system

Abstract

The system according to this embodiment aims to efficiently determine security compliance requirements and generate guidelines when introducing cloud services. [Solution] The system according to the embodiment comprises a reception unit, a judgment unit, and a generation unit. The reception unit inputs information about the cloud service. The judgment unit analyzes the information input by the reception unit and determines the security compliance requirements. The generation unit generates guidelines for implementation based on the results determined by the judgment unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the process of determining whether the security compliance requirements are met when introducing a cloud service requires time and effort and is not efficient.

[0005] The system according to the embodiment aims to efficiently determine the security compliance requirements when introducing a cloud service and generate guidelines.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a judgment unit, and a generation unit. The reception unit receives information about the cloud service. The judgment unit analyzes the information received by the reception unit and determines the security compliance requirements. The generation unit generates implementation guidelines based on the results determined by the judgment unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently determine security compliance requirements and generate guidelines when introducing cloud services. [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 applied 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.

[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 security compliance support system according to an embodiment of the present invention is a system in which, when a company introduces a new cloud service including AI generation, the AI ​​semi-automatically determines whether the security compliance requirements are met and automatically generates guidelines for introduction. In this system, the company inputs information about the cloud service it intends to introduce into the AI, which analyzes the information and determines whether the security compliance requirements are met. For example, it checks whether data encryption is properly performed and whether access control is properly set up. Furthermore, the AI ​​automatically generates guidelines for introduction. These include recommendations for security settings and methods for monitoring after introduction. For example, it specifically indicates methods for data encryption, access control settings, and audit log management. This mechanism significantly reduces the man-hours required for security checks, enabling anyone to safely introduce convenient tools and other services. For example, even small and medium-sized enterprises can safely introduce cloud services without specialized knowledge, as the AI ​​automatically checks security compliance requirements and generates guidelines. In addition, the guidelines generated by the AI ​​are based on the latest security standards and always reflect the latest information. As a result, companies can always implement the latest security measures and minimize security risks. This allows security compliance support systems to significantly reduce the time and effort required for security checks when companies introduce new cloud services, including AI-generated content, enabling everyone to safely and conveniently implement such tools.

[0029] The security compliance support system according to this embodiment comprises a reception unit, a judgment unit, and a generation unit. The reception unit inputs information about cloud services. This information includes, but is not limited to, the service name, provider, functions, and price. The reception unit inputs the information about cloud services into a form, for example. The reception unit can also upload the information about cloud services as a file. Furthermore, the reception unit can obtain information about cloud services through an API. For example, the reception unit inputs the information about cloud services into an input form and sends the information by pressing the submit button. The reception unit can also upload the information about cloud services as a CSV file, which the system can then automatically analyze. When obtaining information through an API, the reception unit sends a request to the API endpoint provided by the cloud service provider and obtains the information. The judgment unit analyzes the information input by the reception unit and determines the security compliance requirements. These security compliance requirements include, but are not limited to, data protection regulations, access control, and encryption standards. For example, the judgment unit verifies the data encryption method. The judgment unit can also verify the access control settings. Furthermore, the judgment unit can verify the audit log management method. For example, the judgment unit verifies whether encryption technologies such as AES or RSA are used as the data encryption method. The judgment unit can also verify whether user authentication and permission management are properly implemented as access control settings. As for audit log management, it can also verify whether the log retention period and analysis method are properly set. The generation unit generates implementation guidelines based on the results determined by the judgment unit. These guidelines may include, but are not limited to, security setting recommendations and post-implementation monitoring methods. For example, the generation unit can generate guidelines that include password policies, firewall settings, and antivirus measures as security setting recommendations. The generation unit can also generate guidelines that include periodic security scans, log monitoring, and anomaly detection as post-implementation monitoring methods.As a result, the security compliance support system according to this embodiment can significantly reduce the man-hours required for security checks by inputting information on cloud services, determining security compliance requirements, and generating guidelines.

[0030] The determination unit may include an encryption verification unit that verifies the data encryption method. The encryption verification unit, for example, verifies whether encryption technologies such as AES or RSA are used as the data encryption method. For example, the encryption verification unit verifies whether the data of the cloud service is encrypted with AES-256. The encryption verification unit can also verify whether TLS is used during data transfer. Furthermore, the encryption verification unit can also verify whether RSA encryption is used when the data is stored. For example, the encryption verification unit verifies whether the data of the cloud service is encrypted with AES-256. The encryption verification unit can also verify whether TLS is used during data transfer. Furthermore, the encryption verification unit can verify whether RSA encryption is used when the data is stored. This improves the accuracy of security compliance by verifying the data encryption method. Some or all of the above processing in the encryption verification unit may be performed using AI, for example, or without AI. For example, the encryption verification unit can input the cloud service data encryption information into a generating AI and have the generating AI perform the encryption method verification.

[0031] The decision unit may include an access control verification unit that checks the access control settings. The access control verification unit can, for example, check whether user authentication and permission management are being performed appropriately. For example, the access control verification unit can check whether user authentication for the cloud service is two-factor authentication. The access control verification unit can also check whether user permissions are set appropriately. Furthermore, the access control verification unit can also check whether access logs are being recorded appropriately. For example, the access control verification unit can check whether user authentication for the cloud service is two-factor authentication. The access control verification unit can also check whether user permissions are set appropriately. Furthermore, the access control verification unit can also check whether access logs are being recorded appropriately. This improves the accuracy of security compliance by checking the access control settings. Some or all of the above processing in the access control verification unit may be performed using AI, for example, or without using AI. For example, the access control verification unit can input cloud service access control information into a generating AI and have the generating AI perform the access control check.

[0032] The judgment unit may include an audit log verification unit that verifies the audit log management method. The audit log verification unit can, for example, verify whether the log retention period and analysis method are appropriately set. For example, the audit log verification unit can verify whether the audit logs of the cloud service are retained for one year. The audit log verification unit can also verify whether the log analysis method is appropriately set. Furthermore, the audit log verification unit can also verify whether the log protection measures are appropriately set. For example, the audit log verification unit can verify whether the audit logs of the cloud service are retained for one year. The audit log verification unit can also verify whether the log analysis method is appropriately set. Furthermore, the audit log verification unit can also verify whether the log protection measures are appropriately set. This improves the accuracy of security compliance by verifying the audit log management method. Some or all of the above processing in the audit log verification unit may be performed using AI, for example, or without using AI. For example, the audit log verification unit can input audit log information of the cloud service into a generating AI and have the generating AI perform the audit log verification.

