proposes optimal security measures. In particular, the emotion engine recognizes the user's emotions and adjusts the risk diagnosis and proposed measures based on those emotions. For example, if the user is feeling anxious, the emotion engine recognizes that anxiety and reduces the user's anxiety by providing a more detailed explanation and proposing specific measures. Also, if the user is feeling angry, the emotion engine recognizes that anger, identifies the cause of the problem, and proposes a solution. In this way, the information security diagnostic system that incorporates an emotion engine can provide appropriate measures that correspond to the user's emotions.

[1066] "Example 2"

[1067] Another embodiment of the present invention is an information security diagnostic system that incorporates an emotion engine. This system diagnoses information security risks through dialogue with the user and proposes optimal security measures. In particular, the emotion engine recognizes the user's emotions and adjusts the risk diagnosis and proposed measures based on those emotions. For example, if the user is feeling happy, the emotion engine recognizes that joy and proposes measures that will allow the user to use the system with peace of mind. Also, if the user is feeling surprised, the emotion engine recognizes that surprise and proposes measures to address unexpected risks. In this way, the information security diagnostic system that incorporates an emotion engine can provide appropriate measures that correspond to the user's emotions.

[1068] "Example 3"

[1069] A further embodiment of the present invention is an information security diagnostic system that incorporates an emotion engine. This system diagnoses information security risks through dialogue with the user and proposes optimal security measures. In particular, the emotion engine recognizes the user's emotions and adjusts the risk diagnosis and proposed measures based on those emotions. For example, if the user is feeling sad, the emotion engine recognizes that sadness and proposes measures that take the user's feelings into consideration. Also, if the user is excited, the emotion engine recognizes that excitement and proposes measures that allow the user to use the system safely. In this way, the information security diagnostic system that incorporates an emotion engine can provide appropriate measures that correspond to the user's emotions.

[1070] The processing flow of each embodiment will be described below.

[1071] "Example 1"

[1072] Step 1: Start a dialogue with the user. This dialogue is for information security risk assessment.

[1073] Step 2: The emotion engine recognizes the user's emotions, which are inferred from the user's statements and actions.

[1074] Step 3: The emotion engine adjusts the risk assessment results and suggested measures based on the emotions it recognizes. For example, if the user feels anxious, it will suggest more detailed explanations and specific measures.

[1075] "Example 2"

[1076] Step 1: Start a dialogue with the user. This dialogue is for information security risk assessment.

[1077] Step 2: The emotion engine recognizes the user's emotions, which are inferred from the user's statements and actions.

[1078] Step 3: The emotion engine adjusts the risk assessment results and proposed measures based on the emotions it recognizes. For example, if the user is feeling happy, it will propose measures that will allow the user to use the system with peace of mind.

[1079] "Example 3"

[1080] Step 1: Start a dialogue with the user. This dialogue is for information security risk assessment.

[1081] Step 2: The emotion engine recognizes the user's emotions, which are inferred from the user's statements and actions.

[1082] Step 3: Based on the emotions recognized by the emotion engine, the risk assessment results and suggested measures are adjusted. For example, if the user is feeling sad, the system will suggest measures that take into account the user's feelings.

[1083] Example 1

[1084] 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."

[1085] Conventional information security diagnostic systems diagnose risks and propose countermeasures without considering the user's feelings, which means that they are unable to fully alleviate users' anxiety and anger. Another issue is that the countermeasure proposals are uniform, making it difficult to respond flexibly to the user's specific situation.

[1086] 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.

[1087] In this invention, the server includes an AI-based interactive hearing means, a risk diagnosis means based on the hearing content, an emotion recognition means, a means for proposing current risks and optimal security measures, and a means for adjusting the proposed measures based on the user's emotions, thereby enabling risk diagnosis and proposed measures that take the user's emotions into consideration.

[1088] "AI-based interactive hearing method" is a method that uses artificial intelligence to interact with users and gather information about situations and concerns regarding information security.

[1089] "Risk diagnosis method based on interview content" is a method for evaluating and diagnosing information security risks based on information collected from users.

[1090] The "emotion recognition means" is a means for analyzing and recognizing emotions from user input and dialogue content.

[1091] The "means for proposing current risks and optimal security measures" is a means for proposing optimal security measures to users for diagnosed risks.

[1092] The "means for adjusting the proposed measures based on the user's emotions" is a means for adjusting the content and method of the proposed measures in accordance with the recognized emotions of the user.

[1093] "Information leakage risk" refers to the risk that confidential information may be leaked to the outside.

[1094] "Information incident risk" refers to the risk that a security incident may occur in an information system.

[1095] "Providing multiple options" means presenting multiple different options for security measures to the user.

[1096] The information security diagnostic system of this invention includes an AI-based interactive hearing means, a risk diagnosis means based on the hearing content, an emotion recognition means, a means for proposing current risks and optimal security measures, and a means for adjusting the proposed measures based on the user's emotions.

[1097] Hardware and software used

[1098] Hardware: Servers, terminals (PCs, smartphones)

[1099] Software: Generative AI models, emotion engines, conversational interfaces

[1100] Program processing explanation

[1101] Start of user interaction

[1102] The user uses the interactive interface of the terminal to input questions or concerns about information security. For example, they might input, "Recently, there has been an information leak within our company. Please tell me what measures we should take."

[1103] Sending input data

[1104] The terminal receives the user's input and sends it to a server over the Internet, where the input data is encrypted using the SSL / TLS protocol.

[1105] Analyzing input data

[1106] The server receives the user's input and passes it to the generative AI model for analysis. The generative AI model understands the user's questions and concerns and extracts the necessary information.

[1107] Emotion recognition

[1108] The server uses an emotion engine to recognize emotions from the user's input, for example, if the user is feeling "anxious" or "angry," it identifies that emotion.

[1109] Risk diagnosis and countermeasure proposals

[1110] The server uses the generative AI model to diagnose the user's information security risk. Based on the results of the emotion engine, it generates countermeasures that correspond to the user's emotions. For example, for a user who feels anxious, it provides a detailed explanation and suggests specific countermeasures.

[1111] Sending diagnostic results and proposed solutions

[1112] The server generates an encrypted version of the diagnostic results and proposed solutions, which are then sent to the device. For example, the server sends a message such as, "The cause of the information leak is inadequate access control. Please follow the steps below to strengthen access control."

[1113] Displaying the results

[1114] The device displays the diagnosis results and proposed solutions to the user, who can then check and implement the specific solutions displayed on the screen.

[1115] Specific examples

[1116] If a user enters "Recently, there has been an information leak within our company. Please tell me what measures we should take," the system will behave as follows:

[1117] 1. The terminal receives the user's input and sends it to the server.

[1118] 2. The server uses a generative AI model to analyze the input and obtain details of the data breach.

[1119] 3. The emotion engine recognizes the user's concerns and suggests detailed explanations and specific solutions.

[1120] 4. The server sends the diagnosis results and proposed solutions to the device, which then displays the results to the user.

[1121] In this way, the information security diagnostic system of the present invention diagnoses information security risks through dialogue with the user, and provides appropriate measures corresponding to the user's emotions using an emotion engine.

[1122] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1123] Step 1:

[1124] The user provides input through a dialogue interface.

[1125] The user types into the interactive interface of the terminal, "Recently, there has been an information leak within the company. Please tell me what measures should be taken."

[1126] Input: User's question or concern

[1127] Output: User input data

[1128] Step 2:

[1129] The terminal sends the user's input to the server.

[1130] The terminal receives the user's input, encrypts it using the SSL / TLS protocol, and sends it to the server.

[1131] Input: User-entered data

[1132] Output: The encrypted input data

[1133] Step 3:

[1134] The server parses the input using a generative AI model.

[1135] The server passes the received input data to the generative AI model, which analyzes it using natural language processing technology. For example, it extracts keywords such as "information leak" and "countermeasures" from the input sentence.

[1136] Input: Encrypted input data

[1137] Output: Parsed information (keywords, etc.)

[1138] Step 4:

[1139] The server uses an emotion engine to recognize the user's emotion.

[1140] The server uses an emotion engine to recognize that the user is feeling "anxiety" from the input sentence. The emotion engine identifies emotions from the context of the input sentence and the choice of words.

[1141] Input: Analyzed information (keywords, etc.)

[1142] Output: Recognized emotion information

[1143] Step 5:

[1144] The server performs risk assessment and generates countermeasures.

[1145] The server uses the generative AI model to diagnose the user's information security risk. Based on the results of the emotion engine, it generates countermeasures that correspond to the user's emotions. For example, for a user who feels anxious, it provides a detailed explanation and suggests specific countermeasures.

[1146] Input: Recognized emotion information, analyzed information (keywords, etc.)

[1147] Output: Diagnostic results and proposed solutions

[1148] Step 6:

[1149] The server sends the diagnosis results and suggested solutions to the terminal.

[1150] The server generates an encrypted version of the diagnostic results and proposes countermeasures, and sends them to the device. For example, it sends a message such as, "The cause of the information leak is inadequate access control. Please follow the steps below to strengthen access control."

[1151] Input: Diagnostic results and proposed solutions

[1152] Output: Encrypted diagnostic results and suggested solutions

[1153] Step 7:

[1154] The terminal displays the results to the user.

[1155] The device displays the diagnosis results and proposed solutions to the user, who can then check and implement the specific solutions displayed on the screen.

[1156] Input: Encrypted diagnostic results and suggested solutions

[1157] Output: Diagnostic results and suggested solutions displayed to the user

[1158] (Application example 1)

[1159] 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."

[1160] Conventional information security diagnostic systems perform risk assessments and propose countermeasures without taking user emotions into consideration, which means they are unable to respond appropriately to user emotions such as anxiety and anger. Furthermore, because they are unable to propose flexible countermeasures based on user emotions, user satisfaction may decline. This could result in a decrease in the effectiveness of information security measures.

