system

Abstract

The system according to this embodiment aims to respond quickly and comprehensively to security risk alerts that are updated daily. [Solution] The system according to the embodiment comprises an analysis unit, a search unit, a proposal unit, and a policy proposal unit. The analysis unit analyzes security risk alerts that are updated daily. The search unit performs a full search of the company's systems based on the information analyzed by the analysis unit to determine if there are any risks. The proposal unit proposes countermeasures for the risks identified by the search unit. The policy proposal unit proposes a response policy to management based on the countermeasures proposed by the proposal unit.

Description

Technical Field

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[0001] ​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​The system according to this embodiment comprises an analysis unit, a search unit, a proposal unit, and a policy proposal unit. The analysis unit analyzes security risk alerts that are updated daily. The search unit performs a full search of the company's systems based on the information analyzed by the analysis unit to determine if there are any risks. The proposal unit proposes countermeasures for the risks identified by the search unit. The policy proposal unit proposes a response policy to management based on the countermeasures proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can respond quickly and comprehensively to security risk alerts that are updated daily. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applicable to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the 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. [[ID=I6]]

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. 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. Also, the database 24 and the communication I / F 26 are 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).

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The comprehensive security consulting system according to an embodiment of the present invention is a system that uses a generating AI to analyze security risk alerts that are updated daily and performs a full search for risks in a company's systems. This system provides consulting on responses when security incidents (including concerns and warning signs) occur. The response consulting ranges from detailed on-site analysis to management decision support consulting for senior management. For example, the generating AI analyzes security risk alerts that are updated daily. In this process, the generating AI extracts vulnerability information and identifies cases that require company action. For example, it determines whether a newly discovered vulnerability affects the company's systems. This information is provided to security personnel, enabling a rapid response. Next, it provides consulting on responses when system alerts occur. The generating AI analyzes the content of the system alert and proposes appropriate countermeasures. For example, if an anomaly occurs in a specific system, it identifies the cause and proposes a method of correction. This information is provided to on-site security personnel, enabling a rapid response. Furthermore, it provides consulting on response policies to senior management. The generating AI evaluates the impact of security risks and proposes appropriate response policies to senior management. For example, it evaluates the impact of a specific risk on the entire company and proposes strategies to mitigate that risk. This information supports management decision-making and improves the overall security level of the company. This reduces the workload of security personnel and enables a rapid and appropriate response to security risks. Furthermore, it provides management with appropriate information, improving the overall security level of the company. For example, by having the AI ​​analyze vulnerability information and identify issues requiring company-wide action, security personnel can quickly implement countermeasures. Also, when a system alert occurs, the AI ​​proposes appropriate countermeasures, allowing on-site security personnel to respond quickly. In addition, the AI ​​assesses the impact of security risks and proposes appropriate response strategies for management, enabling them to make quick and appropriate decisions. In this way, the comprehensive security consulting system can improve the overall security level of the company.

[0029] The security comprehensive consulting system according to this embodiment comprises an analysis unit, a search unit, a proposal unit, and a policy proposal unit. The analysis unit analyzes security risk alerts that are updated daily. The analysis unit analyzes security risk alerts using, for example, a generation AI and extracts vulnerability information. The analysis unit uses, for example, a generation AI to determine whether newly discovered vulnerabilities affect the company's systems. The analysis unit uses, for example, a generation AI to analyze the content of security risk alerts and provides it to security personnel. The search unit performs a full search of the company's systems based on the information analyzed by the analysis unit to determine if there are any risks. The search unit uses, for example, a generation AI to scan the entire company's systems and identify risks. The search unit uses, for example, a generation AI to search a specific database and identify risks. The search unit uses, for example, a generation AI to scan the entire system and identify risks. The proposal unit proposes countermeasures for the risks identified by the search unit. The proposal unit uses, for example, a generation AI to propose appropriate countermeasures for the risks. The proposal unit uses, for example, a generation AI to analyze the content of system alerts and propose appropriate countermeasures. The proposal department, for example, identifies the cause of an anomaly in a specific system when a generation AI occurs and proposes a corrective method. The policy proposal department proposes a response policy to management based on the countermeasures proposed by the proposal department. The policy proposal department, for example, uses generation AI to evaluate the impact of security risks and proposes an appropriate response policy to management. The policy proposal department, for example, uses generation AI to evaluate the impact of a specific risk on the entire company and proposes a strategy to mitigate that risk. The policy proposal department, for example, uses generation AI to evaluate the impact of security risks and proposes an appropriate response policy to management. As a result, the comprehensive security consulting system according to the embodiment can consistently perform everything from analyzing security risk alerts to proposing countermeasures and proposing policies to management.

[0030] The analysis department analyzes security risk alerts that are updated daily. For example, the analysis department uses generative AI to analyze security risk alerts and extract vulnerability information. Specifically, the generative AI utilizes natural language processing technology to analyze the content of security risk alerts in detail. For instance, the generative AI receives text data from security risk alerts as input and extracts keywords and phrases related to vulnerabilities. Furthermore, based on these keywords and phrases, the generative AI identifies the type of vulnerability and its scope of impact. The analysis department, for example, determines whether a newly discovered vulnerability by the generative AI affects the company's systems. The generative AI compares the vulnerability information with the system's configuration information and evaluates the impact. For example, the generative AI retrieves the system's software version and configuration information from a database and compares it with the vulnerability information to identify potentially affected parts. The analysis department, for example, provides the analysis of the security risk alert content by the generative AI to security personnel. The generative AI generates a report of the analysis results in an easy-to-understand format and notifies security personnel. The report includes detailed vulnerability information, scope of impact, and recommended countermeasures. This allows the analysis unit to quickly and accurately analyze security risk alerts and provide valuable information to security personnel. Furthermore, the analysis unit can improve analysis accuracy by accumulating data from past security risk alerts and having the generating AI learn from this data. As a result, the analysis unit can always perform highly accurate analyses based on the latest information and support rapid responses to security risks.

[0031] The search unit performs a full scan of the company's systems to check for risks based on the information analyzed by the analysis unit. For example, the search unit uses generative AI to scan the entire company's systems and identify risks. Specifically, the generative AI analyzes the system's configuration information and log data to detect potential risks. The generative AI thoroughly investigates the state and settings of each system component to identify vulnerabilities and abnormal behavior. For example, the generative AI monitors network traffic to detect suspicious communication patterns and abnormal data transfers. It also analyzes system logs to identify abnormal login attempts and unauthorized access. The search unit, for example, uses generative AI to search specific databases to identify risks. The generative AI analyzes the database structure and content to detect vulnerable settings and unauthorized data manipulation. For example, the generative AI checks database access permissions to identify inappropriate permission settings and unauthorized access. It also analyzes database query logs to detect abnormal query patterns and unauthorized data manipulation. The search unit, for example, uses generative AI to scan the entire system and identify risks. The generating AI centrally manages system-wide configuration information and log data, and performs real-time scans. This allows the search unit to quickly and accurately identify risks across the entire system and provide appropriate information to security personnel. Furthermore, based on the scan results, the search unit can evaluate risk priorities and determine the urgency of the response. This enables the search unit to efficiently and effectively identify risks and improve the overall security of the system.

[0032] The proposal department proposes countermeasures for risks identified by the search department. For example, the proposal department uses generative AI to propose appropriate countermeasures for risks. Specifically, the generative AI refers to a database of past countermeasures and identifies effective countermeasures for similar risks. The generative AI proposes the optimal countermeasure depending on the type of risk and the scope of impact. For example, the generative AI proposes patching procedures and configuration change methods for specific vulnerabilities. The generative AI also proposes emergency and long-term countermeasures to minimize the impact of the risk. For example, the proposal department uses the generative AI to analyze the content of system alerts and propose appropriate countermeasures. The generative AI analyzes the content of system alerts in detail and identifies the cause and impact of the alert. For example, if the cause of the alert is abnormal operation of a specific component, the generative AI proposes a method for correcting or reconfiguring that component. The generative AI also evaluates the scope of impact of the alert and proposes methods for protecting affected systems and data. For example, if the generative AI detects an anomaly in a specific system, the proposal department identifies the cause and proposes a corrective method. The generative AI analyzes system log data and configuration information to identify the cause of the anomaly. For example, the generation AI can identify misconfigurations of specific components or software bugs and propose solutions. It can also suggest preventative measures and monitoring methods to prevent the recurrence of anomalies. This allows the proposal unit to quickly propose appropriate countermeasures for risks identified by the search unit, thereby improving system security. Furthermore, the proposal unit can evaluate the effectiveness of the proposed countermeasures and propose improvements as needed. This ensures that the proposal unit consistently provides optimal solutions and maintains system security.