[0033] The generation unit can generate guidelines that include security setting recommendations. For example, the generation unit can generate guidelines that include password policies, firewall settings, and antivirus measures as security setting recommendations. For example, the generation unit may recommend setting a password of 8 characters or more as the password policy for cloud services. The generation unit may also recommend restricting access from specific IP addresses as a firewall setting. Furthermore, the generation unit may also recommend performing regular virus scans as an antivirus measure. For example, the generation unit may recommend setting a password of 8 characters or more as the password policy for cloud services. The generation unit may also recommend restricting access from specific IP addresses as a firewall setting. Furthermore, the generation unit may also recommend performing regular virus scans as an antivirus measure. By generating guidelines that include security setting recommendations, security measures at the time of deployment are strengthened. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input the security setting information of the cloud service into a generation AI and have the generation AI perform the generation of guidelines.

[0034] The generation unit can generate guidelines that include monitoring methods after deployment. For example, the generation unit can generate guidelines that include periodic security scans, log monitoring, and anomaly detection as post-deployment monitoring methods. For example, the generation unit may recommend conducting periodic security scans after deploying a cloud service. The generation unit may also recommend periodically checking access logs and error logs as log monitoring. Furthermore, the generation unit may also recommend implementing a system to detect unauthorized access and abnormal behavior as anomaly detection. For example, the generation unit may recommend conducting periodic security scans after deploying a cloud service. The generation unit may also recommend periodically checking access logs and error logs as log monitoring. Furthermore, the generation unit may also recommend implementing a system to detect unauthorized access and abnormal behavior as anomaly detection. By generating guidelines that include monitoring methods after deployment, security measures during operation are strengthened. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input cloud service monitoring information into a generation AI and have the generation AI generate guidelines for monitoring methods.

[0035] The reception department can analyze past cloud service implementation history and select the optimal information input method. For example, the reception department can propose the optimal input method based on the history of successful cloud service implementations in the past. For example, the reception department can save past implementation history in a database and analyze successful cases. The reception department can also analyze past failure cases and select an input method to avoid similar mistakes. For example, the reception department can save past failure cases in a database and analyze the causes of failure. Furthermore, the reception department can propose an input method suitable for a specific industry or company size based on past implementation history. For example, the reception department can classify past implementation history by industry and propose an input method suitable for a specific industry. In this way, the optimal information input method can be selected by analyzing past implementation history. Some or all of the above processes in the reception department may be performed using AI, for example, or not using AI. For example, the reception department can input past implementation history data into a generating AI and have the generating AI select the optimal information input method.

[0036] The reception unit can filter cloud service information based on the user's current work status and areas of interest when inputting it. For example, the reception unit can prioritize inputting relevant cloud service information based on the user's current project status. For example, the reception unit can obtain the current project status from the user's project management tool and filter the relevant cloud service information. The reception unit can also filter and input relevant cloud service information based on the user's areas of interest. For example, the reception unit can obtain the user's areas of interest from surveys or past activity history and filter the relevant cloud service information. Furthermore, the reception unit can filter and input only the necessary information according to the user's workload. For example, the reception unit can obtain the user's workload from work profiles or past activity history and filter only the necessary information. This allows for the efficient collection of highly relevant information by filtering information based on the user's work status and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or not. For example, the reception unit can input data on the user's work status and areas of interest into a generating AI and have the generating AI perform the information filtering.

[0037] The reception unit can prioritize inputting highly relevant information when entering cloud service information, taking into account the user's geographical location. For example, if the user is in a specific region, the reception unit will prioritize inputting cloud service information related to that region. For example, the reception unit can obtain the user's geographical location information from GPS data or IP address and filter cloud service information related to that region. Furthermore, if the user is on the move, the reception unit can input the most suitable cloud service information based on their current location. For example, the reception unit can obtain the user's current location in real time and provide the most suitable cloud service information. In addition, if the user is overseas, the reception unit can prioritize inputting cloud service information based on the regulations and laws of that country. For example, based on the user's geographical location information, the reception unit can provide cloud service information that complies with the regulations and laws of that country. This allows for the efficient collection of highly relevant information by considering the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information into a generating AI and have the generating AI perform the filtering of highly relevant information.

[0038] The reception unit can analyze a user's social media activity and input relevant information when inputting cloud service information. For example, the reception unit can analyze the user's social media activity and input relevant cloud service information. For example, the reception unit can retrieve posts from the user's social media account and filter relevant cloud service information. The reception unit can also input relevant cloud service information based on information about companies and experts that the user follows. For example, the reception unit can analyze the posts of companies and experts that the user follows and provide relevant cloud service information. Furthermore, the reception unit can analyze the user's interests on social media and input relevant cloud service information. For example, the reception unit can analyze the user's social media "likes" and comment history and provide relevant cloud service information. This allows for the efficient collection of highly relevant information by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI perform the filtering of relevant information.

[0039] The decision unit can improve the accuracy of security compliance decisions by considering the interrelationships of cloud service information. For example, the decision unit analyzes data exchanges between cloud services and assesses security risks. For example, the decision unit analyzes data flows between cloud services and verifies whether data exchanges are conducted securely. The decision unit can also make security compliance decisions by considering the dependencies of cloud services. For example, the decision unit maps the dependencies of cloud services and evaluates the impact of these dependencies on security risks. Furthermore, the decision unit can evaluate the interoperability of cloud services and minimize security risks. For example, the decision unit tests the interoperability of cloud services to check for security risks. This improves the accuracy of security compliance decisions by considering the interrelationships of cloud service information. Some or all of the above processing in the decision unit may be performed using AI, for example, or not using AI. For example, the decision unit can input interrelationship data of cloud service information into a generating AI and have the generating AI perform security compliance decisions.

[0040] The decision-making unit can determine security compliance by considering the attribute information of the cloud service provider. For example, the decision-making unit can analyze the cloud service provider's past security incidents and perform a risk assessment. For example, the decision-making unit can obtain a history of the cloud service provider's past security incidents from a database and perform a risk assessment. The decision-making unit can also evaluate the cloud service provider's security policies and make a compliance judgment. For example, the decision-making unit can review the cloud service provider's security policies and verify whether the policies are being properly implemented. Furthermore, the decision-making unit can evaluate the cloud service provider's compliance with industry standards and determine security compliance. For example, the decision-making unit can verify whether the cloud service provider complies with industry standards and perform a risk assessment based on the compliance status. This improves the accuracy of security compliance judgments by considering the attribute information of the cloud service provider. Some or all of the above processes in the decision-making unit may be performed using AI, for example, or not. For example, the decision-making unit can input the cloud service provider's attribute information into a generating AI and have the generating AI perform the security compliance judgment.

[0041] The decision unit can determine security compliance by considering the geographical distribution of cloud service information. For example, the decision unit can assess security risks by considering the location of the cloud service's data center. For example, the decision unit can map the locations of the cloud service's data center on a map and assess the impact of location on security risks. The decision unit can also determine security compliance by considering the geographical distribution of cloud service users. For example, the decision unit can analyze the locations of cloud service users and assess the impact of geographical distribution on security risks. Furthermore, the decision unit can analyze the data transfer paths of cloud services and assess geographical risks. For example, the decision unit can analyze the data transfer paths of cloud services and assess the impact of geographical risks on security. This improves the accuracy of security compliance determinations by considering the geographical distribution of cloud service information. Some or all of the above processing in the decision unit may be performed using AI, for example, or without AI. For example, the decision unit can input geographical distribution data of cloud service information into a generating AI and have the generating AI perform security compliance determinations.