[1161] 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.

[1162] In this invention, the server includes an AI-based interactive hearing means, a risk diagnosis means based on the hearing content, a vulnerability diagnosis means, an emotion engine means for recognizing the user's emotions, a means for adjusting the risk diagnosis and countermeasure proposals based on the emotions, and a means for proposing current risks and optimal security countermeasures. This enables flexible risk diagnosis and countermeasure proposals according to the user's emotions.

[1163] "AI-based interactive hearing method" is a method that uses artificial intelligence to interact with users and gather information about situations and concerns regarding information security.

[1164] "Risk diagnosis method based on interview content" is a method for evaluating and diagnosing information security risks based on collected interview content.

[1165] A "vulnerability assessment method" is a method for detecting and evaluating the vulnerabilities of a system or network.

[1166] The "emotion engine means for recognizing user emotions" is an engine for analyzing and recognizing emotions from the user's statements and actions.

[1167] The "means for adjusting risk diagnosis and proposed countermeasures based on emotions" refers to a means for adjusting risk diagnosis and proposed security countermeasures according to the recognized emotions of the user.

[1168] "Means for proposing current risks and optimal security measures" refers to a means for assessing the current risk situation and proposing optimal security measures to users based on that assessment.

[1169] As an embodiment of the present invention, an information security diagnostic system operates in cooperation with three entities: a server, a terminal, and a user.

[1170] System Program

[1171] The server runs programs that implement the following main functions:

[1172] 1. Interactive hearing methods

[1173] The server uses artificial intelligence (AI) to interact with users and gather information about their information security situation and concerns. Specifically, it analyzes the information entered by users via their smartphones or computer terminals and asks questions in an interactive format.

[1174] 2. Risk diagnostic tools

[1175] The server evaluates and diagnoses information security risks based on the collected interview data, including information leakage risks and information incident risks.

[1176] 3. Vulnerability Assessment Methods

[1177] The server detects and assesses system and network vulnerabilities, thereby identifying potential security holes.

[1178] 4. Emotional Engine Means

[1179] The server uses an emotion engine to analyze and recognize emotions from the user's words and actions, thereby determining whether the user is feeling anxious or angry.

[1180] 5. Emotion-based regulation

[1181] The server then adjusts the risk assessment and security measures it proposes based on the user's emotions. For example, if the user feels anxious, it will provide more detailed explanations and suggest specific measures.

[1182] 6. Security measures proposal method

[1183] The server evaluates the current risk situation and proposes optimal security measures to the user based on that assessment. This proposal can provide multiple options, like a "pine, bamboo, plum" system.

[1184] Hardware and software used

[1185] Hardware: Smartphones, computer terminals, servers

[1186] Software: Python, OpenAI API, Transformers library

[1187] Data processing and calculation

[1188] 1. Getting User Input

[1189] The terminal takes input from the user and sends it to the server.

[1190] 2. Emotion recognition

[1191] The server uses an emotion engine to analyze user input and recognize emotions, using the Transformers library.

[1192] 3. Risk diagnosis and generation of countermeasure proposals

[1193] The server uses OpenAI's generative AI model to generate security measures based on user input and sentiment.

[1194] Specific examples

[1195] For example, if a user types, "I'm worried because I've been receiving a lot of phishing emails lately," the server analyzes this input and recognizes the emotion "anxiety." It then uses a generative AI model to suggest the following security measures:

[1196] Example prompt sentence:

[1197] User input: I'm worried because I've been getting a lot of phishing emails lately.

[1198] Emotion: Anxiety

[1199] Please suggest appropriate security measures.

[1200] In this way, it becomes possible to flexibly diagnose risks and propose countermeasures according to the user's emotions.

[1201] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1202] Step 1:

[1203] A user uses a terminal to input information security concerns. The input data is sent from the terminal to a server. The input data includes the user's concerns and questions.

[1204] Step 2:

[1205] The server analyzes the received user input data and generates additional interactive questions to gain a more detailed understanding of the user's information security situation. The generated questions are sent to the terminal and displayed to the user.

[1206] Step 3:

[1207] The user answers questions posed by the server through the terminal, and the answer data is sent back to the server from the terminal, allowing the server to collect detailed information about the user's information security.

[1208] Step 4:

[1209] The server analyzes the user's emotions using an emotion engine based on the collected user input data and response data. The emotion engine uses the Transformers library to recognize emotions from the user's text data. The input data is the user's text, and the output data is the recognized emotion.

[1210] Step 5:

[1211] The server performs risk diagnosis based on the recognized emotion. The risk diagnosis means evaluates the risk of information leakage and information incidents based on the user's input data and emotion data. The input data is the user's text and emotion, and the output data is the risk assessment result.

[1212] Step 6:

[1213] The server uses a generative AI model to propose optimal security measures based on the risk assessment results and the user's emotions. The generative AI model uses the OpenAI API to generate specific measures based on the user's situation. The input data is the risk assessment results and emotions, and the output data is the security measure proposals.

[1214] Step 7:

[1215] The server sends the generated security measure proposals to the terminal and displays them to the user. The user can check the proposed measures and implement them as necessary. This allows the user to take concrete actions to strengthen their own information security.

[1216] Example 2

[1217] 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."

[1218] Conventional information security diagnostic systems perform risk assessments and propose countermeasures without considering the user's feelings, which can make it difficult for users to use the system with confidence. Furthermore, risk and vulnerability assessments are sometimes insufficient, which can lead to inadequate security measures. This increases the risk of information leaks and cyber attacks.

[1219] 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.

[1220] In this invention, the server includes an AI-based interactive hearing means, a risk assessment means based on the hearing content, a vulnerability assessment means, an emotion engine means for analyzing the user's emotions, and a means for proposing current risks and optimal security measures. This enables flexible risk assessment and countermeasure proposals based on the user's emotions, allowing users to use the system with peace of mind. Furthermore, by performing comprehensive risk and vulnerability assessments, security measures can be strengthened and the risk of information leaks and cyber attacks can be reduced.

[1221] "AI-based interactive hearing means" refers to a means of using artificial intelligence to converse with users and gather information about their needs and concerns.

[1222] "Risk diagnosis method based on interview content" is a method for evaluating and diagnosing risks related to information security based on interview information collected from users.

[1223] A "vulnerability assessment method" is a method for identifying vulnerabilities in a system or network and evaluating them against known vulnerability information.

[1224] The "emotion engine means for analyzing user emotions" is a means for analyzing emotions from the user's input contents and dialogue, and adjusting risk diagnosis and countermeasure proposals based on the emotions.

[1225] The "means for proposing current risks and optimal security measures" is a means for proposing optimal security measures to users based on the results of risk diagnosis and the results of user sentiment analysis.

[1226] The present invention relates to an information security diagnostic system, and in particular to a system that performs risk diagnosis and proposes countermeasures while taking into account the user's emotions. A specific embodiment of this system will be described below.

[1227] First, the user accesses the system interface using a terminal and enters the information they have gathered interactively. For example, they might say, "I would like to install new software, but I'm worried about security risks."

[1228] The server receives the hearing information sent by the user and begins risk assessment. This risk assessment uses vulnerability scanners such as Nessus and OpenVAS. The server uses these tools to scan system and network vulnerabilities and evaluate the risk of information leakage and cyber attacks.

[1229] The server then performs a vulnerability scan, looking up known vulnerabilities in the CVE database and comparing them with the scan results. For example, if a particular port is open, it checks for vulnerabilities associated with that port.

[1230] Furthermore, the server uses an emotion engine to analyze the user's emotions. The emotion engine analyzes emotions from the user's input and adjusts risk diagnosis and countermeasure suggestions based on those emotions. For example, if a user uses the keyword "worry," the emotion engine recognizes that the user is feeling anxious.

[1231] Finally, the server will suggest the most appropriate security measures based on the user's feelings along with the risk assessment results, such as "When installing new software, we recommend strengthening firewall settings and conducting regular vulnerability scans."

[1232] Examples of prompts include:

[1233] "I want to install new software, but I'm worried about security risks. What measures should I take?"

[1234] In this way, the system diagnoses risks based on user input and proposes appropriate countermeasures. By combining it with an emotion engine, it is possible to respond flexibly to the user's emotions, allowing users to use the system with peace of mind.

[1235] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1236] Step 1:

[1237] The user enters the hearing information.

[1238] The user accesses the system interface using a terminal and inputs information such as, "I want to install new software, but I'm worried about security risks." The input information is sent to the server.

[1239] Step 2:

[1240] The server receives the hearing information and starts risk diagnosis.

[1241] The server receives the information sent by the user. Based on the received information, it assesses the risk of information leakage, cyber-attacks, and internal fraud. This process uses vulnerability scanners such as Nessus and OpenVAS. The input is the information sent by the user, and the output is the risk assessment results.

[1242] Step 3:

[1243] The server performs vulnerability assessment.

[1244] The server performs vulnerability assessment to identify system and network vulnerabilities. Specifically, it references the CVE database and compares the scan results to identify known vulnerabilities. For example, if a specific port is open, it checks for vulnerabilities associated with that port. The input is the scan results, and the output is the vulnerability assessment results.

[1245] Step 4:

[1246] The server uses an emotion engine to analyze the user's emotions.

[1247] The server uses an emotion engine to analyze emotions from user input. For example, if a user inputs "I want to install new software, but I'm worried about security risks," the emotion engine recognizes that the user is feeling anxious. The input is the user's hearing information, and the output is the emotion analysis result.

[1248] Step 5:

[1249] The server presents the risk assessment results and countermeasures to the user.

[1250] The server presents the risk assessment results along with countermeasures according to the user's emotions. For example, if the user feels anxious, the server will suggest reassuring countermeasures such as strengthening firewall settings or conducting regular vulnerability scans. The inputs are the risk assessment results and the emotion analysis results, and the output is countermeasure suggestions.