[0033] The Policy Proposal Department proposes response policies to management based on the countermeasures proposed by the Proposal Department. For example, the Policy Proposal Department uses generative AI to assess the impact of security risks and proposes appropriate response policies to management. Specifically, the generative AI assesses the scope of risk impact and economic losses, and explains the importance of the risk to management. The generative AI quantitatively evaluates the impact of the risk and creates a report in a format easily understood by management. For example, the generative AI calculates the amount of loss and recovery costs in the event of a risk and presents this to management. The generative AI also proposes strategies and policies to mitigate the impact of the risk. For example, the Policy Proposal Department uses generative AI to assess the impact of a specific risk on the entire company and proposes strategies to mitigate that risk. When assessing the impact of a risk, the generative AI considers the importance of the company's business processes and assets. For example, the generative AI assesses the impact of a specific risk on the company's key business processes and proposes specific measures to mitigate that risk. The generative AI also proposes resource allocation and priorities to minimize the impact of the risk. For example, the Policy Proposal Department uses generative AI to assess the impact of security risks and proposes appropriate response policies to management. Generative AI utilizes historical data and statistical information to predict the probability and scope of risk occurrence when assessing the impact of risks. For example, based on data from past security incidents, the generative AI predicts the likelihood of a specific risk occurring and its scope of impact, explaining the importance of risk management to management. The generative AI also proposes long-term strategies and policies to mitigate the impact of risks, supporting management in taking appropriate responses to risks. As a result, the policy proposal department can propose appropriate response policies to management based on the countermeasures proposed by the department, thereby improving security throughout the company. Furthermore, the policy proposal department can monitor the implementation status of the proposed policies and propose improvement measures as needed. This allows the policy proposal department to always provide optimal response policies and maintain security throughout the company.

[0034] The analysis unit can extract vulnerability information and identify cases requiring in-house action. For example, the analysis unit extracts vulnerability information using generative AI. For example, the generative AI retrieves vulnerability information from a CVE database and determines whether it affects the company's systems. For example, the generative AI analyzes security vendor reports and identifies cases requiring in-house action. This allows for rapid response by extracting vulnerability information and identifying cases requiring in-house action.

[0035] The proposal department can analyze the content of system alerts and propose appropriate countermeasures. For example, the proposal department uses a generation AI to analyze the content of system alerts. For example, the proposal department's generation AI analyzes anomaly detection alerts and proposes appropriate countermeasures. For example, the proposal department's generation AI analyzes login failure alerts and proposes appropriate countermeasures. For example, the proposal department's generation AI analyzes resource overload alerts and proposes appropriate countermeasures. This enables a rapid response by analyzing the content of system alerts and proposing appropriate countermeasures.

[0036] The policy proposal department can assess the impact of security risks and propose appropriate response strategies to management. For example, the policy proposal department can use generative AI to assess the impact of security risks. For example, the policy proposal department can use generative AI to assess the impact on business and propose appropriate response strategies. For example, the policy proposal department can use generative AI to assess the risk of data breaches and propose appropriate response strategies. For example, the policy proposal department can use generative AI to assess the legal implications and propose appropriate response strategies. In this way, by assessing the impact of security risks and proposing appropriate response strategies to management, the security level of the entire company is improved.

[0037] The proposal department can identify the cause of an anomaly in a specific system and propose a corrective method. For example, the proposal department can use generative AI to identify the cause of the anomaly. For example, the proposal department can use generative AI to identify the cause of a system crash and propose a corrective method. For example, the proposal department can use generative AI to identify the cause of a data inconsistency and propose a corrective method. For example, the proposal department can use generative AI to identify the cause of a performance degradation and propose a corrective method. This enables rapid problem resolution by identifying the cause of an anomaly in a specific system and proposing a corrective method.

[0038] The policy proposal department can assess the impact of a specific risk on the entire company and propose strategies to mitigate that risk. For example, the policy proposal department can use generative AI to assess the impact of a risk. For example, the policy proposal department can use generative AI to assess the impact on the business and propose strategies to mitigate the risk. For example, the policy proposal department can use generative AI to assess the risk of data breaches and propose strategies to mitigate the risk. For example, the policy proposal department can use generative AI to assess the legal implications and propose strategies to mitigate the risk. This improves overall risk management within the company by assessing the impact of a specific risk on the entire company and proposing strategies to mitigate that risk.

[0039] The analysis unit can improve the accuracy of its analysis of security risk alerts by referring to historical risk data. For example, the analysis unit can refer to historical risk data using a generation AI. For example, the analysis unit can use a generation AI to refer to historical security incident data and identify similar risk alerts. For example, the analysis unit can use a generation AI to analyze historical log data and extract specific patterns. For example, the analysis unit can use a generation AI to refer to historical reports and perform analysis considering the frequency of risk occurrences. In this way, the accuracy of the analysis is improved by referring to historical risk data.

[0040] The analysis unit can determine the priority of analysis based on the frequency of risk alerts during the analysis process. For example, the analysis unit measures the frequency of risk alerts using a generation AI. For example, the generation AI measures the frequency of risk alerts based on daily, weekly, and monthly occurrences. For example, the analysis unit prioritizes analyzing risk alerts that occur frequently, based on the generation AI. For example, the analysis unit postpones analyzing risk alerts that occur infrequently, based on the generation AI. For example, the analysis unit adjusts the analysis schedule based on the frequency of occurrence, based on the generation AI. In this way, by determining the priority of analysis based on the frequency of risk alerts, important risks can be analyzed preferentially.

[0041] The analysis unit can perform analysis while considering the geographical distribution of risk alerts. For example, the analysis unit can obtain the geographical distribution of risk alerts using a generation AI. For example, the analysis unit can obtain the geographical distribution of risk alerts based on country-specific, regional, and city-specific distributions using a generation AI. For example, the analysis unit can identify the areas where risk alerts occur and perform analysis specific to those areas using a generation AI. For example, the analysis unit can determine the priority of risk alerts based on geographical distribution using a generation AI. For example, the analysis unit can adjust the analysis schedule using a generation AI that takes geographical distribution into consideration. This makes it possible to perform analysis on region-specific risks by considering the geographical distribution of risk alerts.

[0042] The analysis unit can improve the accuracy of its analysis by referring to relevant literature related to risk alerts during the analysis process. For example, the analysis unit uses a generation AI to refer to relevant literature related to risk alerts. For example, the generation AI in the analysis unit refers to relevant literature such as academic papers, technical reports, and white papers. For example, the generation AI in the analysis unit refers to literature related to risk alerts and incorporates it into the analysis. For example, the generation AI in the analysis unit obtains detailed information about risk alerts from relevant literature and utilizes it in the analysis. For example, the generation AI in the analysis unit identifies the cause of the risk alert based on the relevant literature and incorporates it into the analysis. In this way, the accuracy of the analysis is improved by referring to relevant literature.

[0043] The search unit can optimize its search algorithm by referring to past search history during a search. For example, the search unit may use generative AI to refer to past search history. For example, the generative AI may refer to past search history based on search queries and click history of search results. For example, the generative AI may select the optimal search algorithm based on past search history. For example, the generative AI may extract specific patterns from past search history and reflect them in the search algorithm. For example, the generative AI may refer to past search history to improve the accuracy of search results. As a result, by referring to past search history, the search algorithm is optimized and search accuracy is improved.