[0042] The decision-making unit can improve the accuracy of its security compliance decisions by referring to relevant literature on cloud services. For example, the decision-making unit can evaluate security risks by referring to the latest research papers on cloud services. For example, the decision-making unit can retrieve the latest research papers on cloud services from a database and evaluate security risks. The decision-making unit can also make compliance decisions by referring to literature describing best practices for cloud services. For example, the decision-making unit can review literature describing best practices for cloud services and perform a risk assessment based on those best practices. Furthermore, the decision-making unit can improve the accuracy of its decisions by referring to security guidelines for cloud services. For example, the decision-making unit can review security guidelines for cloud services and perform a risk assessment based on those guidelines. This improves the accuracy of security compliance decisions by referring to relevant literature on cloud services. Some or all of the above processes in the decision-making unit may be performed using AI, for example, or not using AI. For example, the decision-making unit can input relevant literature data on cloud services into a generating AI and have the generating AI perform security compliance decisions.

[0043] The generation unit can adjust the level of detail of the guidelines based on the importance of the cloud service when generating the guidelines. For example, the generation unit generates detailed guidelines for high-importance cloud services. For example, the generation unit evaluates the importance of cloud services based on criteria such as business impact, data confidentiality, and service availability, and provides detailed guidelines for high-importance cloud services. The generation unit can also generate concise guidelines for low-importance cloud services. For example, the generation unit provides concise guidelines for low-importance cloud services that include the minimum necessary security measures. Furthermore, the generation unit can generate guidelines that appropriately include the necessary information according to the importance of the cloud service. For example, the generation unit provides guidelines that include detailed security settings and monitoring methods based on the importance of the cloud service. In this way, appropriate guidelines can be provided by adjusting the level of detail of the guidelines according to the importance of the cloud service. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input cloud service importance data into a generation AI and have the generation AI perform the adjustment of the level of detail of the guidelines.

[0044] The generation unit can apply different guideline generation algorithms depending on the category of the cloud service when generating guidelines. For example, for security-focused cloud services, the generation unit can apply a guideline generation algorithm specialized in security. For example, the generation unit can classify cloud service categories by service model such as IaaS, PaaS, and SaaS, and provide guidelines to strengthen security measures for security-focused cloud services. The generation unit can also apply a guideline generation algorithm specialized in cost efficiency for cost-focused cloud services. For example, the generation unit can provide guidelines for cost-focused cloud services that include recommended settings for cost reduction. Furthermore, the generation unit can apply a guideline generation algorithm specialized in performance optimization for performance-focused cloud services. For example, the generation unit can provide guidelines for performance-focused cloud services that include recommended settings for performance improvement. In this way, appropriate guidelines can be provided by applying a guideline generation algorithm according to the category of the cloud service. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input cloud service category data into a generation AI and have the generation AI execute the application of the guideline generation algorithm.

[0045] The generation unit can determine the priority of guidelines based on the timing of cloud service deployment when generating guidelines. For example, the generation unit will prioritize generating guidelines for cloud services that are scheduled for deployment in the near future. For example, the generation unit will evaluate the timing of cloud service deployment based on criteria such as project schedule, release date, and deployment plan, and provide guidelines preferentially to cloud services that are scheduled for deployment in the near future. The generation unit can also postpone generating guidelines for cloud services that are scheduled for deployment in the distant future. For example, the generation unit will provide guidelines for cloud services that are scheduled for deployment in the distant future, including the minimum necessary security measures. Furthermore, the generation unit can generate guidelines that appropriately include the necessary information according to the deployment timing. For example, the generation unit will provide guidelines that include detailed security settings and monitoring methods based on the deployment timing of cloud services. This ensures that guidelines are provided at the appropriate time by determining the priority of guidelines based on the deployment timing of cloud services. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input cloud service deployment timing data into a generation AI and have the generation AI perform the determination of guideline priority.

[0046] The generation unit can adjust the order of guidelines based on the relevance of cloud services when generating them. For example, the generation unit can prioritize generating guidelines for highly relevant cloud services. For instance, the generation unit can evaluate the relevance of cloud services based on criteria such as overlapping functions, data sharing, and interoperability, and prioritize providing guidelines for highly relevant cloud services. The generation unit can also postpone generating guidelines for less relevant cloud services. For example, the generation unit can provide guidelines for less relevant cloud services that include only the minimum necessary security measures. Furthermore, the generation unit can generate guidelines that appropriately include the necessary information depending on the relevance of the cloud services. For example, the generation unit can provide guidelines that include detailed security settings and monitoring methods based on the relevance of the cloud services. This allows the guidelines to be provided in the appropriate order by adjusting the order of the guidelines based on the relevance of the cloud services. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input cloud service relevance data into a generation AI and have the generation AI perform the adjustment of the guideline order.

[0047] The encryption verification unit can improve the accuracy of encryption method verification by considering the data characteristics of the cloud service when verifying the encryption method. For example, the encryption verification unit selects and verifies an appropriate encryption method based on the data characteristics of the cloud service. For example, if the data of the cloud service is text data, the encryption verification unit verifies whether AES encryption is used. The encryption verification unit can also verify whether RSA encryption is used if the data of the cloud service is image data. Furthermore, the encryption verification unit can also verify whether TLS encryption is used if the data of the cloud service is video data. For example, if the encryption verification unit is text data, it verifies whether AES encryption is used. The encryption verification unit can also verify whether RSA encryption is used if the data of the cloud service is image data. Furthermore, the encryption verification unit can also verify whether TLS encryption is used if the data of the cloud service is video data. This improves the accuracy of encryption method verification by considering the data characteristics of the cloud service. Some or all of the above processing in the encryption verification unit may be performed using AI, for example, or without AI. For example, the encryption verification unit can input cloud service data characteristics data into a generating AI and have the generating AI perform the encryption method verification.

[0048] The encryption verification unit can perform encryption method verification while considering the geographical distribution of cloud services. For example, the encryption verification unit can verify the encryption method while considering the location of the cloud service's data center. For example, the encryption verification unit maps the location of the cloud service's data center on a map and evaluates the impact of the location on security risks. The encryption verification unit can also verify the encryption method while considering the geographical distribution of cloud service users. For example, the encryption verification unit analyzes the locations of cloud service users and evaluates the impact of geographical distribution on security risks. Furthermore, the encryption verification unit can analyze the data transfer path of the cloud service, evaluate geographical risks, and verify the encryption method. For example, the encryption verification unit analyzes the data transfer path of the cloud service and evaluates the impact of geographical risks on security. This improves the accuracy of encryption method verification by considering the geographical distribution of cloud services. Some or all of the above processing in the encryption verification unit may be performed using AI, for example, or without AI. For example, the encryption verification unit can input geographical distribution data of cloud services into a generating AI and have the generating AI perform the encryption method verification.