[1251] (Application example 2)

[1252] 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."

[1253] Conventional information security diagnostic systems diagnose risks and propose countermeasures without considering the user's feelings, which can lead to users feeling anxious or dissatisfied with the proposed countermeasures. Furthermore, the accuracy of risk and vulnerability diagnostics is low, making it difficult to provide optimal security measures.

[1254] 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.

[1255] In this invention, the server includes an AI-based interactive hearing means, a risk diagnosis means based on the hearing content, a vulnerability diagnosis means, an emotion engine means for recognizing the user's emotions, a means for adjusting the risk diagnosis and countermeasure proposals based on the user's emotions, and a means for proposing current risks and optimal security countermeasures. This makes it possible to perform appropriate risk diagnosis and countermeasure proposals according to the user's emotions.

[1256] "AI-based interactive hearing method" is a method that uses artificial intelligence to interact with users and collect information.

[1257] "Risk diagnosis means based on interview content" is a means of analyzing collected interview content and diagnosing risks related to information security.

[1258] A "vulnerability assessment method" is a method for identifying and assessing vulnerabilities in a system or network.

[1259] The "emotion engine means for recognizing user's emotions" is a means using an engine for analyzing and recognizing user's emotions.

[1260] "Means for adjusting risk diagnosis and proposed measures based on user emotions" refers to means for adjusting risk diagnosis and proposed security measures based on the user's recognized emotions.

[1261] "Means for proposing current risks and optimal security measures" refers to means for proposing optimal security measures to users in response to diagnosed risks.

[1262] As an embodiment of the present invention, an information security diagnostic system is configured as follows.

[1263] System configuration

[1264] Hardware

[1265] Server: Performs the central processing of the information security diagnostic system.

[1266] Smartphone: A device that allows users to conduct interactive hearings.

[1267] Network: Infrastructure for communication between the server and smartphones.

[1268] software

[1269] emotion_recognition library: Software for recognizing user emotions.

[1270] risk_assessment library: Software for diagnosing risks based on interview content.

[1271] vulnerability_scanner library: Software for scanning systems and networks for vulnerabilities.

[1272] Program processing

[1273] Interviews and risk assessments

[1274] The server receives the interview content from the user via smartphone, which is then analyzed by a risk assessment tool to determine the risk of information leakage and information incidents.

[1275] emotion recognition

[1276] The server uses the emotion_recognition library to recognize the user's emotions. For example, if a user types, "I feel like there have been an increasing number of phishing emails lately," the server will recognize the user's emotion as "surprise" based on the content.

[1277] Vulnerability Assessment

[1278] The server uses the vulnerability_scanner library to scan and identify system and network vulnerabilities.

[1279] Countermeasure proposals

[1280] The server adjusts the results of risk and vulnerability assessments based on the user's emotions and suggests optimal security measures. For example, if the user's emotion is "surprise," the server will emphasize and suggest measures against unexpected risks.

[1281] Specific examples

[1282] If a user enters "I feel like there have been an increasing number of phishing emails lately," the server will process the following:

[1283] 1. Receive hearing: Receive user input.

[1284] 2. Emotion Recognition: Use the emotion_recognition library to recognize the user's emotion as "surprise."

[1285] 3. Risk assessment: Use the risk_assessment library to assess the risk of phishing emails.

[1286] 4. Vulnerability Assessment: Scan for email filtering vulnerabilities using the vulnerability_scanner library.

[1287] 5. Propose countermeasures: Based on the perceived emotions, propose countermeasures for unexpected risks.

[1288] Prompt Sentence Examples

[1289] User Input: I feel like there's been an increase in phishing emails lately.

[1290] Emotion Recognition: Surprise

[1291] Risk Assessment: Risk of Phishing Emails

[1292] Vulnerability Assessment: Email Filtering Vulnerabilities

[1293] Countermeasure proposals: Countermeasures against unexpected risks: Risks of phishing emails, vulnerabilities in email filtering

[1294] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1295] Step 1:

[1296] A user launches an information security assessment application on their smartphone. The user inputs security concerns and questions through the application's interactive interface. The input data is then sent to the server.

[1297] Input: User security concerns or questions

[1298] Output: User input data sent to the server

[1299] Step 2:

[1300] The server analyzes the received user input data and recognizes the user's emotion using the emotion_recognition library. For example, if a user inputs "I feel like there have been an increasing number of phishing emails lately," the server will recognize the user's emotion as "surprise" from the content.

[1301] Input: User-entered data

[1302] Output: Recognized user emotion (e.g., surprise)

[1303] Step 3:

[1304] The server uses the risk_assessment library to diagnose information security risks based on user input data, for example, identifying risks to phishing emails and assessing their risk level.

[1305] Input: User-entered data

[1306] Output: Assessed risk (e.g., risk of phishing emails)

[1307] Step 4:

[1308] The server uses the vulnerability_scanner library to scan and identify system and network vulnerabilities, for example to detect email filtering vulnerabilities.

[1309] Input: User input data and system state

[1310] Output: Identified vulnerabilities (e.g. email filtering vulnerabilities)

[1311] Step 5:

[1312] The server then proposes optimal security measures based on the user's perceived emotions and the diagnosed risks and vulnerabilities. For example, if the user's emotion is "surprise," the server will propose measures that emphasize unexpected risks.

[1313] Input: Perceived user sentiment, diagnosed risks, identified vulnerabilities

[1314] Output: Proposed security measures (e.g., measures against unexpected risks)

[1315] Step 6:

[1316] The server sends the proposed security measures to the user's smartphone and displays them to the user, who can then review the measures and implement them as necessary.

[1317] Input: Proposed security measures

[1318] Output: Security measures displayed on the user's smartphone

[1319] Through the above steps, the information security diagnostic system can perform appropriate risk diagnosis and propose countermeasures according to the user's emotions.

[1320] Example 3

[1321] 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."

[1322] While conventional information security diagnostic systems can perform risk and vulnerability assessments, they have the problem of being unable to propose countermeasures that take into account the user's feelings. Another issue is that when proposing optimal security measures, it is difficult to provide flexible options that correspond to the user's situation. Furthermore, due to a lack of functionality to automatically generate specific countermeasures using generative AI models, there are limitations to the quality and applicability of the countermeasures.

[1323] 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.

[1324] In this invention, the server includes an AI-based interactive hearing means, a risk assessment means based on the hearing content, a vulnerability assessment means, a means for proposing current risks and optimal security measures, a means for generating security measures by inputting prompt sentences into a generative AI model, a means for recognizing user emotions and adjusting measures based on those emotions, and a means for presenting measures in the form of a "pine, bamboo, plum" system. This makes it possible to propose flexible and appropriate security measures that take user emotions into consideration.

[1325] "AI-based interactive hearing method" is a method of collecting information through dialogue with users using artificial intelligence.

[1326] "Risk diagnosis means based on interview content" is a means of evaluating the risk of a system based on collected information.

[1327] A "vulnerability assessment means" is a means for detecting potential vulnerabilities within a system and assessing their impact.

[1328] "Means for proposing current risks and optimal security measures" refers to means for evaluating current risks based on the results of the diagnosis and proposing optimal security measures.

[1329] "Means for generating security measures by inputting prompt statements into a generative AI model" refers to means for inputting specific instruction statements into a generative AI model and automatically generating specific security measures based on those statements.

[1330] "Means for recognizing user emotions and adjusting countermeasures based on those emotions" refers to means for analyzing user emotions and adjusting the security countermeasures proposed based on the results.

[1331] "Means for presenting measures in the form of a 'pine, bamboo, plum' system" refers to a means for presenting multiple options in the form of a 'pine, bamboo, plum' system, allowing the user to select the most appropriate measure according to their own situation.

[1332] MODE FOR CARRYING OUT THE INVENTION

[1333] The information security diagnostic system of the present invention has the following configuration and processing procedure.

[1334] System configuration

[1335] This system consists of three main components: a server, a terminal, and a user. The server performs risk assessment, vulnerability assessment, security countermeasure generation using a generative AI model, and emotion recognition and countermeasure adjustment using an emotion engine. The terminal provides an interface for users to access the system and select countermeasures. Users log in to the system, provide information interactively, and select proposed countermeasures.

[1336] Hardware and software used

[1337] A server is a computer system with a powerful processor and large memory capacity. Specifically, it uses the following software:

[1338] Vulnerability scanners: Nessus, OpenVAS

[1339] Emotion engine: Microsoft Azure Emotion API, IBM Watson Tone Analyzer

[1340] Generative AI models: Advanced generative AI models such as GPT-3

[1341] The terminal is a personal computer or smartphone that a user accesses, and a web browser is installed on it, allowing the user to access the server via the Internet.

[1342] Program processing

[1343] The server first conducts an interactive interview when the user logs in to the system, collecting information about the user's system. Next, it performs risk and vulnerability assessment based on the collected information. This is done using vulnerability scanners such as Nessus and OpenVAS.

[1344] After analyzing the diagnostic results, the server inputs prompts into the generative AI model to generate optimal security measures. For example, the following prompts can be used:

[1345] "After conducting a risk assessment of our company's systems, we discovered vulnerabilities such as port 80 being open, passwords being too easy, and employees having low security awareness. Please propose the optimal security measures based on a balanced approach."

[1346] The generative AI model suggests specific countermeasures based on this prompt.

[1347] Furthermore, the server uses an emotion engine to recognize the user's emotions. For example, if the user is feeling sad, the server recognizes that emotion and adjusts the proposed measures accordingly. This enables the server to propose flexible and appropriate security measures that take the user's emotions into consideration.

[1348] The terminal displays suggested security measures to the user. The measures are presented in a "pine, bamboo, and plum" format, and the user can select the most appropriate one based on their own situation. The selected measure is then sent to the server and implemented.