[0044] The search unit can determine search priorities based on the severity of the risks during a search. For example, the search unit uses generative AI to evaluate the severity of risks. For example, the generative AI evaluates the severity of risks based on factors such as business impact, data breach risk, and legal impact. For example, the search unit prioritizes searches for risks with high severity based on the generative AI. For example, the search unit postpones searches for risks with low severity based on the generative AI. For example, the search unit adjusts the search schedule based on severity based on the generative AI. In this way, by determining search priorities based on the severity of risks, important risks can be prioritized in the search.

[0045] The search unit can perform searches while considering the geographical distribution of risks. For example, the search unit can obtain the geographical distribution of risks using generative AI. For example, the search unit can obtain the geographical distribution of risks based on country-specific, regional, and city-specific distributions using generative AI. For example, the search unit can identify areas where risks occur and perform searches specific to those areas using generative AI. For example, the search unit can determine the priority of risks based on geographical distribution using generative AI. For example, the search unit can adjust the search schedule considering geographical distribution using generative AI. This makes it possible to search for region-specific risks by considering the geographical distribution of risks.

[0046] The search unit can improve the accuracy of its search by referencing relevant literature on risks during the search process. For example, the search unit uses generative AI to reference relevant literature on risks. For example, the generative AI references relevant literature such as academic papers, technical reports, and white papers. For example, the generative AI references literature related to risks and incorporates it into the search. For example, the generative AI obtains detailed information about risks from the relevant literature and utilizes it in the search. For example, the generative AI identifies the causes of risks based on the relevant literature and incorporates it into the search. As a result, the accuracy of the search is improved by referencing relevant literature.

[0047] The proposal unit can apply different proposal algorithms depending on the risk category when making a proposal. For example, the proposal unit classifies risk categories using generative AI. For example, the proposal unit classifies risks based on categories such as technical risk, business risk, and legal risk using generative AI. For example, the proposal unit selects the optimal proposal algorithm according to the risk category using generative AI. For example, the proposal unit improves the accuracy of proposals by applying different proposal algorithms for each category using generative AI. For example, the proposal unit determines the priority of proposals based on the risk category using generative AI. This improves the accuracy of proposals by applying the optimal proposal algorithm according to the risk category.

[0048] The proposal department can prioritize proposals based on the timing of risk occurrence. For example, the proposal department can use generative AI to evaluate the timing of risk occurrence. For example, the generative AI can evaluate the timing of risk occurrence based on past incident data and predictive models. For example, the generative AI can prioritize proposing risks that are close to their occurrence. For example, the generative AI can postpone proposing risks that are far off. For example, the generative AI can adjust the proposal schedule based on the occurrence timing. This allows for rapid proposals for important risks by prioritizing proposals based on the timing of risk occurrence.

[0049] The proposal department can adjust the order of proposals based on the relevance of risks during the proposal process. For example, the proposal department uses generative AI to evaluate the relevance of risks. For example, the generative AI evaluates the relevance of risks based on common causes, overlapping scope of impact, temporal relevance, etc. For example, the proposal department prioritizes proposing risks with high relevance based on the generative AI. For example, the proposal department postpones proposing risks with low relevance based on the generative AI. For example, the proposal department adjusts the proposal schedule based on relevance based on the generative AI. In this way, by adjusting the order of proposals based on the relevance of risks, proposals are given priority to highly relevant risks.

[0050] The policy proposal department can improve the accuracy of its policy proposals by referring to past risk data. For example, the policy proposal department uses a generative AI to refer to past risk data. For example, the generative AI in the policy proposal department refers to past security incident data, log data, reports, etc. For example, the generative AI in the policy proposal department makes optimal policy proposals based on past risk data. For example, the generative AI in the policy proposal department extracts specific patterns from past risk data and reflects them in the policy proposals. For example, the generative AI in the policy proposal department refers to past risk data to improve the accuracy of policy proposals. In this way, the accuracy of policy proposals is improved by referring to past risk data.

[0051] The policy proposal department can prioritize policy proposals based on the impact of the risks when proposing policies. For example, the policy proposal department uses generative AI to evaluate the impact of risks. For example, the generative AI evaluates the impact of risks based on factors such as business impact, data breach risk, and legal impact. For example, the generative AI prioritizes proposing policies for risks with a high impact. For example, the generative AI postpones proposing policies for risks with a low impact. For example, the generative AI adjusts the policy proposal schedule based on the impact. In this way, by prioritizing policy proposals based on the impact of risks, policies are prioritized for important risks.

[0052] The policy proposal department can propose policies while considering the geographical distribution of risks. For example, the policy proposal department can obtain the geographical distribution of risks using generative AI. For example, the generative AI can obtain the geographical distribution of risks based on country-specific, regional, and city-specific distributions. For example, the generative AI can identify areas where risks occur and propose policies specific to those areas. For example, the generative AI can determine the priority of risks based on geographical distribution. For example, the generative AI can adjust the policy proposal schedule while considering geographical distribution. As a result, by considering the geographical distribution of risks, it becomes possible to propose policies for risks specific to a particular region.

[0053] The policy proposal department can improve the accuracy of its policy proposals by referring to relevant risk literature during the policy proposal process. For example, the policy proposal department can use generative AI to refer to relevant risk literature. For example, the generative AI can refer to relevant literature such as academic papers, technical reports, and white papers. For example, the generative AI can refer to risk-related literature and reflect it in the policy proposal. For example, the generative AI can obtain detailed risk information from relevant literature and utilize it in the policy proposal. For example, the generative AI can identify the causes of risk based on relevant literature and reflect it in the policy proposal. In this way, the accuracy of policy proposals is improved by referring to relevant literature.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The search unit can optimize its search algorithm by referring to past search history during a search. For example, it can use a generative AI to refer to past search history. The generative AI can refer to past search history based on search queries and click history of search results. The generative AI can select the optimal search algorithm based on past search history. The generative AI can extract specific patterns from past search history and reflect them in the search algorithm. The generative AI can improve the accuracy of search results by referring to past search history. As a result, the search algorithm is optimized by referring to past search history, and search accuracy is improved.

[0056] The search unit can prioritize searches based on the severity of the risks during the search process. For example, it can use generative AI to assess the severity of risks. Generative AI can assess the severity of risks based on factors such as business impact, data breach risk, and legal impact. Generative AI can prioritize searches for risks with high severity. Generative AI can postpone searches for risks with low severity. Generative AI can adjust the search schedule based on severity. This allows for prioritizing searches based on the severity of the risks, thereby prioritizing searches for important risks.

[0057] The analysis unit can determine the priority of analysis based on the frequency of risk alerts during the analysis process. For example, the frequency of risk alerts can be measured using a generation AI. The generation AI can measure the frequency of risk alerts based on daily, weekly, and monthly occurrences. The generation AI can prioritize the analysis of risk alerts with a high frequency of occurrence. The generation AI can postpone the analysis of risk alerts with a low frequency of occurrence. The generation AI can adjust the analysis schedule based on the frequency of occurrence. As a result, by determining the priority of analysis based on the frequency of risk alerts, important risks can be analyzed preferentially.

[0058] The analysis unit can perform analysis while considering the geographical distribution of risk alerts. For example, the geographical distribution of risk alerts can be obtained using a generation AI. The generation AI can obtain the geographical distribution of risk alerts based on country-specific, regional, and city-specific distributions. The generation AI can identify the areas where risk alerts occur and perform analysis specific to those areas. The generation AI can determine the priority of risk alerts based on their geographical distribution. The generation AI can adjust the analysis schedule while considering the geographical distribution. As a result, by considering the geographical distribution of risk alerts, it becomes possible to perform analysis on risks specific to a particular region.