[0049] The access control verification unit can improve the accuracy of access control verification by considering the user attributes of the cloud service. For example, the access control verification unit selects and verifies an appropriate access control method based on the user attributes of the cloud service. For example, if the user of the cloud service is an administrator, the access control verification unit verifies whether administrator privileges are set appropriately. The access control verification unit can also verify whether general user privileges are set appropriately if the user of the cloud service is a general user. Furthermore, if the user of the cloud service is a guest user, the access control verification unit can also verify whether guest user privileges are set appropriately. For example, if the user of the cloud service is an administrator, the access control verification unit verifies whether administrator privileges are set appropriately. The access control verification unit can also verify whether general user privileges are set appropriately if the user of the cloud service is a general user. Furthermore, if the user of the cloud service is a guest user, the access control verification unit can also verify whether guest user privileges are set appropriately. This improves the accuracy of access control verification by considering the user attributes of the cloud service. Some or all of the above processing in the access control verification unit may be performed using AI, for example, or without using AI. For example, the access control verification unit can input user attribute data from the cloud service into the generating AI and have the generating AI perform access control verification.

[0050] The access control verification unit can perform access control verification while considering the geographical distribution of cloud services. For example, the access control verification unit can verify the access control method while considering the location of the cloud service's data center. For example, the access control verification unit maps the location of the cloud service's data center on a map and evaluates the impact of the location on security risks. The access control verification unit can also verify the access control method while considering the geographical distribution of cloud service users. For example, the access control verification unit analyzes the location of cloud service users and evaluates the impact of geographical distribution on security risks. Furthermore, the access control verification unit can analyze the data transfer path of the cloud service, evaluate geographical risks, and verify the access control method. For example, the access control verification unit analyzes the data transfer path of the cloud service and evaluates the impact of geographical risks on security. As a result, considering the geographical distribution of cloud services improves the accuracy of access control verification. Some or all of the above processing in the access control verification unit may be performed using AI, for example, or without AI. For example, the access control verification unit can input geographical distribution data of cloud services into a generating AI and have the generating AI perform the access control method verification.

[0051] The audit log verification unit can improve the accuracy of audit log verification by considering the log characteristics of cloud services. For example, the audit log verification unit selects and verifies an appropriate verification method based on the log characteristics of the cloud service. For example, if the cloud service log is an access log, the audit log verification unit verifies the retention period and analysis method of the access log. The audit log verification unit can also verify the retention period and analysis method of the error log if the cloud service log is an error log. Furthermore, if the cloud service log is an audit log, the audit log verification unit can also verify the retention period and analysis method of the audit log. For example, if the cloud service log is an access log, the audit log verification unit verifies the retention period and analysis method of the access log. Furthermore, if the cloud service log is an error log, the audit log verification unit can also verify the retention period and analysis method of the error log. Furthermore, if the cloud service log is an audit log, the audit log verification unit can also verify the retention period and analysis method of the audit log. In this way, the accuracy of audit log verification is improved by considering the log characteristics of cloud services. Some or all of the above-described processes in the audit log verification unit may be performed using AI, for example, or without AI. For example, the audit log verification unit can input cloud service log characteristic data into a generating AI and have the generating AI perform audit log verification.

[0052] The audit log verification unit can perform audit log verification while considering the geographical distribution of cloud services. For example, the audit log verification unit can perform audit log verification while considering the location of the cloud service's data center. For example, the audit log verification unit can map the locations of the cloud service's data centers on a map and evaluate the impact of the location on security risks. The audit log verification unit can also perform audit log verification while considering the geographical distribution of cloud service users. For example, the audit log verification unit can analyze the locations of cloud service users and evaluate the impact of geographical distribution on security risks. Furthermore, the audit log verification unit can analyze the data transfer paths of cloud services and evaluate geographical risks before performing audit log verification. For example, the audit log verification unit can analyze the data transfer paths of cloud services and evaluate the impact of geographical risks on security. This improves the accuracy of audit log verification by considering the geographical distribution of cloud services. Some or all of the above processing in the audit log verification unit may be performed using AI, for example, or without AI. For example, the audit log verification unit can input geographical distribution data of cloud services into a generating AI and have the generating AI perform audit log verification.

[0053] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0054] The reception desk can analyze a user's past input history and propose the optimal input method. For example, the reception desk can store past input history in a database and analyze successful cases. It can also analyze past failures and select input methods to avoid similar mistakes. Furthermore, the reception desk can propose input methods suitable for specific industries or company sizes based on past input history. In this way, the optimal information input method can be selected by analyzing past input history. Some or all of the above processes in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input past input history data into a generating AI and have the generating AI select the optimal information input method.

[0055] The encryption verification unit can select and verify an appropriate encryption method based on the data characteristics of the cloud service. For example, if the data of the cloud service is text data, the encryption verification unit can verify whether AES encryption is being used. It can also verify whether RSA encryption is being used if the data of the cloud service is image data. Furthermore, if the data of the cloud service is video data, the encryption verification unit can verify whether TLS encryption is being used. This improves the accuracy of encryption method verification by considering the data characteristics of the cloud service. Some or all of the above processing in the encryption verification unit may be performed using AI, for example, or without AI. For example, the encryption verification unit can input data characteristic data of the cloud service into a generating AI and have the generating AI perform the encryption method verification.

[0056] The access control verification unit can select and verify an appropriate access control method based on the user attributes of the cloud service. For example, if the user of the cloud service is an administrator, the access control verification unit can verify whether administrator privileges are set appropriately. The access control verification unit can also verify whether general user privileges are set appropriately if the user of the cloud service is a general user. Furthermore, if the user of the cloud service is a guest user, the access control verification unit can verify whether guest user privileges are set appropriately. This improves the accuracy of access control verification by considering the user attributes of the cloud service. Some or all of the above processing in the access control verification unit may be performed using AI, for example, or without AI. For example, the access control verification unit can input cloud service user attribute data into a generating AI and have the generating AI perform the access control verification.

[0057] The audit log verification unit can select and verify an appropriate verification method based on the log characteristics of the cloud service. For example, if the cloud service log is an access log, the audit log verification unit can verify the retention period and analysis method of the access log. Furthermore, if the cloud service log is an error log, the audit log verification unit can also verify the retention period and analysis method of the error log. In addition, if the cloud service log is an audit log, the audit log verification unit can also verify the retention period and analysis method of the audit log. This improves the accuracy of audit log verification by considering the log characteristics of the cloud service. Some or all of the above processing in the audit log verification unit may be performed using AI, for example, or without AI. For example, the audit log verification unit can input cloud service log characteristic data into a generating AI and have the generating AI perform the audit log verification.