[1349] Specific examples

[1350] For example, suppose a risk assessment of a company's systems reveals the following vulnerabilities:

[1351] Port 80 is open

[1352] The password is too simple

[1353] Employees have low security awareness

[1354] In this case, the server will suggest the following solutions:

[1355] Matsu: Change firewall settings and close port 80

[1356] Bamboo: Strengthen password policies and require complex passwords

[1357] Ume: Provide security training to employees

[1358] The user selects the most appropriate countermeasure from the presented three countermeasures, and the server executes the selected countermeasure. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1359] Step 1:

[1360] A user logs in to the system.

[1361] The user opens a browser on the terminal and accesses the system's login page. He enters his username and password and clicks the "Login" button. The entered username and password are sent to the server, and the server performs authentication. If authentication is successful, the user can access the dashboard. The user's authentication status and the dashboard screen are displayed as output.

[1362] Step 2:

[1363] The server performs risk assessment and vulnerability assessment.

[1364] The server uses vulnerability scanners such as Nessus and OpenVAS to perform risk and vulnerability assessments. As input, the user's system information is used. This scans for potential vulnerabilities in the system and assesses the risk level. For example, it checks whether a specific port is open or whether the password is strong enough. As output, the assessment results are generated and stored in the server's database.

[1365] Step 3:

[1366] The server analyzes the diagnostic results.

[1367] The server analyzes the results of risk and vulnerability assessments. The assessment result data is used as input. The analysis includes storing the assessment result data in a database and identifying the type of risk and its severity. For example, if port 80 is open, it evaluates how much risk it poses. The analysis result is generated as output.

[1368] Step 4:

[1369] The server generates optimal security measures by inputting a prompt into the AI ​​model to generate suggestions.

[1370] The server inputs a prompt sentence into the generative AI model based on the analysis results to generate the optimal security measures. The following prompt sentences are used as input:

[1371] "After conducting a risk assessment of our company's systems, we discovered vulnerabilities such as port 80 being open, passwords being too easy, and employees having low security awareness. Please propose the optimal security measures based on a balanced approach."

[1372] The generative AI model then proposes specific countermeasures based on this prompt, and generates the proposed security countermeasures as an output.

[1373] Step 5:

[1374] The server uses an emotion engine to recognize the user's emotion.

[1375] When a user accesses the dashboard, the server uses an emotion engine to analyze the user's emotions in real time. The inputs are the user's facial expressions and text input. For example, when the user shows an expression through the camera, the emotion engine analyzes the expression and determines whether the user is sad or not. The output is the user's emotional state.

[1376] Step 6:

[1377] The server adjusts the measures based on the emotion.

[1378] The server adjusts the generated security measures based on the user's recognized emotions. As input, the user's emotional state and the generated security measures are used. For example, if the user is sad, the explanation of the measures is presented in a gentler, more understandable way. As output, the adjusted security measures are generated.

[1379] Step 7:

[1380] The device presents countermeasures to the user.

[1381] The terminal displays the proposed security measures to the user. The adjusted security measures are used as input. The measures are displayed in a Pine, Bamboo, and Plum format, and a detailed description of each measure is also included. The user can select a measure by comparing its advantages and disadvantages. As output, a selection screen for measures is displayed.

[1382] Step 8:

[1383] The user selects a measure.

[1384] The user selects the most appropriate countermeasure from the presented three options. The user's choice is used as input. The selected countermeasure is sent to the server, which starts the procedure to implement the countermeasure. For example, if a firewall setting change is selected, the server automatically executes a script to change the settings. The output is that the selected countermeasure is executed.

[1385] (Application example 3)

[1386] 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."

[1387] Conventional information security diagnostic systems can perform risk and vulnerability assessments, but they have the problem of not being able to propose countermeasures that take into account the user's emotions. In particular, when a user is feeling anxious or excited, there is a need for the system to propose appropriate countermeasures that take into account those emotions.

[1388] 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 an AI-based interactive hearing means, a risk assessment means based on the hearing content, a vulnerability assessment means, a means for proposing current risks and optimal security measures, an emotion recognition means, and a means for adjusting the proposed measures based on the emotion. This makes it possible to propose optimal security measures according to the user's emotions.

[1389] "AI-based interactive hearing method" is a method that uses artificial intelligence to interact with users and collect information.

[1390] "Risk diagnosis method based on interview content" is a method for diagnosing information security risks based on the collected interview content.

[1391] A "vulnerability assessment tool" is a tool for identifying and assessing vulnerabilities in systems and devices.

[1392] "Means for proposing current risks and optimal security measures" refers to means for evaluating current risks based on the results of the diagnosis and proposing optimal security measures.

[1393] The "emotion recognition means" is a means for recognizing and analyzing the user's emotions.

[1394] The "means for adjusting proposed measures based on emotions" is a means for adjusting the content of proposed security measures according to the recognized emotions of the user.

[1395] The system for implementing the present invention uses the following hardware and software.

[1396] Hardware

[1397] Smartphone (camera, microphone, sensor)

[1398] software

[1399] Emotion Recognition Library (emotion_recognition)

[1400] Risk and vulnerability assessment library (security_diagnosis)

[1401] Conversational AI engine

[1402] System configuration

[1403] The server includes an AI-based interactive hearing means, a risk diagnosis means based on the hearing content, a vulnerability diagnosis means, a means for proposing current risks and optimal security measures, an emotion recognition means, and a means for adjusting proposed measures based on emotions.

[1404] Processing flow

[1405] 1. Interactive interview: The user uses a smartphone to interact with the AI ​​engine, which answers the user's security-related questions and collects the necessary information.

[1406] 2. Risk Assessment and Vulnerability Assessment: Based on the collected information, the risk assessment and vulnerability assessment library assesses security risks.

[1407] 3. Emotion Recognition: Using the smartphone's camera and microphone, the emotion recognition library analyzes the user's emotions.

[1408] 4. Adjustment of suggested measures: Based on the results of emotion recognition, the suggested measures are adjusted. For example, if the user is feeling anxious, the system will prioritize suggesting measures that will give the user a sense of security.

[1409] 5. Proposal of optimal security measures: Finally, the current risks and optimal security measures are presented to the user in the form of a "Sho-Take-Ume" system.

[1410] Specific examples

[1411] When a user performs a security check on their smartphone and some vulnerabilities are discovered, the emotion engine recognizes the user's anxiety and suggests measures to reassure them (e.g., simple security setting changes).

[1412] Prompt Sentence Examples

[1413] "Perform a security diagnosis on the smartphone and suggest the best solution based on the user's emotions. If the user is feeling anxious, suggest a solution that will reassure them."

[1414] In this way, it becomes possible to propose optimal security measures according to the user's feelings.

[1415] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1416] Step 1:

[1417] The user interacts with the AI ​​engine using a smartphone. The user answers security questions and provides the required information. The input is the user's answer, and the output is the collected information. The server stores this information in a database.

[1418] Step 2:

[1419] The server performs risk and vulnerability assessment based on the interview content. The input is the interview content, and the output is a risk assessment report and a vulnerability assessment report. The risk and vulnerability assessment library generates these reports.

[1420] Step 3:

[1421] The user's smartphone collects the user's emotional data using a camera and microphone. The input is the user's facial expression and voice data, and the output is emotional data analyzed by an emotion recognition library. The server receives this emotional data and stores the analysis results.

[1422] Step 4:

[1423] The server proposes optimal security measures based on the risk assessment report, vulnerability assessment report, and emotion data. The inputs are the risk assessment report, vulnerability assessment report, and emotion data, and the output is proposed measures. A means for adjusting the proposed measures based on emotion selects measures that correspond to the user's emotion.

[1424] Step 5:

[1425] The server presents the optimal security measures to the user in the form of a "Sho-Take-Ume" system. The input is the proposed measures, and the output is the options presented to the user. The user selects the measures that best suit their own situation from the presented measures.

[1426] Step 6:

[1427] The server provides specific instructions for implementing the measures selected by the user. The input is the user's selection, and the output is specific implementation instructions. The server sends this to the user's smartphone and helps the user implement the measures.

[1428] In this way, it becomes possible to propose and implement optimal security measures according to the user's feelings.

[1429] 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.

[1430] 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.

[1431] Another example of generative AI is Gemini (internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[1432] 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.

[1433] [Third embodiment]

[1434] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1435] 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.

[1436] 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).

[1437] 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.

[1438] 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.

[1439] 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).

[1440] 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.

[1441] 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.

[1442] 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.

[1443] 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.

[1444] 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.

[1445] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1446] "Example 1"

[1447] The information security diagnostic system of the present invention has an AI-based interactive hearing means. Specifically, the system grasps the user's information security situation and concerns through dialogue with the user. This hearing covers a wide range of topics, such as the user's current information security management situation, past information leak cases, and whether or not they currently have security measures in place.

[1448] "Example 2"

[1449] The system of the present invention also includes risk assessment means and vulnerability assessment means based on the content of interviews. Specifically, information security risks are assessed based on the results of the interviews. This assessment comprehensively assesses various risks, such as the risk of information leakage, the risk of cyber attacks, and the risk of internal fraud. Furthermore, vulnerability assessment is performed to identify vulnerabilities in the system and network.

[1450] "Example 3"

[1451] Furthermore, the system of the present invention has a means for proposing current risks and optimal security measures. Specifically, optimal security measures are proposed based on the results of risk and vulnerability assessments. These proposals include specific measures such as installing a firewall, reviewing password policies, and providing security training to employees. The proposed measures are presented in a "pine, bamboo, and plum" format, allowing users to select the measures that best suit their own situation.

[1452] The processing flow of each embodiment will be described below.

[1453] "Example 1"

[1454] Step 1: Initiate a dialogue with the user using an AI-powered conversational listening method to understand the user's information security situation and concerns.