[0059] The proposal department can prioritize proposals based on the timing of risk occurrence. For example, it can use generative AI to assess the timing of risk occurrence. Generative AI can assess the timing of risk occurrence based on past incident data and predictive models. Generative AI can prioritize proposals for risks that are close to their occurrence date. Generative AI can postpone proposals for risks that are far off. Generative AI can adjust the proposal schedule based on the occurrence date. As a result, by prioritizing proposals based on the timing of risk occurrence, proposals can be made quickly for important risks.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The analysis unit analyzes security risk alerts that are updated daily. For example, it uses a generation AI to analyze security risk alerts and extract vulnerability information. The generation AI determines whether newly discovered vulnerabilities affect the company's systems and provides this information to security personnel. Step 2: The search unit performs a full search of the company's systems based on the information analyzed by the analysis unit to determine if there are any risks. For example, it may use generative AI to scan the entire company's systems and identify risks. Alternatively, it may search specific databases, perform a full system scan, and identify risks. Step 3: The proposal unit proposes countermeasures for the risks identified by the search unit. For example, it may use generative AI to propose appropriate countermeasures for risks. It may analyze the content of system alerts and propose appropriate countermeasures. If an anomaly occurs in a specific system, it may identify the cause and propose a method for correction. Step 4: The policy proposal department proposes a response plan to management based on the countermeasures proposed by the proposal department. For example, it may use generative AI to assess the impact of security risks and propose an appropriate response plan to management. It may also assess the impact of a specific risk on the entire company and propose strategies to mitigate that risk.

[0062] (Example of form 2) The comprehensive security consulting system according to an embodiment of the present invention is a system that uses a generating AI to analyze security risk alerts that are updated daily and performs a full search for risks in a company's systems. This system provides consulting on responses when security incidents (including concerns and warning signs) occur. The response consulting ranges from detailed on-site analysis to management decision support consulting for senior management. For example, the generating AI analyzes security risk alerts that are updated daily. In this process, the generating AI extracts vulnerability information and identifies cases that require company action. For example, it determines whether a newly discovered vulnerability affects the company's systems. This information is provided to security personnel, enabling a rapid response. Next, it provides consulting on responses when system alerts occur. The generating AI analyzes the content of the system alert and proposes appropriate countermeasures. For example, if an anomaly occurs in a specific system, it identifies the cause and proposes a method of correction. This information is provided to on-site security personnel, enabling a rapid response. Furthermore, it provides consulting on response policies to senior management. The generating AI evaluates the impact of security risks and proposes appropriate response policies to senior management. For example, it evaluates the impact of a specific risk on the entire company and proposes strategies to mitigate that risk. This information supports management decision-making and improves the overall security level of the company. This reduces the workload of security personnel and enables a rapid and appropriate response to security risks. Furthermore, it provides management with appropriate information, improving the overall security level of the company. For example, by having the AI ​​analyze vulnerability information and identify issues requiring company-wide action, security personnel can quickly implement countermeasures. Also, when a system alert occurs, the AI ​​proposes appropriate countermeasures, allowing on-site security personnel to respond quickly. In addition, the AI ​​assesses the impact of security risks and proposes appropriate response strategies for management, enabling them to make quick and appropriate decisions. In this way, the comprehensive security consulting system can improve the overall security level of the company.

[0063] The security comprehensive consulting system according to this embodiment comprises an analysis unit, a search unit, a proposal unit, and a policy proposal unit. The analysis unit analyzes security risk alerts that are updated daily. The analysis unit analyzes security risk alerts using, for example, a generation AI and extracts vulnerability information. The analysis unit uses, for example, a generation AI to determine whether newly discovered vulnerabilities affect the company's systems. The analysis unit uses, for example, a generation AI to analyze the content of security risk alerts and provides it to security personnel. The search unit performs a full search of the company's systems based on the information analyzed by the analysis unit to determine if there are any risks. The search unit uses, for example, a generation AI to scan the entire company's systems and identify risks. The search unit uses, for example, a generation AI to search a specific database and identify risks. The search unit uses, for example, a generation AI to scan the entire system and identify risks. The proposal unit proposes countermeasures for the risks identified by the search unit. The proposal unit uses, for example, a generation AI to propose appropriate countermeasures for the risks. The proposal unit uses, for example, a generation AI to analyze the content of system alerts and propose appropriate countermeasures. The proposal department, for example, identifies the cause of an anomaly in a specific system when a generation AI occurs and proposes a corrective method. The policy proposal department proposes a response policy to management based on the countermeasures proposed by the proposal department. The policy proposal department, for example, uses generation AI to evaluate the impact of security risks and proposes an appropriate response policy to management. The policy proposal department, for example, uses generation AI to evaluate the impact of a specific risk on the entire company and proposes a strategy to mitigate that risk. The policy proposal department, for example, uses generation AI to evaluate the impact of security risks and proposes an appropriate response policy to management. As a result, the comprehensive security consulting system according to the embodiment can consistently perform everything from analyzing security risk alerts to proposing countermeasures and proposing policies to management.

[0064] The analysis department analyzes security risk alerts that are updated daily. For example, the analysis department uses generative AI to analyze security risk alerts and extract vulnerability information. Specifically, the generative AI utilizes natural language processing technology to analyze the content of security risk alerts in detail. For instance, the generative AI receives text data from security risk alerts as input and extracts keywords and phrases related to vulnerabilities. Furthermore, based on these keywords and phrases, the generative AI identifies the type of vulnerability and its scope of impact. The analysis department, for example, determines whether a newly discovered vulnerability by the generative AI affects the company's systems. The generative AI compares the vulnerability information with the system's configuration information and evaluates the impact. For example, the generative AI retrieves the system's software version and configuration information from a database and compares it with the vulnerability information to identify potentially affected parts. The analysis department, for example, provides the analysis of the security risk alert content by the generative AI to security personnel. The generative AI generates a report of the analysis results in an easy-to-understand format and notifies security personnel. The report includes detailed vulnerability information, scope of impact, and recommended countermeasures. This allows the analysis unit to quickly and accurately analyze security risk alerts and provide valuable information to security personnel. Furthermore, the analysis unit can improve analysis accuracy by accumulating data from past security risk alerts and having the generating AI learn from this data. As a result, the analysis unit can always perform highly accurate analyses based on the latest information and support rapid responses to security risks.

[0065] The search unit performs a full scan of the company's systems to check for risks based on the information analyzed by the analysis unit. For example, the search unit uses generative AI to scan the entire company's systems and identify risks. Specifically, the generative AI analyzes the system's configuration information and log data to detect potential risks. The generative AI thoroughly investigates the state and settings of each system component to identify vulnerabilities and abnormal behavior. For example, the generative AI monitors network traffic to detect suspicious communication patterns and abnormal data transfers. It also analyzes system logs to identify abnormal login attempts and unauthorized access. The search unit, for example, uses generative AI to search specific databases to identify risks. The generative AI analyzes the database structure and content to detect vulnerable settings and unauthorized data manipulation. For example, the generative AI checks database access permissions to identify inappropriate permission settings and unauthorized access. It also analyzes database query logs to detect abnormal query patterns and unauthorized data manipulation. The search unit, for example, uses generative AI to scan the entire system and identify risks. The generating AI centrally manages system-wide configuration information and log data, and performs real-time scans. This allows the search unit to quickly and accurately identify risks across the entire system and provide appropriate information to security personnel. Furthermore, based on the scan results, the search unit can evaluate risk priorities and determine the urgency of the response. This enables the search unit to efficiently and effectively identify risks and improve the overall security of the system.

[0066] The proposal department proposes countermeasures for risks identified by the search department. For example, the proposal department uses generative AI to propose appropriate countermeasures for risks. Specifically, the generative AI refers to a database of past countermeasures and identifies effective countermeasures for similar risks. The generative AI proposes the optimal countermeasure depending on the type of risk and the scope of impact. For example, the generative AI proposes patching procedures and configuration change methods for specific vulnerabilities. The generative AI also proposes emergency and long-term countermeasures to minimize the impact of the risk. For example, the proposal department uses the generative AI to analyze the content of system alerts and propose appropriate countermeasures. The generative AI analyzes the content of system alerts in detail and identifies the cause and impact of the alert. For example, if the cause of the alert is abnormal operation of a specific component, the generative AI proposes a method for correcting or reconfiguring that component. The generative AI also evaluates the scope of impact of the alert and proposes methods for protecting affected systems and data. For example, if the generative AI detects an anomaly in a specific system, the proposal department identifies the cause and proposes a corrective method. The generative AI analyzes system log data and configuration information to identify the cause of the anomaly. For example, the generation AI can identify misconfigurations of specific components or software bugs and propose solutions. It can also suggest preventative measures and monitoring methods to prevent the recurrence of anomalies. This allows the proposal unit to quickly propose appropriate countermeasures for risks identified by the search unit, thereby improving system security. Furthermore, the proposal unit can evaluate the effectiveness of the proposed countermeasures and propose improvements as needed. This ensures that the proposal unit consistently provides optimal solutions and maintains system security.