[0058] The generation unit can adjust the level of detail in the guidelines based on the importance of the cloud services. For example, it can generate detailed guidelines for highly important cloud services. The generation unit evaluates the importance of cloud services based on criteria such as business impact, data confidentiality, and service availability, and provides detailed guidelines for highly important cloud services. The generation unit can also generate concise guidelines for less important cloud services. For example, it can provide concise guidelines for less important cloud services, including only the minimum necessary security measures. By adjusting the level of detail in the guidelines according to the importance of the cloud services, appropriate guidelines can be provided. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input cloud service importance data into a generation AI and have the generation AI adjust the level of detail in the guidelines.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The reception desk enters information about the cloud service. This information includes the service name, provider, features, and price. The reception desk can enter the information into a form, upload it as a file, or retrieve it via an API. For example, the information can be submitted by entering it into an input form and pressing the submit button. Alternatively, it can be uploaded as a CSV file and automatically analyzed by the system. When retrieving information via an API, a request is sent to the API endpoint provided by the cloud service provider to retrieve the information. Step 2: The decision-making unit analyzes the information entered by the reception unit and determines the security compliance requirements. Security compliance requirements include data protection regulations, access control, and encryption standards. For example, it checks whether encryption technologies such as AES or RSA are used as the data encryption method, whether user authentication and permission management are properly implemented as access control settings, and whether the log retention period and analysis method are properly set as audit log management methods. Step 3: The generation unit generates implementation guidelines based on the results determined by the judgment unit. The guidelines include recommendations for security settings and methods for monitoring after implementation. For example, the guidelines may include recommendations for security settings such as password policies, firewall settings, and antivirus software. The guidelines may also include methods for monitoring after implementation such as periodic security scans, log monitoring, and anomaly detection.

[0061] (Example of form 2) The security compliance support system according to an embodiment of the present invention is a system in which, when a company introduces a new cloud service including AI generation, the AI ​​semi-automatically determines whether the security compliance requirements are met and automatically generates guidelines for introduction. In this system, the company inputs information about the cloud service it intends to introduce into the AI, which analyzes the information and determines whether the security compliance requirements are met. For example, it checks whether data encryption is properly performed and whether access control is properly set up. Furthermore, the AI ​​automatically generates guidelines for introduction. These include recommendations for security settings and methods for monitoring after introduction. For example, it specifically indicates methods for data encryption, access control settings, and audit log management. This mechanism significantly reduces the man-hours required for security checks, enabling anyone to safely introduce convenient tools and other services. For example, even small and medium-sized enterprises can safely introduce cloud services without specialized knowledge, as the AI ​​automatically checks security compliance requirements and generates guidelines. In addition, the guidelines generated by the AI ​​are based on the latest security standards and always reflect the latest information. As a result, companies can always implement the latest security measures and minimize security risks. This allows security compliance support systems to significantly reduce the time and effort required for security checks when companies introduce new cloud services, including AI-generated content, enabling everyone to safely and conveniently implement such tools.

[0062] The security compliance support system according to this embodiment comprises a reception unit, a judgment unit, and a generation unit. The reception unit inputs information about cloud services. This information includes, but is not limited to, the service name, provider, functions, and price. The reception unit inputs the information about cloud services into a form, for example. The reception unit can also upload the information about cloud services as a file. Furthermore, the reception unit can obtain information about cloud services through an API. For example, the reception unit inputs the information about cloud services into an input form and sends the information by pressing the submit button. The reception unit can also upload the information about cloud services as a CSV file, which the system can then automatically analyze. When obtaining information through an API, the reception unit sends a request to the API endpoint provided by the cloud service provider and obtains the information. The judgment unit analyzes the information input by the reception unit and determines the security compliance requirements. These security compliance requirements include, but are not limited to, data protection regulations, access control, and encryption standards. For example, the judgment unit verifies the data encryption method. The judgment unit can also verify the access control settings. Furthermore, the judgment unit can verify the audit log management method. For example, the judgment unit verifies whether encryption technologies such as AES or RSA are used as the data encryption method. The judgment unit can also verify whether user authentication and permission management are properly implemented as access control settings. As for audit log management, it can also verify whether the log retention period and analysis method are properly set. The generation unit generates implementation guidelines based on the results determined by the judgment unit. These guidelines may include, but are not limited to, security setting recommendations and post-implementation monitoring methods. For example, the generation unit can generate guidelines that include password policies, firewall settings, and antivirus measures as security setting recommendations. The generation unit can also generate guidelines that include periodic security scans, log monitoring, and anomaly detection as post-implementation monitoring methods.As a result, the security compliance support system according to this embodiment can significantly reduce the man-hours required for security checks by inputting information on cloud services, determining security compliance requirements, and generating guidelines.

[0063] The determination unit may include an encryption verification unit that verifies the data encryption method. The encryption verification unit, for example, verifies whether encryption technologies such as AES or RSA are used as the data encryption method. For example, the encryption verification unit verifies whether the data of the cloud service is encrypted with AES-256. The encryption verification unit can also verify whether TLS is used during data transfer. Furthermore, the encryption verification unit can also verify whether RSA encryption is used when the data is stored. For example, the encryption verification unit verifies whether the data of the cloud service is encrypted with AES-256. The encryption verification unit can also verify whether TLS is used during data transfer. Furthermore, the encryption verification unit can verify whether RSA encryption is used when the data is stored. This improves the accuracy of security compliance by verifying the data encryption method. Some or all of the above processing in the encryption verification unit may be performed using AI, for example, or without AI. For example, the encryption verification unit can input the cloud service data encryption information into a generating AI and have the generating AI perform the encryption method verification.

[0064] The decision unit may include an access control verification unit that checks the access control settings. The access control verification unit can, for example, check whether user authentication and permission management are being performed appropriately. For example, the access control verification unit can check whether user authentication for the cloud service is two-factor authentication. The access control verification unit can also check whether user permissions are set appropriately. Furthermore, the access control verification unit can also check whether access logs are being recorded appropriately. For example, the access control verification unit can check whether user authentication for the cloud service is two-factor authentication. The access control verification unit can also check whether user permissions are set appropriately. Furthermore, the access control verification unit can also check whether access logs are being recorded appropriately. This improves the accuracy of security compliance by checking the access control settings. Some or all of the above processing in the access control verification unit may be performed using AI, for example, or without using AI. For example, the access control verification unit can input cloud service access control information into a generating AI and have the generating AI perform the access control check.