[1455] Step 2: Based on the results of the interviews, assess information security risks. This assessment comprehensively assesses various risks, including the risk of information leaks, cyber attacks, and internal fraud.

[1456] Step 3: Further vulnerability assessment is performed to identify system and network vulnerabilities. Step 4: Based on the results of the risk and vulnerability assessment, optimal security measures are proposed. These proposals include specific measures such as the introduction of firewalls, review of password policies, and security education for employees.

[1457] "Example 2"

[1458] Step 1: Initiate a dialogue with the user using an AI-powered conversational listening method to understand the user's information security situation and concerns.

[1459] Step 2: Based on the results of the interviews, assess information security risks. This assessment comprehensively assesses various risks, including the risk of information leaks, cyber attacks, and internal fraud.

[1460] Step 3: Further vulnerability assessment is performed to identify system and network vulnerabilities. Step 4: Based on the results of the risk and vulnerability assessment, optimal security measures are proposed. These proposals include specific measures such as the introduction of firewalls, review of password policies, and security education for employees.

[1461] "Example 3"

[1462] Step 1: Initiate a dialogue with the user using an AI-powered conversational listening method to understand the user's information security situation and concerns.

[1463] Step 2: Based on the results of the interviews, assess information security risks. This assessment comprehensively assesses various risks, including the risk of information leaks, cyber attacks, and internal fraud.

[1464] Step 3: Further vulnerability assessment is performed to identify system and network vulnerabilities. Step 4: Based on the results of the risk and vulnerability assessment, optimal security measures are proposed. These proposals include specific measures such as the introduction of firewalls, review of password policies, and security education for employees.

[1465] Example 1

[1466] 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."

[1467] Conventional information security diagnostic systems have difficulty fully understanding users' information security situations and concerns, and often do not provide appropriate risk assessments or suggest security measures. As a result, users are unable to accurately understand their current information security situation and are unable to take appropriate measures.

[1468] 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.

[1469] In this invention, the server includes means for generating an initial question for the user using a generative AI model, means for the user to answer the question using a terminal and send the answer to the server, means for the server to analyze the user's answer and generate a next question, means for the server to analyze all answers and evaluate information security, and means to feed back the evaluation results to the user. This makes it possible to grasp the user's information security situation and concerns in detail, and to diagnose appropriate risks and propose security measures.

[1470] A "generative AI model" is an artificial intelligence technology that generates appropriate questions through dialogue with users and analyzes their answers.

[1471] The "server" is a computer system that receives input from users, generates questions using a generative AI model, analyzes the answers, and evaluates information security.

[1472] A "terminal" is a device through which a user answers questions and transmits the answers to a server.

[1473] "Means for generating questions" is a function that uses a generative AI model to create appropriate questions for users.

[1474] The "means for sending an answer" is a function for sending an answer entered by a user using a terminal to a server.

[1475] The "means for analyzing answers" is a function that allows the server to analyze the user's answers and generate the next question.

[1476] The "means for evaluating information security" is a function in which the server analyzes all responses and evaluates the current state of information security for the user.

[1477] "Means for feeding back evaluation results" is a function by which the server provides the results of information security evaluation to the user.

[1478] This invention relates to an information security diagnostic system that uses a generative AI model to understand information security situations and concerns through dialogue with users, and then performs appropriate risk diagnosis and proposes security measures.

[1479] First, the server uses a generative AI model to generate initial questions for the user. For example, OpenAI's GPT-4 can be used as this generative AI model. The generated questions are intended to collect basic information about the user's information security.

[1480] The user answers questions sent from the server using a terminal. The terminal can be a PC, smartphone, tablet, etc. The answers entered by the user are sent from the terminal to the server. This communication is performed using a secure protocol (e.g., HTTPS).

[1481] The server analyzes the answers received from the user and generates the next question to gather the necessary information. This process is done automatically using a generative AI model. For example, if the user answers, "Customer data was leaked in June of last year," the server generates the next question: "What caused the data leak?"

[1482] The user then uses the device to answer the next question. This process is repeated until all the necessary information is collected. The server analyzes all the collected answers and evaluates the user's current information security status. This evaluation uses an AI model to identify risks and propose countermeasures.

[1483] The server provides feedback on the evaluation results to the user, providing specific advice such as, "Your current security measures are insufficient, and additional measures are required."

[1484] As a concrete example, the following prompt sentence could be input to a generative AI model:

[1485] Example prompt sentence:

[1486] "Has your organization experienced any data breaches in the past year? If so, please provide details."

[1487] In this way, users can objectively grasp their own information security situation and take necessary measures. This system enables effective management of information security risks and the implementation of appropriate measures.

[1488] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1489] Step 1:

[1490] A user accesses the system.

[1491] Users access the information security assessment system using a web browser or a dedicated application. They log in by entering their user ID and password. The input is the user ID and password, and the output is a message indicating whether the login was successful or failed.

[1492] Step 2:

[1493] The server generates an initial question for the user.

[1494] The server uses a generative AI model to generate an initial question for the user, for example, "Has your organization experienced a data breach in the past year?" The input is the user's login information, and the output is the generated question.

[1495] Step 3:

[1496] The user answers the question.

[1497] The user uses the terminal to answer questions sent by the server, for example, "Yes, customer data was leaked in June of last year." The input is the question from the server, and the output is the user's answer.

[1498] Step 4:

[1499] The terminal sends the user's answer to the server.

[1500] The terminal transmits the answers entered by the user to the server in real time. This communication is performed using a secure protocol (e.g., HTTPS). The input is the user's answer, and the output is a transmission completion message to the server.

[1501] Step 5:

[1502] The server analyzes the answer and generates the next question.

[1503] The server analyzes the user's answer and generates a question to gather the necessary information. For example, it generates a question like, "What was the cause of the information leak?" The input is the user's answer, and the output is the next question.

[1504] Step 6:

[1505] The user answers the following questions:

[1506] The user uses the terminal to answer the next question sent from the server. For example, the user might answer, "The cause was unauthorized access by an insider." The input is the next question from the server, and the output is the user's answer.

[1507] Step 7:

[1508] The terminal sends the user's answer to the server.

[1509] The terminal again sends the answer entered by the user to the server. This process is repeated until all the necessary information is collected. The input is the user's answer, and the output is a transmission completion message to the server.

[1510] Step 8:

[1511] The server analyzes all responses and evaluates the information security.

[1512] The server analyzes all collected responses and evaluates the current state of information security for the user. This evaluation uses an AI model to identify risks and propose countermeasures. The input is all user responses, and the output is the evaluation result.

[1513] Step 9:

[1514] The server provides feedback of the evaluation results to the user.

[1515] The server provides the evaluation results as feedback to the user, for example, providing specific advice such as "your current security measures are insufficient and additional measures are required." The input is the evaluation results, and the output is a feedback message to the user.

[1516] (Application example 1)

[1517] 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."

[1518] Conventional information security diagnostic systems were unable to fully grasp users' information security situations and concerns, making it difficult to propose appropriate security measures. Furthermore, when assessing security risks based on user responses, there was a lack of means to utilize generative AI models, resulting in low accuracy in risk assessments.

[1519] 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.

[1520] In this invention, the server includes an AI-based interactive hearing means, a risk assessment means based on the hearing content, a vulnerability assessment means, a means for proposing current risks and optimal security measures, a means for evaluating security risks using a generative AI model based on the user's answers and generating prompt sentences, and a means for proposing appropriate security measures based on the generated prompt sentences. This makes it possible to grasp the user's information security situation and concerns in detail, and to utilize the generative AI model to perform highly accurate security risk assessments and propose appropriate measures.

[1521] "AI-based interactive hearing method" is a method that uses artificial intelligence to interact with users and gather information about situations and concerns regarding information security.

[1522] "Risk diagnosis method based on interview content" is a method for evaluating information security risks based on information collected from users.

[1523] A "vulnerability assessment method" is a method for detecting and evaluating the vulnerabilities of a system or network.

[1524] "Means for proposing current risks and optimal security measures" refers to a means for analyzing the current risk situation and proposing optimal security measures to users.

[1525] "Means for assessing security risks using a generative AI model and generating prompt sentences" refers to means for utilizing a generative AI model to analyze user responses, assess security risks, and generate prompt sentences based on the results.

[1526] "Means for suggesting appropriate security measures based on generated prompt sentences" refers to means for suggesting appropriate security measures to users based on prompt sentences generated by a generative AI model.

[1527] The following system configuration is used as an embodiment of the present invention: The system is composed of three main elements: a server, a terminal, and a user.

[1528] The server includes an AI-based interactive hearing means, a risk assessment means based on the hearing content, a vulnerability assessment means, a means for proposing current risks and optimal security measures, a means for evaluating security risks using a generative AI model and generating prompt text, and a means for proposing appropriate security measures based on the generated prompt text.

[1529] The terminal is a device used by the user, such as a smartphone or PC, that communicates with the server to send and receive information. The user interacts with the server through the terminal and inputs the status and concerns regarding information security.

[1530] The server first gathers information from the user through an AI dialogue, which may include questions such as:

[1531] Please tell us about your current information security management situation.

[1532] Have there been any cases of data breaches in the past?

[1533] What security measures do you currently have in place?

[1534] The user's answers are sent from the terminal to the server, and the server performs risk diagnosis based on the answers. The risk diagnosis means analyzes the user's answers and evaluates the risk of information leakage and information incidents.

[1535] Next, the server uses vulnerability assessment tools to detect and evaluate system and network vulnerabilities, thereby clarifying the current risks.

[1536] The server then uses a generative AI model to further analyze the user's answers and assess their security risks. Based on this assessment, the server generates a prompt. For example, the following prompt might be generated:

[1537] Based on the information below, please assess the security risks and suggest appropriate countermeasures.

[1538] We have anti-virus software installed on all of our company's computers.

[1539] Two years ago, an employee's password was leaked.