[0067] The Policy Proposal Department proposes response policies to management based on the countermeasures proposed by the Proposal Department. For example, the Policy Proposal Department uses generative AI to assess the impact of security risks and proposes appropriate response policies to management. Specifically, the generative AI assesses the scope of risk impact and economic losses, and explains the importance of the risk to management. The generative AI quantitatively evaluates the impact of the risk and creates a report in a format easily understood by management. For example, the generative AI calculates the amount of loss and recovery costs in the event of a risk and presents this to management. The generative AI also proposes strategies and policies to mitigate the impact of the risk. For example, the Policy Proposal Department uses generative AI to assess the impact of a specific risk on the entire company and proposes strategies to mitigate that risk. When assessing the impact of a risk, the generative AI considers the importance of the company's business processes and assets. For example, the generative AI assesses the impact of a specific risk on the company's key business processes and proposes specific measures to mitigate that risk. The generative AI also proposes resource allocation and priorities to minimize the impact of the risk. For example, the Policy Proposal Department uses generative AI to assess the impact of security risks and proposes appropriate response policies to management. Generative AI utilizes historical data and statistical information to predict the probability and scope of risk occurrence when assessing the impact of risks. For example, based on data from past security incidents, the generative AI predicts the likelihood of a specific risk occurring and its scope of impact, explaining the importance of risk management to management. The generative AI also proposes long-term strategies and policies to mitigate the impact of risks, supporting management in taking appropriate responses to risks. As a result, the policy proposal department can propose appropriate response policies to management based on the countermeasures proposed by the department, thereby improving security throughout the company. Furthermore, the policy proposal department can monitor the implementation status of the proposed policies and propose improvement measures as needed. This allows the policy proposal department to always provide optimal response policies and maintain security throughout the company.

[0068] The analysis unit can extract vulnerability information and identify cases requiring in-house action. For example, the analysis unit extracts vulnerability information using generative AI. For example, the generative AI retrieves vulnerability information from a CVE database and determines whether it affects the company's systems. For example, the generative AI analyzes security vendor reports and identifies cases requiring in-house action. This allows for rapid response by extracting vulnerability information and identifying cases requiring in-house action.

[0069] The proposal department can analyze the content of system alerts and propose appropriate countermeasures. For example, the proposal department uses a generation AI to analyze the content of system alerts. For example, the proposal department's generation AI analyzes anomaly detection alerts and proposes appropriate countermeasures. For example, the proposal department's generation AI analyzes login failure alerts and proposes appropriate countermeasures. For example, the proposal department's generation AI analyzes resource overload alerts and proposes appropriate countermeasures. This enables a rapid response by analyzing the content of system alerts and proposing appropriate countermeasures.

[0070] The policy proposal department can assess the impact of security risks and propose appropriate response strategies to management. For example, the policy proposal department can use generative AI to assess the impact of security risks. For example, the policy proposal department can use generative AI to assess the impact on business and propose appropriate response strategies. For example, the policy proposal department can use generative AI to assess the risk of data breaches and propose appropriate response strategies. For example, the policy proposal department can use generative AI to assess the legal implications and propose appropriate response strategies. In this way, by assessing the impact of security risks and proposing appropriate response strategies to management, the security level of the entire company is improved.

[0071] The proposal department can identify the cause of an anomaly in a specific system and propose a corrective method. For example, the proposal department can use generative AI to identify the cause of the anomaly. For example, the proposal department can use generative AI to identify the cause of a system crash and propose a corrective method. For example, the proposal department can use generative AI to identify the cause of a data inconsistency and propose a corrective method. For example, the proposal department can use generative AI to identify the cause of a performance degradation and propose a corrective method. This enables rapid problem resolution by identifying the cause of an anomaly in a specific system and proposing a corrective method.

[0072] The policy proposal department can assess the impact of a specific risk on the entire company and propose strategies to mitigate that risk. For example, the policy proposal department can use generative AI to assess the impact of a risk. For example, the policy proposal department can use generative AI to assess the impact on the business and propose strategies to mitigate the risk. For example, the policy proposal department can use generative AI to assess the risk of data breaches and propose strategies to mitigate the risk. For example, the policy proposal department can use generative AI to assess the legal implications and propose strategies to mitigate the risk. This improves overall risk management within the company by assessing the impact of a specific risk on the entire company and proposing strategies to mitigate that risk.

[0073] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, the analysis unit estimates the user's emotions using generative AI. For example, the analysis unit estimates the user's emotions using generative AI facial recognition technology. For example, the analysis unit estimates the user's emotions using generative AI voice analysis technology. For example, the analysis unit estimates the user's emotions using generative AI text analysis technology. For example, if the user is nervous, the analysis unit provides a simple and highly visible display method. For example, if the user is relaxed, the analysis unit provides a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit provides a display method that gets straight to the point. By adjusting the display method of the analysis results according to the user's emotions, it becomes possible to provide a display that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0074] The analysis unit can improve the accuracy of its analysis of security risk alerts by referring to historical risk data. For example, the analysis unit can refer to historical risk data using a generation AI. For example, the analysis unit can use a generation AI to refer to historical security incident data and identify similar risk alerts. For example, the analysis unit can use a generation AI to analyze historical log data and extract specific patterns. For example, the analysis unit can use a generation AI to refer to historical reports and perform analysis considering the frequency of risk occurrences. In this way, the accuracy of the analysis is improved by referring to historical risk data.

[0075] The analysis unit can determine the priority of analysis based on the frequency of risk alerts during the analysis process. For example, the analysis unit measures the frequency of risk alerts using a generation AI. For example, the generation AI measures the frequency of risk alerts based on daily, weekly, and monthly occurrences. For example, the analysis unit prioritizes analyzing risk alerts that occur frequently, based on the generation AI. For example, the analysis unit postpones analyzing risk alerts that occur infrequently, based on the generation AI. For example, the analysis unit adjusts the analysis schedule based on the frequency of occurrence, based on the generation AI. In this way, by determining the priority of analysis based on the frequency of risk alerts, important risks can be analyzed preferentially.

[0076] The analysis unit can estimate the user's emotions and adjust the timing of the analysis based on the estimated emotions. For example, the analysis unit estimates the user's emotions using generative AI. For example, the analysis unit estimates the user's emotions using generative AI facial recognition technology. For example, the analysis unit estimates the user's emotions using generative AI voice analysis technology. For example, the analysis unit estimates the user's emotions using generative AI text analysis technology. For example, the analysis unit delays the timing of the analysis if the user is stressed. For example, the analysis unit speeds up the timing of the analysis if the user is relaxed. For example, the analysis unit starts the analysis immediately if the user is in a hurry. In this way, by adjusting the timing of the analysis according to the user's emotions, the analysis is performed at the optimal time for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0077] The analysis unit can perform analysis while considering the geographical distribution of risk alerts. For example, the analysis unit can obtain the geographical distribution of risk alerts using a generation AI. For example, the analysis unit can obtain the geographical distribution of risk alerts based on country-specific, regional, and city-specific distributions using a generation AI. For example, the analysis unit can identify the areas where risk alerts occur and perform analysis specific to those areas using a generation AI. For example, the analysis unit can determine the priority of risk alerts based on geographical distribution using a generation AI. For example, the analysis unit can adjust the analysis schedule using a generation AI that takes geographical distribution into consideration. This makes it possible to perform analysis on region-specific risks by considering the geographical distribution of risk alerts.