[0065] The judgment unit may include an audit log verification unit that verifies the audit log management method. The audit log verification unit can, for example, verify whether the log retention period and analysis method are appropriately set. For example, the audit log verification unit can verify whether the audit logs of the cloud service are retained for one year. The audit log verification unit can also verify whether the log analysis method is appropriately set. Furthermore, the audit log verification unit can also verify whether the log protection measures are appropriately set. For example, the audit log verification unit can verify whether the audit logs of the cloud service are retained for one year. The audit log verification unit can also verify whether the log analysis method is appropriately set. Furthermore, the audit log verification unit can also verify whether the log protection measures are appropriately set. This improves the accuracy of security compliance by verifying the audit log management method. Some or all of the above processing in the audit log verification unit may be performed using AI, for example, or without using AI. For example, the audit log verification unit can input audit log information of the cloud service into a generating AI and have the generating AI perform the audit log verification.

[0066] The generation unit can generate guidelines that include security setting recommendations. For example, the generation unit can generate guidelines that include password policies, firewall settings, and antivirus measures as security setting recommendations. For example, the generation unit may recommend setting a password of 8 characters or more as the password policy for cloud services. The generation unit may also recommend restricting access from specific IP addresses as a firewall setting. Furthermore, the generation unit may also recommend performing regular virus scans as an antivirus measure. For example, the generation unit may recommend setting a password of 8 characters or more as the password policy for cloud services. The generation unit may also recommend restricting access from specific IP addresses as a firewall setting. Furthermore, the generation unit may also recommend performing regular virus scans as an antivirus measure. By generating guidelines that include security setting recommendations, security measures at the time of deployment are strengthened. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input the security setting information of the cloud service into a generation AI and have the generation AI perform the generation of guidelines.

[0067] The generation unit can generate guidelines that include monitoring methods after deployment. For example, the generation unit can generate guidelines that include periodic security scans, log monitoring, and anomaly detection as post-deployment monitoring methods. For example, the generation unit may recommend conducting periodic security scans after deploying a cloud service. The generation unit may also recommend periodically checking access logs and error logs as log monitoring. Furthermore, the generation unit may also recommend implementing a system to detect unauthorized access and abnormal behavior as anomaly detection. For example, the generation unit may recommend conducting periodic security scans after deploying a cloud service. The generation unit may also recommend periodically checking access logs and error logs as log monitoring. Furthermore, the generation unit may also recommend implementing a system to detect unauthorized access and abnormal behavior as anomaly detection. By generating guidelines that include monitoring methods after deployment, security measures during operation are strengthened. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input cloud service monitoring information into a generation AI and have the generation AI generate guidelines for monitoring methods.

[0068] The reception desk can estimate the user's emotions and adjust the timing of cloud service information input based on the estimated emotions. For example, if the user is feeling stressed, the reception desk can delay the input timing to provide time for relaxation. For example, the reception desk can capture the user's facial expression with a camera and use an emotion estimation algorithm to determine if the user is feeling stressed. The reception desk can also speed up the input timing to efficiently collect information if the user is relaxed. For example, the reception desk can record the user's voice and use voice analysis technology to determine if the user is relaxed. Furthermore, if the user is in a hurry, the reception desk can optimize the input timing to quickly collect information. For example, the reception desk can collect the user's biometric data (heart rate and skin electrical activity) with sensors and use an emotion estimation algorithm to determine if the user is in a hurry. This allows for efficient information collection by adjusting the input timing 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the reception area may be performed using AI, for example, or without AI. For example, the reception area can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0069] The reception department can analyze past cloud service implementation history and select the optimal information input method. For example, the reception department can propose the optimal input method based on the history of successful cloud service implementations in the past. For example, the reception department can save past implementation history in a database and analyze successful cases. The reception department can also analyze past failure cases and select an input method to avoid similar mistakes. For example, the reception department can save past failure cases in a database and analyze the causes of failure. Furthermore, the reception department can propose an input method suitable for a specific industry or company size based on past implementation history. For example, the reception department can classify past implementation history by industry and propose an input method suitable for a specific industry. In this way, the optimal information input method can be selected by analyzing past implementation history. Some or all of the above processes in the reception department may be performed using AI, for example, or not using AI. For example, the reception department can input past implementation history data into a generating AI and have the generating AI select the optimal information input method.

[0070] The reception unit can filter cloud service information based on the user's current work status and areas of interest when inputting it. For example, the reception unit can prioritize inputting relevant cloud service information based on the user's current project status. For example, the reception unit can obtain the current project status from the user's project management tool and filter the relevant cloud service information. The reception unit can also filter and input relevant cloud service information based on the user's areas of interest. For example, the reception unit can obtain the user's areas of interest from surveys or past activity history and filter the relevant cloud service information. Furthermore, the reception unit can filter and input only the necessary information according to the user's workload. For example, the reception unit can obtain the user's workload from work profiles or past activity history and filter only the necessary information. This allows for the efficient collection of highly relevant information by filtering information based on the user's work status and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or not. For example, the reception unit can input data on the user's work status and areas of interest into a generating AI and have the generating AI perform the information filtering.

[0071] The reception desk can estimate the user's emotions and prioritize the cloud service information to be entered based on the estimated emotions. For example, if the user is stressed, the reception desk will prioritize and postpone less important information. For instance, the reception desk may capture the user's facial expression with a camera and use an emotion estimation algorithm to determine if the user is stressed. Conversely, if the user is relaxed, the reception desk can prioritize the input of highly important information. For example, the reception desk may record the user's voice and use voice analysis technology to determine if the user is relaxed. Furthermore, if the user is in a hurry, the reception desk can prioritize the input of the most important information. For example, the reception desk may collect the user's biometric data (heart rate and skin electrical activity) with sensors and use an emotion estimation algorithm to determine if the user is in a hurry. This enables efficient information gathering by prioritizing information according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the reception area may be performed using AI, for example, or without AI. For example, the reception area can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0072] The reception unit can prioritize inputting highly relevant information when entering cloud service information, taking into account the user's geographical location. For example, if the user is in a specific region, the reception unit will prioritize inputting cloud service information related to that region. For example, the reception unit can obtain the user's geographical location information from GPS data or IP address and filter cloud service information related to that region. Furthermore, if the user is on the move, the reception unit can input the most suitable cloud service information based on their current location. For example, the reception unit can obtain the user's current location in real time and provide the most suitable cloud service information. In addition, if the user is overseas, the reception unit can prioritize inputting cloud service information based on the regulations and laws of that country. For example, based on the user's geographical location information, the reception unit can provide cloud service information that complies with the regulations and laws of that country. This allows for the efficient collection of highly relevant information by considering the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information into a generating AI and have the generating AI perform the filtering of highly relevant information.

[0073] The reception unit can analyze a user's social media activity and input relevant information when inputting cloud service information. For example, the reception unit can analyze the user's social media activity and input relevant cloud service information. For example, the reception unit can retrieve posts from the user's social media account and filter relevant cloud service information. The reception unit can also input relevant cloud service information based on information about companies and experts that the user follows. For example, the reception unit can analyze the posts of companies and experts that the user follows and provide relevant cloud service information. Furthermore, the reception unit can analyze the user's interests on social media and input relevant cloud service information. For example, the reception unit can analyze the user's social media "likes" and comment history and provide relevant cloud service information. This allows for the efficient collection of highly relevant information by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI perform the filtering of relevant information.