[1540] We encourage you to change your password regularly.

[1541] Based on the generated prompt, the server will suggest appropriate security measures, such as updating antivirus software, strengthening password management, and conducting regular security training.

[1542] In this way, users can receive specific and practical security measures. The server uses generative AI models (e.g., OpenAI's GPT-3) to perform highly accurate risk assessments and propose countermeasures, effectively improving users' information security.

[1543] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1544] Step 1:

[1545] The server collects information from the user in an AI-driven dialogue format. Specifically, the server sends the user questions such as, "Please tell us about the current status of information security management." The user answers these questions via their device, and the answers are sent to the server. The input is the user's answer, and the output is the collected information.

[1546] Step 2:

[1547] The server analyzes the user's answers using risk assessment means based on the interview content and evaluates information security risks. Specifically, the server analyzes the user's answers using natural language processing technology and identifies information leakage risks and information incident risks. The input is the user's answers, and the output is the risk assessment results.

[1548] Step 3:

[1549] The server uses vulnerability assessment tools to detect and assess system and network vulnerabilities. Specifically, the server compares the results with an existing vulnerability database to identify vulnerabilities that may exist in the user's system. The input is the user's system information, and the output is the vulnerability assessment results.

[1550] Step 4:

[1551] The server uses the generative AI model to further analyze the user's answers and evaluate security risks. Specifically, the server inputs the user's answers as prompts into the generative AI model to obtain risk assessment results. The inputs are the user's answers and the generative AI model, and the outputs are prompts and risk assessment results.

[1552] Step 5:

[1553] The server proposes appropriate security measures based on the generated prompt. Specifically, the server proposes specific security measures to the user based on the risk assessment results obtained from the generative AI model. Examples include updating antivirus software, strengthening password management, and conducting regular security training. The input is the prompt and the risk assessment results, and the output is proposed security measures.

[1554] Step 6:

[1555] The user receives the security measures proposed by the server and implements them. Specifically, the user checks the proposed measures through the terminal and implements the necessary measures. The input is the security measures proposed by the server, and the output is the implemented security measures.

[1556] In this way, servers, terminals, and users can work together to evaluate information security risks and propose and implement appropriate countermeasures.

[1557] Example 2

[1558] 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."

[1559] In conventional information security assessment systems, analysis of interview content, risk assessment, and vulnerability assessment are often performed manually, which is inefficient. In addition, the assessment results are often inaccurate and incomplete, making it difficult to propose optimal security measures.

[1560] 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.

[1561] In this invention, the server includes a means for a user to input the interview content, a means for the server to analyze the interview content, a means for the server to diagnose risks using a generative AI model, a means for the server to diagnose vulnerabilities using a vulnerability scanner, and a means for the server to provide the diagnosis results to the user. This enables efficient analysis of the interview content and highly accurate risk and vulnerability diagnosis, and makes it possible to propose optimal security measures.

[1562] The "means for the user to input the hearing contents" refers to the means by which the user inputs the hearing contents through the system interface.

[1563] The "means for the server to analyze the hearing content" refers to the means by which the server analyzes the hearing content sent from the user and extracts important keywords and phrases.

[1564] "Means for the server to diagnose risks using a generative AI model" refers to means for the server to use a generative AI model to diagnose information security risks based on the content of the interview.

[1565] "Means for a server to diagnose vulnerabilities using a vulnerability scanner" refers to means for a server to use a vulnerability scanner to diagnose vulnerabilities in a system or network.

[1566] The "means by which the server provides the diagnosis results to the user" refers to the means by which the server integrates the risk diagnosis results and the vulnerability diagnosis results and provides them to the user.

[1567] The present invention relates to an information security diagnostic system, in which a user inputs interview details, a server analyzes the details, performs risk and vulnerability diagnostics, and provides the diagnostic results to the user.

[1568] System configuration

[1569] A means for users to input their hearing details

[1570] A user can use a web form or a dedicated application to describe in detail the current system status and concerns. For example, the user accesses a web form, enters information in the text boxes for "current system status" and "concerns," and clicks the "Submit" button.

[1571] The server's means of analyzing the hearing content

[1572] The server receives the audio recordings sent by the user and analyzes them using natural language processing (NLP) techniques, specifically by tokenizing the text using NLP libraries (e.g., spaCy or NLTK) and extracting important keywords and phrases.

[1573] How the server uses the generative AI model to diagnose risk

[1574] The server diagnoses risk using a generative AI model (e.g., OpenAI's GPT-4) based on the analyzed interview content. It generates prompt sentences and inputs them into the AI ​​model to obtain the results of risk diagnosis.

[1575] As a concrete example, consider the following situation where a user enters the following information:

[1576] "Our system is frequently accessed from outside, and our internal database contains confidential information. We are also concerned about whether employee access rights are being properly managed."

[1577] In this case, the server will input the following prompt into the generative AI model:

[1578] "Please assess information security risks based on the following interview content. Interview content: 'Our system is frequently accessed from outside, and our internal database contains confidential information. We are also concerned about whether employee access privileges are being properly managed.'"

[1579] The generative AI model diagnoses the risk of information leaks, cyber attacks, and internal fraud, and returns the results to the server.

[1580] How the server uses a vulnerability scanner to diagnose vulnerabilities

[1581] The server uses a vulnerability scanner (e.g., Nessus) to assess system and network vulnerabilities. The scanner scans the entire system and identifies potential vulnerabilities, such as out-of-date software or improper access privilege settings.

[1582] A means by which the server provides the diagnostic results to the user

[1583] The server integrates the results of risk assessment and vulnerability assessment and provides them to the user. The results are displayed in the form of a dashboard or report. The user can access the dashboard and check the assessment results.

[1584] In this way, it becomes possible to efficiently analyze the interview content and perform highly accurate risk and vulnerability diagnosis, thereby proposing optimal security measures.

[1585] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1586] Step 1:

[1587] The user inputs the details of the hearing.

[1588] Specifically, the user accesses a web form or dedicated application, enters information in the text boxes for "current system status" and "concerns," and clicks the "Submit" button.

[1589] Input: Hearing contents entered by the user (text data)

[1590] Output: Interview content (text data) sent to the server

[1591] Step 2:

[1592] The server analyzes the hearing content.

[1593] The server receives the audio recordings sent by the user and analyzes them using natural language processing (NLP) techniques, specifically by tokenizing the text using NLP libraries (e.g., spaCy or NLTK) and extracting important keywords and phrases.

[1594] Input: Interview details sent by the user (text data)

[1595] Output: Analyzed keywords and phrases (text data)

[1596] Step 3:

[1597] The server uses generative AI models to diagnose risks.

[1598] The server diagnoses risk using a generative AI model (e.g., OpenAI's GPT-4) based on the analyzed interview content. The server generates prompt sentences and inputs them into the AI ​​model to obtain the results of risk diagnosis.

[1599] Specifically, the server generates a prompt sentence and inputs it into the generative AI model. It receives a response from the generative AI model and obtains the risk assessment result.

[1600] Input: Analyzed keywords and phrases (text data), generated prompt sentences

[1601] Output: Risk diagnosis results (text data)

[1602] Step 4:

[1603] The server uses a vulnerability scanner to check for vulnerabilities.

[1604] The server uses a vulnerability scanner (e.g., Nessus) to assess system and network vulnerabilities. The scanner scans the entire system and identifies potential vulnerabilities.

[1605] Specifically, the server launches a vulnerability scanner, provides information about the systems and networks to be scanned, receives the scan results, and generates a list of vulnerabilities.

[1606] Input: Information about the system or network you want to scan

[1607] Output: Vulnerability diagnosis results (text data)

[1608] Step 5:

[1609] The server provides the diagnostic results to the user.

[1610] The server integrates the results of risk assessment and vulnerability assessment and provides them to the user. The results are displayed in the form of a dashboard or report.

[1611] Specifically, the server integrates the risk assessment results and vulnerability assessment results and displays them in a user-friendly format (e.g., graphs and tables). Users can access the dashboard and check the assessment results.

[1612] Input: Risk assessment results, vulnerability assessment results

[1613] Output: Diagnostic results provided to the user (dashboard and report format)

[1614] (Application example 2)

[1615] 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."

[1616] In today's information society, information security risks are diverse and have become a serious issue for companies and individuals. In particular, risks such as information leaks, cyber attacks, and internal fraud can cause serious damage if appropriate measures are not taken. However, there are only a limited number of systems that can comprehensively diagnose these risks and propose appropriate countermeasures. In addition, there is a lack of functionality to identify specific vulnerabilities based on the diagnosis results and provide them as a report. This makes it difficult for users to grasp the full picture of risks and take appropriate countermeasures.

[1617] 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.

[1618] In this invention, the server includes an AI-based interactive hearing means, a risk diagnosis means based on the hearing content, a vulnerability diagnosis means, a means for proposing current risks and optimal security measures, a means for identifying system and network vulnerabilities based on the diagnosis results, and a means for generating a report of the risk diagnosis results and vulnerability diagnosis results, thereby enabling users to comprehensively diagnose information security risks, identify specific vulnerabilities, and take appropriate measures.

[1619] "AI-based interactive hearing method" is a method of collecting information from users in an interactive manner using artificial intelligence.

[1620] "Risk diagnosis method based on interview content" is a method for diagnosing information security risks based on collected interview content.

[1621] A "vulnerability assessment method" is a method for identifying vulnerabilities in a system or network.

[1622] "Means for proposing current risks and optimal security measures" refers to means for proposing optimal security measures for diagnosed risks.

[1623] "Means for identifying vulnerabilities in a system or network based on the results of the diagnosis" refers to means for identifying specific vulnerabilities in a system or network based on the results of the risk diagnosis.

[1624] The "means for generating a report of the risk diagnosis result and the vulnerability diagnosis result" is a means for outputting the risk diagnosis result and the vulnerability diagnosis result together in a report format.