[0078] The analysis unit can improve the accuracy of its analysis by referring to relevant literature related to risk alerts during the analysis process. For example, the analysis unit uses a generation AI to refer to relevant literature related to risk alerts. For example, the generation AI in the analysis unit refers to relevant literature such as academic papers, technical reports, and white papers. For example, the generation AI in the analysis unit refers to literature related to risk alerts and incorporates it into the analysis. For example, the generation AI in the analysis unit obtains detailed information about risk alerts from relevant literature and utilizes it in the analysis. For example, the generation AI in the analysis unit identifies the cause of the risk alert based on the relevant literature and incorporates it into the analysis. In this way, the accuracy of the analysis is improved by referring to relevant literature.

[0079] The search unit can estimate the user's emotions and adjust the display method of search results based on the estimated user emotions. For example, the search unit estimates the user's emotions using generative AI. For example, the search unit estimates the user's emotions using generative AI facial recognition technology. For example, the search unit estimates the user's emotions using generative AI voice analysis technology. For example, the search unit estimates the user's emotions using generative AI text analysis technology. For example, if the user is nervous, the search unit provides a simple and highly visible display method. For example, if the user is relaxed, the search unit provides a display method that includes detailed information. For example, if the user is in a hurry, the search unit provides a display method that gets straight to the point. This allows for a user-friendly display by adjusting the display method of search results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0080] The search unit can optimize its search algorithm by referring to past search history during a search. For example, the search unit may use generative AI to refer to past search history. For example, the generative AI may refer to past search history based on search queries and click history of search results. For example, the generative AI may select the optimal search algorithm based on past search history. For example, the generative AI may extract specific patterns from past search history and reflect them in the search algorithm. For example, the generative AI may refer to past search history to improve the accuracy of search results. As a result, by referring to past search history, the search algorithm is optimized and search accuracy is improved.

[0081] The search unit can determine search priorities based on the severity of the risks during a search. For example, the search unit uses generative AI to evaluate the severity of risks. For example, the generative AI evaluates the severity of risks based on factors such as business impact, data breach risk, and legal impact. For example, the search unit prioritizes searches for risks with high severity based on the generative AI. For example, the search unit postpones searches for risks with low severity based on the generative AI. For example, the search unit adjusts the search schedule based on severity based on the generative AI. In this way, by determining search priorities based on the severity of risks, important risks can be prioritized in the search.

[0082] The search unit can estimate the user's emotions and adjust the timing of the search based on the estimated emotions. For example, the search unit might use generative AI to estimate the user's emotions. Alternatively, the search unit might use generative AI with facial recognition technology to estimate the user's emotions. Or, it might use generative AI with speech analysis technology to estimate the user's emotions. The search unit might use generative AI with text analysis technology to estimate the user's emotions. For example, if the user is stressed, the search unit might delay the search. For example, if the user is relaxed, the search unit might speed up the search. For example, if the user is in a hurry, the search unit might start immediately. This allows the search to be performed at the optimal time for the user by adjusting the search timing according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0083] The search unit can perform searches while considering the geographical distribution of risks. For example, the search unit can obtain the geographical distribution of risks using generative AI. For example, the search unit can obtain the geographical distribution of risks based on country-specific, regional, and city-specific distributions using generative AI. For example, the search unit can identify areas where risks occur and perform searches specific to those areas using generative AI. For example, the search unit can determine the priority of risks based on geographical distribution using generative AI. For example, the search unit can adjust the search schedule considering geographical distribution using generative AI. This makes it possible to search for region-specific risks by considering the geographical distribution of risks.

[0084] The search unit can improve the accuracy of its search by referencing relevant literature on risks during the search process. For example, the search unit uses generative AI to reference relevant literature on risks. For example, the generative AI references relevant literature such as academic papers, technical reports, and white papers. For example, the generative AI references literature related to risks and incorporates it into the search. For example, the generative AI obtains detailed information about risks from the relevant literature and utilizes it in the search. For example, the generative AI identifies the causes of risks based on the relevant literature and incorporates it into the search. As a result, the accuracy of the search is improved by referencing relevant literature.

[0085] The proposal unit can estimate the user's emotions and adjust the way the proposal is presented based on the estimated emotions. For example, the proposal unit can estimate the user's emotions using generative AI. For example, the proposal unit can estimate the user's emotions using generative AI facial recognition technology. For example, the proposal unit can estimate the user's emotions using generative AI voice analysis technology. For example, the proposal unit can estimate the user's emotions using generative AI text analysis technology. For example, if the user is nervous, the proposal unit can provide a simple and highly visible proposal method. For example, if the user is relaxed, the proposal unit can provide a proposal method that includes detailed information. For example, if the user is in a hurry, the proposal unit can provide a proposal method that gets straight to the point. By adjusting the way the proposal is presented according to the user's emotions, it becomes possible to make proposals that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0086] The proposal unit can apply different proposal algorithms depending on the risk category when making a proposal. For example, the proposal unit classifies risk categories using generative AI. For example, the proposal unit classifies risks based on categories such as technical risk, business risk, and legal risk using generative AI. For example, the proposal unit selects the optimal proposal algorithm according to the risk category using generative AI. For example, the proposal unit improves the accuracy of proposals by applying different proposal algorithms for each category using generative AI. For example, the proposal unit determines the priority of proposals based on the risk category using generative AI. This improves the accuracy of proposals by applying the optimal proposal algorithm according to the risk category.

[0087] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. For example, the suggestion unit might use generative AI to estimate the user's emotions. For example, the suggestion unit might use generative AI with facial recognition technology to estimate the user's emotions. For example, the suggestion unit might use generative AI with voice analysis technology to estimate the user's emotions. For example, the suggestion unit might use generative AI with text analysis technology to estimate the user's emotions. For example, if the user is in a hurry, the suggestion unit might provide a short, to-the-point suggestion. For example, if the user is relaxed, the suggestion unit might provide a longer suggestion with detailed explanations. For example, if the user is excited, the suggestion unit might provide a suggestion with visually stimulating effects. By adjusting the length of the suggestion according to the user's emotions, the system can provide the most suitable suggestion for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The proposal department can prioritize proposals based on the timing of risk occurrence. For example, the proposal department can use generative AI to evaluate the timing of risk occurrence. For example, the generative AI can evaluate the timing of risk occurrence based on past incident data and predictive models. For example, the generative AI can prioritize proposing risks that are close to their occurrence. For example, the generative AI can postpone proposing risks that are far off. For example, the generative AI can adjust the proposal schedule based on the occurrence timing. This allows for rapid proposals for important risks by prioritizing proposals based on the timing of risk occurrence.

[0089] The proposal department can adjust the order of proposals based on the relevance of risks during the proposal process. For example, the proposal department uses generative AI to evaluate the relevance of risks. For example, the generative AI evaluates the relevance of risks based on common causes, overlapping scope of impact, temporal relevance, etc. For example, the proposal department prioritizes proposing risks with high relevance based on the generative AI. For example, the proposal department postpones proposing risks with low relevance based on the generative AI. For example, the proposal department adjusts the proposal schedule based on relevance based on the generative AI. In this way, by adjusting the order of proposals based on the relevance of risks, proposals are given priority to highly relevant risks.

[0090] The policy suggestion unit can estimate the user's emotions and adjust the policy suggestion method based on the estimated user emotions. For example, the policy suggestion unit estimates the user's emotions using generative AI. For example, the policy suggestion unit estimates the user's emotions using generative AI facial recognition technology. For example, the policy suggestion unit estimates the user's emotions using generative AI voice analysis technology. For example, the policy suggestion unit estimates the user's emotions using generative AI text analysis technology. For example, if the user is nervous, the policy suggestion unit provides a simple and highly visual policy suggestion method. For example, if the user is relaxed, the policy suggestion unit provides a policy suggestion method that includes detailed information. For example, if the user is in a hurry, the policy suggestion unit provides a policy suggestion method that gets straight to the point. In this way, by adjusting the policy suggestion method according to the user's emotions, policy suggestions that are easy for the user to understand are provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The policy proposal department can improve the accuracy of its policy proposals by referring to past risk data. For example, the policy proposal department uses a generative AI to refer to past risk data. For example, the generative AI in the policy proposal department refers to past security incident data, log data, reports, etc. For example, the generative AI in the policy proposal department makes optimal policy proposals based on past risk data. For example, the generative AI in the policy proposal department extracts specific patterns from past risk data and reflects them in the policy proposals. For example, the generative AI in the policy proposal department refers to past risk data to improve the accuracy of policy proposals. In this way, the accuracy of policy proposals is improved by referring to past risk data.