[0074] The decision-making unit can estimate the user's emotions and adjust security compliance criteria based on the estimated emotions. For example, if the user is stressed, the decision-making unit can relax the criteria and make a quick decision. For example, the decision-making unit can capture the user's facial expression with a camera and use an emotion estimation algorithm to determine if the user is stressed. The decision-making unit can also apply strict criteria to make a detailed decision if the user is relaxed. For example, the decision-making unit can record the user's voice and use voice analysis technology to determine if the user is relaxed. Furthermore, if the user is in a hurry, the decision-making unit can optimize the criteria and make a quick decision. For example, the decision-making unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and use an emotion estimation algorithm to determine if the user is in a hurry. This allows for efficient security compliance decisions by adjusting the criteria according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0075] The decision unit can improve the accuracy of security compliance decisions by considering the interrelationships of cloud service information. For example, the decision unit analyzes data exchanges between cloud services and assesses security risks. For example, the decision unit analyzes data flows between cloud services and verifies whether data exchanges are conducted securely. The decision unit can also make security compliance decisions by considering the dependencies of cloud services. For example, the decision unit maps the dependencies of cloud services and evaluates the impact of these dependencies on security risks. Furthermore, the decision unit can evaluate the interoperability of cloud services and minimize security risks. For example, the decision unit tests the interoperability of cloud services to check for security risks. This improves the accuracy of security compliance decisions by considering the interrelationships of cloud service information. Some or all of the above processing in the decision unit may be performed using AI, for example, or not using AI. For example, the decision unit can input interrelationship data of cloud service information into a generating AI and have the generating AI perform security compliance decisions.

[0076] The decision-making unit can determine security compliance by considering the attribute information of the cloud service provider. For example, the decision-making unit can analyze the cloud service provider's past security incidents and perform a risk assessment. For example, the decision-making unit can obtain a history of the cloud service provider's past security incidents from a database and perform a risk assessment. The decision-making unit can also evaluate the cloud service provider's security policies and make a compliance judgment. For example, the decision-making unit can review the cloud service provider's security policies and verify whether the policies are being properly implemented. Furthermore, the decision-making unit can evaluate the cloud service provider's compliance with industry standards and determine security compliance. For example, the decision-making unit can verify whether the cloud service provider complies with industry standards and perform a risk assessment based on the compliance status. This improves the accuracy of security compliance judgments by considering the attribute information of the cloud service provider. Some or all of the above processes in the decision-making unit may be performed using AI, for example, or not. For example, the decision-making unit can input the cloud service provider's attribute information into a generating AI and have the generating AI perform the security compliance judgment.

[0077] The decision-making unit can estimate the user's emotions and adjust the order in which security compliance results are displayed based on the estimated emotions. For example, if the user is stressed, the decision-making unit can display important results first to enable a quick response. For example, the decision-making unit can capture the user's facial expressions with a camera and use an emotion estimation algorithm to determine if the user is stressed. The decision-making unit can also display detailed results in order if the user is relaxed. For example, the decision-making unit can record the user's voice and use voice analysis technology to determine if the user is relaxed. Furthermore, if the user is in a hurry, the decision-making unit can prioritize displaying the most important results. For example, the decision-making unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and use an emotion estimation algorithm to determine if the user is in a hurry. This allows for an efficient response by adjusting the order in which results are displayed according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0078] The decision unit can determine security compliance by considering the geographical distribution of cloud service information. For example, the decision unit can assess security risks by considering the location of the cloud service's data center. For example, the decision unit can map the locations of the cloud service's data center on a map and assess the impact of location on security risks. The decision unit can also determine security compliance by considering the geographical distribution of cloud service users. For example, the decision unit can analyze the locations of cloud service users and assess the impact of geographical distribution on security risks. Furthermore, the decision unit can analyze the data transfer paths of cloud services and assess geographical risks. For example, the decision unit can analyze the data transfer paths of cloud services and assess the impact of geographical risks on security. This improves the accuracy of security compliance determinations by considering the geographical distribution of cloud service information. Some or all of the above processing in the decision unit may be performed using AI, for example, or without AI. For example, the decision unit can input geographical distribution data of cloud service information into a generating AI and have the generating AI perform security compliance determinations.

[0079] The decision-making unit can improve the accuracy of its security compliance decisions by referring to relevant literature on cloud services. For example, the decision-making unit can evaluate security risks by referring to the latest research papers on cloud services. For example, the decision-making unit can retrieve the latest research papers on cloud services from a database and evaluate security risks. The decision-making unit can also make compliance decisions by referring to literature describing best practices for cloud services. For example, the decision-making unit can review literature describing best practices for cloud services and perform a risk assessment based on those best practices. Furthermore, the decision-making unit can improve the accuracy of its decisions by referring to security guidelines for cloud services. For example, the decision-making unit can review security guidelines for cloud services and perform a risk assessment based on those guidelines. This improves the accuracy of security compliance decisions by referring to relevant literature on cloud services. Some or all of the above processes in the decision-making unit may be performed using AI, for example, or not using AI. For example, the decision-making unit can input relevant literature data on cloud services into a generating AI and have the generating AI perform security compliance decisions.

[0080] The generation unit can estimate the user's emotions and adjust the way the guidelines are expressed based on the estimated emotions. For example, if the user is stressed, the generation unit will adopt a simple and easy-to-understand expression. For instance, the generation unit may capture the user's facial expression with a camera and use an emotion estimation algorithm to determine if the user is stressed. Furthermore, if the user is relaxed, the generation unit may adopt an expression that includes detailed explanations. For example, the generation unit may record the user's voice and use voice analysis technology to determine if the user is relaxed. Additionally, if the user is in a hurry, the generation unit may adopt a concise expression that gets straight to the point. For example, the generation unit may collect the user's biometric data (heart rate and skin electrical activity) with sensors and use an emotion estimation algorithm to determine if the user is in a hurry. This allows for the provision of more appropriate guidelines by adjusting the expression of the guidelines 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user emotion data into a generation AI and have the generation AI perform emotion estimation.

[0081] The generation unit can adjust the level of detail of the guidelines based on the importance of the cloud service when generating the guidelines. For example, the generation unit generates detailed guidelines for high-importance cloud services. For example, the generation unit evaluates the importance of cloud services based on criteria such as business impact, data confidentiality, and service availability, and provides detailed guidelines for high-importance cloud services. The generation unit can also generate concise guidelines for low-importance cloud services. For example, the generation unit provides concise guidelines for low-importance cloud services that include the minimum necessary security measures. Furthermore, the generation unit can generate guidelines that appropriately include the necessary information according to the importance of the cloud service. For example, the generation unit provides guidelines that include detailed security settings and monitoring methods based on the importance of the cloud service. In this way, appropriate guidelines can be provided by adjusting the level of detail of the guidelines according to the importance of the cloud service. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input cloud service importance data into a generation AI and have the generation AI perform the adjustment of the level of detail of the guidelines.