[1625] To implement the present invention, the following system configuration and processing procedures are required.

[1626] System Configuration

[1627] 1. Hardware:

[1628] Smartphone: A device that allows users to listen.

[1629] Server: A central processing unit for performing risk and vulnerability assessments.

[1630] 2. Software:

[1631] Python: A programming language for implementing risk and vulnerability assessment logic.

[1632] JSON: A data format for outputting diagnostic results in report format.

[1633] Processing steps

[1634] 1. Hearing:

[1635] The smartphone application conducts an interactive interview with the user, who then inputs information about risks such as information leaks and cyber attacks.

[1636] 2. Risk Assessment:

[1637] The server diagnoses information security risks based on the interview results, specifically assessing the risk of information leaks, cyber attacks, and internal fraud.

[1638] 3. Vulnerability Assessment:

[1639] The server identifies system and network vulnerabilities based on the results of the risk assessment, such as insufficient data encryption or an unconfigured firewall.

[1640] 4. Proposed measures:

[1641] The server proposes the most appropriate security measures for the assessed risks and vulnerabilities, presenting multiple countermeasure options (e.g., basic, intermediate, advanced).

[1642] 5. Report Generation:

[1643] The server compiles the risk assessment results and vulnerability assessment results and outputs them in a report format. This report is generated in JSON format and provided to the user.

[1644] Specific examples

[1645] The user launches the smartphone application and inputs interview data about risks related to "information leakage" and "cyber attacks." Based on this, the server identifies vulnerabilities such as "insufficient data encryption" and "no firewall configured" and generates a report.

[1646] Prompt Sentence Examples

[1647] User: My system is at risk of data leakage and cyber attacks.

[1648] App: Information leakage risk identified. Insufficient data encryption.

[1649] App: Cyber ​​attack risk identified. Firewall not configured.

[1650] App: The results of the risk assessment and vulnerability assessment were generated as a report.

[1651] In this way, users can comprehensively diagnose information security risks, identify specific vulnerabilities, and take appropriate measures.

[1652] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1653] Step 1:

[1654] The user launches the smartphone application and begins the hearing. The user enters information about risks such as information leaks and cyber attacks. The entered data is sent to the server as the hearing content.

[1655] Input: Information about risks entered by users (e.g., information leaks, cyber attacks)

[1656] Output: The hearing is sent to the server

[1657] Step 2:

[1658] The server performs risk assessment based on the received interview content. Specifically, it evaluates the risk of information leakage, cyber attack risk, internal fraud risk, etc. The results of the risk assessment are saved as risk assessment results.

[1659] Input: Hearing details

[1660] Output: Risk diagnosis results

[1661] Step 3:

[1662] The server identifies system and network vulnerabilities based on the risk assessment results. For example, it identifies specific vulnerabilities such as insufficient data encryption or an unconfigured firewall. The vulnerability assessment results are saved as vulnerability assessment results.

[1663] Input: Risk diagnosis results

[1664] Output: Vulnerability assessment results

[1665] Step 4:

[1666] The server proposes optimal security measures for the diagnosed risks and vulnerabilities. It presents multiple countermeasure options (e.g., basic, intermediate, advanced). The countermeasure proposal results are saved as countermeasure proposal results.

[1667] Input: Risk assessment results, vulnerability assessment results

[1668] Output: Countermeasure proposal results

[1669] Step 5:

[1670] The server compiles the risk assessment results and vulnerability assessment results and outputs them in a report format. This report is generated in JSON format and provided to the user. The user can check the report through a smartphone application.

[1671] Input: Risk assessment results, vulnerability assessment results, countermeasure proposal results

[1672] Output: Report (JSON format)

[1673] In this way, users can comprehensively diagnose information security risks, identify specific vulnerabilities, and take appropriate measures.

[1674] Example 3

[1675] 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."

[1676] In today's information security environment, companies and organizations face a variety of risks and vulnerabilities. It is extremely important to properly assess these risks and propose optimal security measures. However, current systems face the challenge of effectively analyzing the results of risk and vulnerability assessments and presenting measures in a format that is easy for users to understand. In particular, there is a lack of support for users to select appropriate measures based on their own situation.

[1677] 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.

[1678] In this invention, the server includes an AI-based interactive hearing means, a risk assessment means based on the hearing content, a vulnerability assessment means, a means for storing the assessment results in a database, a means for analyzing the stored assessment results, a means for proposing current risks and optimal security measures, and a means for presenting the proposed measures in a pine, bamboo, and plum format. This allows the user to accurately grasp the current risks based on the assessment results and select the optimal security measures according to their own situation.

[1679] "AI-based interactive hearing means" is a means of using artificial intelligence to converse with users and collect necessary information.

[1680] "Risk diagnosis method based on interview content" is a method for evaluating the risk of a system or network based on collected information.

[1681] A "vulnerability assessment method" is a method for detecting and evaluating the vulnerabilities of a system or network.

[1682] The "means for storing the diagnosis results in a database" refers to a means for storing the results of risk diagnosis and vulnerability diagnosis in a database.

[1683] The "means for analyzing stored diagnostic results" is a means for analyzing the diagnostic results stored in the database and evaluating the current risk.

[1684] "Means for proposing current risks and optimal security measures" refers to a means for generating and proposing optimal security measures based on the analysis results.

[1685] The "means for presenting proposed measures in the form of a 'pine, bamboo, plum' system" is a means for presenting generated security measures in the form of a 'pine, bamboo, plum' system so that the user can easily select one.

[1686] MODE FOR CARRYING OUT THE INVENTION

[1687] This invention relates to an information security diagnostic system, and in particular to a system that proposes optimal security measures based on the results of risk and vulnerability diagnostics. This system operates in cooperation with three parties: a server, a terminal, and a user.

[1688] 1. Program Generation

[1689] The server generates a program that proposes optimal security measures based on the results of risk and vulnerability assessments. This program is developed using programming languages ​​such as Python and Java.

[1690] 2. Program processing explanation

[1691] The server collects the results of risk and vulnerability assessments and stores them in a database. The server then analyzes the stored data and evaluates the current risk. This evaluation uses machine learning models (such as Scikit-learn and TensorFlow).

[1692] Based on the evaluation results, the server proposes optimal security measures, such as installing a firewall, reviewing password policies, and providing security training to employees. The proposed measures are presented in a "pine, bamboo, and plum" format, allowing users to select the one that best suits their situation.

[1693] 3. Examples of concrete examples and prompts

[1694] For example, suppose a company conducts a risk and vulnerability assessment and finds the following risks:

[1695] High risk of external attacks on internal networks

[1696] Weak password

[1697] Employees have low security awareness

[1698] In this case, the server will suggest the following security measures:

[1699] Matsu: Implementing a high-performance firewall, setting strong password policies, and providing security training to all employees.

[1700] Take: Implementing a mid-range firewall, reviewing password policies, and providing security training to key employees.

[1701] Ume: Implementing basic firewalls, simple review of password policies, and distributing security awareness materials.

[1702] Example prompts to input to a generative AI model:

[1703] Please suggest optimal security measures based on the results of the company's risk and vulnerability assessment. The following risks have been identified:

[1704] High risk of external attacks on internal networks

[1705] Weak password

[1706] Employees have low security awareness

[1707] Please make your proposal in the form of pine, bamboo, and plum."

[1708] In this way, the user can select the most suitable security measure according to his / her own situation. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1709] Step 1:

[1710] The server uses AI to conduct dialogue with the user. As input, it receives information provided by the user (such as the system configuration, software used, and security policies). As output, it generates data for risk and vulnerability assessment based on the interview content. Specifically, the server uses natural language processing technology to analyze the user's responses and extract the necessary information.

[1711] Step 2:

[1712] The server performs risk diagnosis based on the interview content. It uses the data obtained in step 1 as input. It generates risk assessment results as output. Specifically, the server uses the risk assessment model to assess the risk of information leakage and information incidents.

[1713] Step 3:

[1714] The server performs vulnerability assessment. It uses the data obtained in step 1 as input and generates vulnerability assessment results as output. Specifically, the server uses a network scanning tool (e.g., Nessus or OpenVAS) to detect system vulnerabilities.

[1715] Step 4:

[1716] The server saves the results of the risk assessment and vulnerability assessment in a database. As input, it uses the assessment results obtained in steps 2 and 3. As output, it generates the assessment results stored in the database. Specifically, the server obtains the assessment results in JSON format and inserts them into the database.

[1717] Step 5:

[1718] The server analyzes the stored diagnostic results. As input, it uses the diagnostic results stored in the database. As output, it generates a current risk assessment result. Specifically, the server uses a machine learning model (e.g., Scikit-learn or TensorFlow) to score the severity of the risk.

[1719] Step 6:

[1720] The server proposes optimal security measures based on the analysis results. The risk assessment results obtained in step 5 are used as input. Security measure proposals are generated as output. Specifically, the server prioritizes measures based on the risk score and generates specific measures such as installing a firewall, reviewing password policies, and providing security training.

[1721] Step 7:

[1722] The terminal presents the generated security measures to the user. As input, it uses the security measure proposals generated in step 6. As output, it generates the security measures selected by the user. Specifically, the terminal displays the proposed measures in the form of a pine, bamboo, and plum tree through the user interface and sends the user's selection to the server.

[1723] Step 8:

[1724] The user selects the most appropriate security measure from the presented security measures. As input, the security measures presented in step 7 are used. As output, the selected security measure is sent to the server. In concrete terms, the user operates the terminal interface to select the most appropriate measure.

[1725] (Application example 3)

[1726] 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."

[1727] In today's information society, personal and corporate devices and networks are exposed to a variety of risks. The risks of information leaks and information incidents are particularly serious, making it necessary to properly diagnose these risks and propose optimal security measures. However, current security assessment systems lack specific assessments based on the user's device information and comprehensive recommendations based on the assessment results. This makes it difficult for users to select the optimal security measures for their own situation.