[0092] The policy proposal department can prioritize policy proposals based on the impact of the risks when proposing policies. For example, the policy proposal department uses generative AI to evaluate the impact of risks. For example, the generative AI evaluates the impact of risks based on factors such as business impact, data breach risk, and legal impact. For example, the generative AI prioritizes proposing policies for risks with a high impact. For example, the generative AI postpones proposing policies for risks with a low impact. For example, the generative AI adjusts the policy proposal schedule based on the impact. In this way, by prioritizing policy proposals based on the impact of risks, policies are prioritized for important risks.

[0093] The policy suggestion unit can estimate the user's emotions and adjust the timing of policy suggestions based on the estimated emotions. For example, the policy suggestion unit might use generative AI to estimate the user's emotions. For example, the generative AI might use facial recognition technology to estimate the user's emotions. For example, the generative AI might use voice analysis technology to estimate the user's emotions. For example, the generative AI might use text analysis technology to estimate the user's emotions. For example, if the user is stressed, the policy suggestion unit might delay the timing of policy suggestions. For example, if the user is relaxed, the policy suggestion unit might speed up the timing of policy suggestions. For example, if the user is in a hurry, the policy suggestion unit might immediately start suggesting policies. This allows the policy suggestion unit to provide suggestions at the optimal time for the user by adjusting the timing of policy suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0094] The policy proposal department can propose policies while considering the geographical distribution of risks. For example, the policy proposal department can obtain the geographical distribution of risks using generative AI. For example, the generative AI can obtain the geographical distribution of risks based on country-specific, regional, and city-specific distributions. For example, the generative AI can identify areas where risks occur and propose policies specific to those areas. For example, the generative AI can determine the priority of risks based on geographical distribution. For example, the generative AI can adjust the policy proposal schedule while considering geographical distribution. As a result, by considering the geographical distribution of risks, it becomes possible to propose policies for risks specific to a particular region.

[0095] The policy proposal department can improve the accuracy of its policy proposals by referring to relevant risk literature during the policy proposal process. For example, the policy proposal department can use generative AI to refer to relevant risk literature. For example, the generative AI can refer to relevant literature such as academic papers, technical reports, and white papers. For example, the generative AI can refer to risk-related literature and reflect it in the policy proposal. For example, the generative AI can obtain detailed risk information from relevant literature and utilize it in the policy proposal. For example, the generative AI can identify the causes of risk based on relevant literature and reflect it in the policy proposal. In this way, the accuracy of policy proposals is improved by referring to relevant literature.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, the user's emotions can be estimated using generative AI. The generative AI can estimate the user's emotions using facial recognition technology. The generative AI can estimate the user's emotions using voice analysis technology. The generative AI can estimate the user's emotions using text analysis technology. For example, if the user is nervous, a simple and highly visible display method can be provided. If the user is relaxed, a display method containing detailed information can be provided. If the user is in a hurry, a display method that gets straight to the point can be provided. In this way, by adjusting the display method of the analysis results according to the user's emotions, it becomes possible to provide a display that is easy for the user to understand.

[0098] The search unit can optimize its search algorithm by referring to past search history during a search. For example, it can use a generative AI to refer to past search history. The generative AI can refer to past search history based on search queries and click history of search results. The generative AI can select the optimal search algorithm based on past search history. The generative AI can extract specific patterns from past search history and reflect them in the search algorithm. The generative AI can improve the accuracy of search results by referring to past search history. As a result, the search algorithm is optimized by referring to past search history, and search accuracy is improved.

[0099] The proposal function can estimate the user's emotions and adjust the way the proposal is presented based on those emotions. For example, it can use generative AI to estimate the user's emotions. The generative AI can use facial recognition technology to estimate the user's emotions. The generative AI can use voice analysis technology to estimate the user's emotions. The generative AI can use text analysis technology to estimate the user's emotions. For example, if the user is nervous, it can provide a simple and highly visible proposal. If the user is relaxed, it can provide a proposal that includes detailed information. If the user is in a hurry, it can provide a proposal that gets straight to the point. By adjusting the way the proposal is presented according to the user's emotions, it becomes possible to make proposals that are easy for the user to understand.

[0100] The search unit can prioritize searches based on the severity of the risks during the search process. For example, it can use generative AI to assess the severity of risks. Generative AI can assess the severity of risks based on factors such as business impact, data breach risk, and legal impact. Generative AI can prioritize searches for risks with high severity. Generative AI can postpone searches for risks with low severity. Generative AI can adjust the search schedule based on severity. This allows for prioritizing searches based on the severity of the risks, thereby prioritizing searches for important risks.

[0101] The policy proposal unit can estimate the user's emotions and adjust the policy proposal method based on the estimated emotions. For example, it can use generative AI to estimate the user's emotions. The generative AI can use facial recognition technology to estimate the user's emotions. The generative AI can use voice analysis technology to estimate the user's emotions. The generative AI can use text analysis technology to estimate the user's emotions. For example, if the user is nervous, it can provide a simple and highly visual policy proposal method. If the user is relaxed, it can provide a policy proposal method that includes detailed information. If the user is in a hurry, it can provide a policy proposal method that gets straight to the point. In this way, by adjusting the policy proposal method according to the user's emotions, policy proposals that are easy for the user to understand are provided.

[0102] The analysis unit can determine the priority of analysis based on the frequency of risk alerts during the analysis process. For example, the frequency of risk alerts can be measured using a generation AI. The generation AI can measure the frequency of risk alerts based on daily, weekly, and monthly occurrences. The generation AI can prioritize the analysis of risk alerts with a high frequency of occurrence. The generation AI can postpone the analysis of risk alerts with a low frequency of occurrence. The generation AI can adjust the analysis schedule based on the frequency of occurrence. As a result, by determining the priority of analysis based on the frequency of risk alerts, important risks can be analyzed preferentially.

[0103] The search unit can estimate the user's emotions and adjust the search timing based on those emotions. For example, it can use generative AI to estimate the user's emotions. Generative AI can use facial recognition technology to estimate the user's emotions. Generative AI can use voice analysis technology to estimate the user's emotions. Generative AI can use text analysis technology to estimate the user's emotions. For example, if the user is stressed, the search timing can be delayed. If the user is relaxed, the search timing can be sped up. If the user is in a hurry, the search can be started immediately. In this way, by adjusting the search timing according to the user's emotions, the search is performed at the optimal time for the user.

[0104] The analysis unit can perform analysis while considering the geographical distribution of risk alerts. For example, the geographical distribution of risk alerts can be obtained using a generation AI. The generation AI can obtain the geographical distribution of risk alerts based on country-specific, regional, and city-specific distributions. The generation AI can identify the areas where risk alerts occur and perform analysis specific to those areas. The generation AI can determine the priority of risk alerts based on their geographical distribution. The generation AI can adjust the analysis schedule while considering the geographical distribution. As a result, by considering the geographical distribution of risk alerts, it becomes possible to perform analysis on risks specific to a particular region.

[0105] The proposal department can prioritize proposals based on the timing of risk occurrence. For example, it can use generative AI to assess the timing of risk occurrence. Generative AI can assess the timing of risk occurrence based on past incident data and predictive models. Generative AI can prioritize proposals for risks that are close to their occurrence date. Generative AI can postpone proposals for risks that are far off. Generative AI can adjust the proposal schedule based on the occurrence date. As a result, by prioritizing proposals based on the timing of risk occurrence, proposals can be made quickly for important risks.