[1728] 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 an AI-based interactive hearing means, a risk assessment means based on the hearing content, a vulnerability assessment means, a means for proposing current risks and optimal security measures, a means for performing risk and vulnerability assessment based on the user's device information, a means for proposing security measures in the form of a pine, bamboo, and plum system based on the assessment results, and a means for displaying the proposed security measures to the user. This enables the user to specifically assess the risks of their own device and network and select optimal security measures.

[1729] "AI-based interactive hearing method" is a method that uses artificial intelligence to interact with users and collect necessary information.

[1730] "Risk diagnosis method based on interview content" is a method for evaluating potential risks based on collected information.

[1731] A "vulnerability assessment tool" is a tool for identifying and assessing vulnerabilities in systems and devices.

[1732] "Means for proposing current risks and optimal security measures" refers to a means for analyzing the current risk situation and proposing optimal security measures for that situation.

[1733] "Means for diagnosing risks and vulnerabilities based on user device information" refers to means for diagnosing risks and vulnerabilities using information about a user's device.

[1734] "Means for proposing security measures in the form of a 'Sho, Take, Plum' hierarchy based on diagnostic results" refers to means for proposing security measures of different levels in the form of a 'Sho, Take, Plum' hierarchy based on diagnostic results.

[1735] The "means for displaying the proposed security measures to the user" refers to a means for visually displaying the proposed security measures to the user.

[1736] A system for carrying out this invention has the following configuration: The server includes means for conducting an interactive interview using AI, means for diagnosing risks and vulnerabilities based on the interview content, means for proposing current risks and optimal security measures, means for diagnosing risks and vulnerabilities based on the user's device information, means for proposing security measures in the form of a pine, bamboo, and plum system based on the results of the assessment, and means for displaying the proposed security measures to the user.

[1737] Program processing explanation

[1738] The server first collects necessary information from the user through an AI-based dialogue. This includes the user's device information (OS, version, installed apps, etc.). The collected information is then analyzed by risk assessment and vulnerability assessment methods. The risk assessment method evaluates the risk of information leakage and information incidents, while the vulnerability assessment method identifies vulnerabilities in the system and device.

[1739] Next, the server proposes the current risks and optimal security measures based on the results of the diagnosis. The proposed measures are presented in the form of a "pine, bamboo, and plum" system, allowing users to select the measures that best suit their own situation. This allows users to specifically diagnose the risks to their devices and networks and select the optimal security measures.

[1740] Hardware and software used

[1741] Hardware: Server, user's smartphone or computer

[1742] Software: AI models (e.g., natural language processing models), APIs for risk and vulnerability assessment, and applications that provide user interfaces

[1743] Specific examples

[1744] For example, if a user is using Android 10 and has installed apps "App1", "App2", and "App3", the server will do the following:

[1745] 1. Collect user device information.

[1746] 2. Use risk assessment tools and vulnerability assessment tools to assess information leakage risks and information incident risks and identify device vulnerabilities.

[1747] 3. Based on the diagnostic results, we propose security measures in the form of a 3-pronged approach.

[1748] 4. Display the proposed security measures to the user.

[1749] Prompt Sentence Examples

[1750] An example of a prompt sentence to input to the generative AI model is as follows:

[1751] Please create a program that performs risk and vulnerability assessments based on the user's device information and proposes optimal security measures. The proposed measures should be presented in a "pine, bamboo, and plum" format. Please use the following device information:

[1752] OS: Android

[1753] Version: 10

[1754] Installed Apps: ["App1", "App2", "App3"]

[1755] In this way, the detailed description provides specific measures for enhancing the security of users' devices and networks.

[1756] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1757] Step 1:

[1758] The server collects necessary information from the user through an AI-based dialogue. Specifically, it receives the user's device information (OS, version, installed apps, etc.) as input and stores it in a database. The input information serves as the basis for subsequent risk and vulnerability assessments.

[1759] Step 2:

[1760] The server analyzes the collected device information using a risk assessment method based on the interview content. Specifically, it receives the device information as input and applies a risk assessment algorithm to evaluate the risk of information leakage and information incidents. The output is a risk level (e.g., low, medium, or high).

[1761] Step 3:

[1762] The server uses a vulnerability assessment tool to identify device vulnerabilities. Specifically, it receives device information as input and compares it with a database of known vulnerabilities. The output is a list of identified vulnerabilities and their severity.

[1763] Step 4:

[1764] The server proposes the current risk and optimal security measures based on the results of risk and vulnerability assessment. Specifically, it receives the risk level and vulnerability list as input and applies a security measure proposal algorithm. The output is a list of proposed security measures in the form of a "pine, bamboo, and plum" system.

[1765] Step 5:

[1766] The server proposes security measures based on the diagnosis results in a "Sho-Take-Ume" format. Specifically, it receives a list of proposed security measures as input and generates data to visually display to the user. As an output, it obtains a selection of security measures that are displayed in the user interface.

[1767] Step 6:

[1768] The user checks the proposed security measures and selects the most appropriate one based on their own situation. Specifically, the user receives the security measure options provided by the server as input and sends the selected measure to the server. As output, the selected security measure is recorded on the server.

[1769] Step 7:

[1770] The server provides specific procedures for implementing the security measures selected by the user. Specifically, it receives the selected security measures as input and generates the execution procedures. As an output, it obtains a list of execution procedures to be provided to the user.

[1771] In this way, the server specifically diagnoses the risks to the user's device and network and assists in selecting and implementing the most appropriate security measures.

[1772] 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.

[1773] "Example 1"

[1774] One embodiment of the present invention is an information security diagnostic system that incorporates an emotion engine. This system diagnoses information security risks through dialogue with the user and proposes optimal security measures. In particular, the emotion engine recognizes the user's emotions and adjusts the risk diagnosis and proposed measures based on those emotions. For example, if the user is feeling anxious, the emotion engine recognizes that anxiety and reduces the user's anxiety by providing a more detailed explanation and proposing specific measures. Also, if the user is feeling angry, the emotion engine recognizes that anger, identifies the cause of the problem, and proposes a solution. In this way, the information security diagnostic system that incorporates an emotion engine can provide appropriate measures that correspond to the user's emotions.

[1775] "Example 2"

[1776] Another embodiment of the present invention is an information security diagnostic system that incorporates an emotion engine. This system diagnoses information security risks through dialogue with the user and proposes optimal security measures. In particular, the emotion engine recognizes the user's emotions and adjusts the risk diagnosis and proposed measures based on those emotions. For example, if the user is feeling happy, the emotion engine recognizes that joy and proposes measures that will allow the user to use the system with peace of mind. Also, if the user is feeling surprised, the emotion engine recognizes that surprise and proposes measures to address unexpected risks. In this way, the information security diagnostic system that incorporates an emotion engine can provide appropriate measures that correspond to the user's emotions.

[1777] "Example 3"

[1778] A further embodiment of the present invention is an information security diagnostic system that incorporates an emotion engine. This system diagnoses information security risks through dialogue with the user and proposes optimal security measures. In particular, the emotion engine recognizes the user's emotions and adjusts the risk diagnosis and proposed measures based on those emotions. For example, if the user is feeling sad, the emotion engine recognizes that sadness and proposes measures that take the user's feelings into consideration. Also, if the user is excited, the emotion engine recognizes that excitement and proposes measures that allow the user to use the system safely. In this way, the information security diagnostic system that incorporates an emotion engine can provide appropriate measures that correspond to the user's emotions.

[1779] The processing flow of each embodiment will be described below.

[1780] "Example 1"

[1781] Step 1: Start a dialogue with the user. This dialogue is for information security risk assessment.

[1782] Step 2: The emotion engine recognizes the user's emotions, which are inferred from the user's statements and actions.

[1783] Step 3: The emotion engine adjusts the risk assessment results and suggested measures based on the emotions it recognizes. For example, if the user feels anxious, it will suggest more detailed explanations and specific measures.

[1784] "Example 2"

[1785] Step 1: Start a dialogue with the user. This dialogue is for information security risk assessment.

[1786] Step 2: The emotion engine recognizes the user's emotions, which are inferred from the user's statements and actions.

[1787] Step 3: The emotion engine adjusts the risk assessment results and proposed measures based on the emotions it recognizes. For example, if the user is feeling happy, it will propose measures that will allow the user to use the system with peace of mind.

[1788] "Example 3"

[1789] Step 1: Start a dialogue with the user. This dialogue is for information security risk assessment.

[1790] Step 2: The emotion engine recognizes the user's emotions, which are inferred from the user's statements and actions.

[1791] Step 3: Based on the emotions recognized by the emotion engine, the risk assessment results and suggested measures are adjusted. For example, if the user is feeling sad, the system will suggest measures that take into account the user's feelings.

[1792] Example 1

[1793] 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."

[1794] Conventional information security diagnostic systems diagnose risks and propose countermeasures without considering the user's feelings, which means that they are unable to fully alleviate users' anxiety and anger. Another issue is that the countermeasure proposals are uniform, making it difficult to respond flexibly to the user's specific situation.

[1795] 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.

[1796] In this invention, the server includes an AI-based interactive hearing means, a risk diagnosis means based on the hearing content, an emotion recognition means, a means ...

Claims

1. A dialogue-based hearing method using AI, A risk assessment method based on the contents of the interview; A vulnerability diagnostic means for detecting vulnerabilities; An emotion engine means for analyzing an emotion of a user; a means for proposing current risks and optimal security measures for the vulnerabilities detected by the vulnerability diagnosis means; A system including:

2. The system according to claim 1 , wherein the risk diagnosis means diagnoses an information leakage risk and an information incident risk.

3. The system of claim 1 , wherein the means for suggesting optimal security measures adjusts the measures based on user sentiment.

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