[0106] The policy proposal unit can estimate the user's emotions and adjust the timing of policy proposals based on those emotions. For example, it can use generative AI to estimate the user's emotions. Generative AI can use facial recognition technology to estimate the user's emotions. Generative AI can use voice analysis technology to estimate the user's emotions. Generative AI can use text analysis technology to estimate the user's emotions. For example, if the user is stressed, the timing of policy proposals can be delayed. If the user is relaxed, the timing of policy proposals can be advanced. If the user is in a hurry, policy proposals can be started immediately. In this way, by adjusting the timing of policy proposals according to the user's emotions, policy proposals are delivered at the optimal time for the user.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The analysis unit analyzes security risk alerts that are updated daily. For example, it uses a generation AI to analyze security risk alerts and extract vulnerability information. The generation AI determines whether newly discovered vulnerabilities affect the company's systems and provides this information to security personnel. Step 2: The search unit performs a full search of the company's systems based on the information analyzed by the analysis unit to determine if there are any risks. For example, it may use generative AI to scan the entire company's systems and identify risks. Alternatively, it may search specific databases, perform a full system scan, and identify risks. Step 3: The proposal unit proposes countermeasures for the risks identified by the search unit. For example, it may use generative AI to propose appropriate countermeasures for risks. It may analyze the content of system alerts and propose appropriate countermeasures. If an anomaly occurs in a specific system, it may identify the cause and propose a method for correction. Step 4: The policy proposal department proposes a response plan to management based on the countermeasures proposed by the proposal department. For example, it may use generative AI to assess the impact of security risks and propose an appropriate response plan to management. It may also assess the impact of a specific risk on the entire company and propose strategies to mitigate that risk.

[0109] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0110] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0111] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0112] Each of the multiple elements described above, including the analysis unit, search unit, proposal unit, and policy proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the smart device 14 and the processor 28 of the data processing unit 12, and uses generating AI to analyze security risk alerts and extract vulnerability information. The search unit is implemented by the processor 46 of the smart device 14 and the processor 28 of the data processing unit 12, and scans the entire company system based on the analyzed information to identify risks. The proposal unit is implemented by the processor 46 of the smart device 14 and the processor 28 of the data processing unit 12, and proposes appropriate countermeasures for the identified risks. The policy proposal unit is implemented by the processor 46 of the smart device 14 and the processor 28 of the data processing unit 12, and proposes appropriate countermeasures to management. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0116] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0117] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0119] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0120] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0121] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0122] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0123] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0125] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0127] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements described above, including the analysis unit, search unit, proposal unit, and policy proposal unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the smart glasses 214 and the processor 28 of the data processing unit 12, and uses generating AI to analyze security risk alerts and extract vulnerability information. The search unit is implemented by the processor 46 of the smart glasses 214 and the processor 28 of the data processing unit 12, and scans the entire company system based on the analyzed information to identify risks. The proposal unit is implemented by the processor 46 of the smart glasses 214 and the processor 28 of the data processing unit 12, and proposes appropriate countermeasures for the identified risks. The policy proposal unit is implemented by the processor 46 of the smart glasses 214 and the processor 28 of the data processing unit 12, and proposes appropriate countermeasures to management. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements described above, including the analysis unit, search unit, proposal unit, and policy proposal unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the headset terminal 314 and the processor 28 of the data processing unit 12, and uses generated AI to analyze security risk alerts and extract vulnerability information. The search unit is implemented by the processor 46 of the headset terminal 314 and the processor 28 of the data processing unit 12, and scans the entire company system based on the analyzed information to identify risks. The proposal unit is implemented by the processor 46 of the headset terminal 314 and the processor 28 of the data processing unit 12, and proposes appropriate countermeasures for the identified risks. The policy proposal unit is implemented by the processor 46 of the headset terminal 314 and the processor 28 of the data processing unit 12, and proposes appropriate countermeasures to management. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0153] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0154] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0155] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0156] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0157] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0158] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0159] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0161] Each of the multiple elements described above, including the analysis unit, search unit, proposal unit, and policy proposal unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the robot 414 and the processor 28 of the data processing unit 12, and uses generated AI to analyze security risk alerts and extract vulnerability information. The search unit is implemented by the processor 46 of the robot 414 and the processor 28 of the data processing unit 12, and scans the entire company system based on the analyzed information to identify risks. The proposal unit is implemented by the processor 46 of the robot 414 and the processor 28 of the data processing unit 12, and proposes appropriate countermeasures for the identified risks. The policy proposal unit is implemented by the processor 46 of the robot 414 and the processor 28 of the data processing unit 12, and proposes appropriate countermeasures to management. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0162] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0163] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0164] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0165] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0166] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0167] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0169] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0170] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0171] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0172] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0173] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0174] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0175] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0176] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0177] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0178] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0179] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0180] (Note 1) The analysis department analyzes security risk alerts that are updated daily, A search unit performs a full search of the company's systems based on the information analyzed by the aforementioned analysis unit to determine if there are any risks. A proposal unit proposes countermeasures for the risks identified by the search unit, The system comprises a policy proposal unit that proposes a course of action to management based on the countermeasures proposed by the aforementioned proposal unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Extract vulnerability information and identify cases that require in-house action. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, We analyze the content of system alerts and propose appropriate countermeasures. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned policy proposal department, We assess the impact of security risks and propose appropriate response strategies to management. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, If a malfunction occurs in a specific system, we will identify the cause and propose a solution. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned policy proposal department, We assess the impact of specific risks on the entire company and propose strategies to mitigate those risks. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, When analyzing security risk alerts, we improve the accuracy of the analysis by referring to historical risk data. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, During analysis, the analysis priority is determined based on the frequency of risk alerts. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, It estimates the user's emotions and adjusts the timing of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, During the analysis, the geographical distribution of risk alerts will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During analysis, we refer to relevant literature on risk alerts to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned search unit, It estimates the user's sentiment and adjusts how search results are displayed based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned search unit, When performing a search, the search algorithm is optimized by referring to past search history. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned search unit, When searching, search priorities are determined based on the severity of the risk. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned search unit, It estimates the user's emotions and adjusts the timing of searches based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned search unit, When searching, consider the geographical distribution of risk. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned search unit, When searching, refer to relevant literature on risk to improve search accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the risk category. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When making a proposal, prioritize the proposal based on when the risk will occur. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the risks. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned policy proposal department, We estimate the user's emotions and adjust the policy proposal method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned policy proposal department, When proposing policies, we improve the accuracy of those policies by referring to past risk data. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned policy proposal department, When proposing policies, prioritize the policy proposals based on the impact of the risks. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned policy proposal department, We estimate the user's emotions and adjust the timing of policy suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned policy proposal department, When proposing policies, consider the geographical distribution of risks. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned policy proposal department, When proposing policies, we improve the accuracy of the policy proposals by referring to relevant risk literature. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The analysis department analyzes security risk alerts that are updated daily, A search unit performs a full search of the company's systems based on the information analyzed by the aforementioned analysis unit to determine if there are any risks. A proposal unit proposes countermeasures for the risks identified by the search unit, The system comprises a policy proposal unit that proposes a course of action to management based on the countermeasures proposed by the aforementioned proposal unit. A system characterized by the following features.

2. The aforementioned analysis unit, Extract vulnerability information and identify cases that require in-house action. The system according to feature 1.

3. The aforementioned proposal section is, We analyze the content of system alerts and propose appropriate countermeasures. The system according to feature 1.

4. The aforementioned policy proposal department, We assess the impact of security risks and propose appropriate response strategies to management. The system according to feature 1.

5. The aforementioned proposal section is, If a malfunction occurs in a specific system, we will identify the cause and propose a solution. The system according to feature 1.

6. The aforementioned policy proposal department, We assess the impact of specific risks on the entire company and propose strategies to mitigate those risks. The system according to feature 1.

7. The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system according to feature 1.

8. The aforementioned analysis unit, When analyzing security risk alerts, we improve the accuracy of the analysis by referring to historical risk data. The system according to feature 1.

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