[0082] The generation unit can apply different guideline generation algorithms depending on the category of the cloud service when generating guidelines. For example, for security-focused cloud services, the generation unit can apply a guideline generation algorithm specialized in security. For example, the generation unit can classify cloud service categories by service model such as IaaS, PaaS, and SaaS, and provide guidelines to strengthen security measures for security-focused cloud services. The generation unit can also apply a guideline generation algorithm specialized in cost efficiency for cost-focused cloud services. For example, the generation unit can provide guidelines for cost-focused cloud services that include recommended settings for cost reduction. Furthermore, the generation unit can apply a guideline generation algorithm specialized in performance optimization for performance-focused cloud services. For example, the generation unit can provide guidelines for performance-focused cloud services that include recommended settings for performance improvement. In this way, appropriate guidelines can be provided by applying a guideline generation algorithm according to the category of the cloud service. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input cloud service category data into a generation AI and have the generation AI execute the application of the guideline generation algorithm.

[0083] The generation unit can estimate the user's emotions and adjust the length of the guidelines based on the estimated emotions. For example, if the user is stressed, the generation unit can generate short, concise guidelines. For example, the generation unit can capture the user's facial expressions with a camera and use an emotion estimation algorithm to determine if the user is stressed. The generation unit can also generate longer guidelines with more detailed explanations if the user is relaxed. For example, the generation unit can record the user's voice and use voice analysis technology to determine if the user is relaxed. Furthermore, if the user is in a hurry, the generation unit can generate concise, quickly understandable guidelines. For example, the generation unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and use an emotion estimation algorithm to determine if the user is in a hurry. This allows for the provision of more appropriate guidelines by adjusting the length of the guidelines according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user emotion data into a generation AI and have the generation AI perform emotion estimation.

[0084] The generation unit can determine the priority of guidelines based on the timing of cloud service deployment when generating guidelines. For example, the generation unit will prioritize generating guidelines for cloud services that are scheduled for deployment in the near future. For example, the generation unit will evaluate the timing of cloud service deployment based on criteria such as project schedule, release date, and deployment plan, and provide guidelines preferentially to cloud services that are scheduled for deployment in the near future. The generation unit can also postpone generating guidelines for cloud services that are scheduled for deployment in the distant future. For example, the generation unit will provide guidelines for cloud services that are scheduled for deployment in the distant future, including the minimum necessary security measures. Furthermore, the generation unit can generate guidelines that appropriately include the necessary information according to the deployment timing. For example, the generation unit will provide guidelines that include detailed security settings and monitoring methods based on the deployment timing of cloud services. This ensures that guidelines are provided at the appropriate time by determining the priority of guidelines based on the deployment timing of cloud services. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input cloud service deployment timing data into a generation AI and have the generation AI perform the determination of guideline priority.

[0085] The generation unit can adjust the order of guidelines based on the relevance of cloud services when generating them. For example, the generation unit can prioritize generating guidelines for highly relevant cloud services. For instance, the generation unit can evaluate the relevance of cloud services based on criteria such as overlapping functions, data sharing, and interoperability, and prioritize providing guidelines for highly relevant cloud services. The generation unit can also postpone generating guidelines for less relevant cloud services. For example, the generation unit can provide guidelines for less relevant cloud services that include only the minimum necessary security measures. Furthermore, the generation unit can generate guidelines that appropriately include the necessary information depending on the relevance of the cloud services. For example, the generation unit can provide guidelines that include detailed security settings and monitoring methods based on the relevance of the cloud services. This allows the guidelines to be provided in the appropriate order by adjusting the order of the guidelines based on the relevance of the cloud services. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input cloud service relevance data into a generation AI and have the generation AI perform the adjustment of the guideline order.

[0086] The encryption verification unit can estimate the user's emotions and adjust the verification criteria for the encryption method based on the estimated emotions. For example, if the user is stressed, the encryption verification unit can relax the verification criteria and perform verification quickly. For example, the encryption verification unit can capture the user's facial expression with a camera and use an emotion estimation algorithm to determine if the user is stressed. The encryption verification unit can also apply strict verification criteria and perform detailed verification if the user is relaxed. For example, the encryption verification unit can record the user's voice and use voice analysis technology to determine if the user is relaxed. Furthermore, if the encryption verification unit is in a hurry, it can optimize the verification criteria and perform verification quickly. For example, the encryption verification unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and use an emotion estimation algorithm to determine if the user is in a hurry. This allows for efficient verification by adjusting the verification criteria for the encryption method 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the encryption verification unit may be performed using AI, for example, or without AI. For example, the encryption verification unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0087] The encryption verification unit can improve the accuracy of encryption method verification by considering the data characteristics of the cloud service when verifying the encryption method. For example, the encryption verification unit selects and verifies an appropriate encryption method based on the data characteristics of the cloud service. For example, if the data of the cloud service is text data, the encryption verification unit verifies whether AES encryption is used. The encryption verification unit can also verify whether RSA encryption is used if the data of the cloud service is image data. Furthermore, the encryption verification unit can also verify whether TLS encryption is used if the data of the cloud service is video data. For example, if the encryption verification unit is text data, it verifies whether AES encryption is used. The encryption verification unit can also verify whether RSA encryption is used if the data of the cloud service is image data. Furthermore, the encryption verification unit can also verify whether TLS encryption is used if the data of the cloud service is video data. This improves the accuracy of encryption method verification by considering the data characteristics of the cloud service. Some or all of the above processing in the encryption verification unit may be performed using AI, for example, or without AI. For example, the encryption verification unit can input cloud service data characteristics data into a generating AI and have the generating AI perform the encryption method verification.

[0088] The encryption verification unit can estimate the user's emotions and adjust the order in which it displays the verification results of the encryption method based on the estimated emotions. For example, if the user is stressed, the encryption verification unit can display important verification results first to enable a quick response. For example, the encryption verification unit can capture the user's facial expression with a camera and use an emotion estimation algorithm to determine whether the user is stressed. The encryption verification unit can also display detailed verification results in order if the user is relaxed. For example, the encryption verification unit can record the user's voice and use voice analysis technology to determine whether the user is relaxed. Furthermore, if the user is in a hurry, the encryption verification unit can prioritize displaying the most important verification results. For example, the encryption verification unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and use an emotion estimation algorithm to determine whether the user is in a hurry. This allows for an efficient response by adjusting the display order of verification results 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the encryption verification unit may be performed using AI, for example, or without AI. For example, the encryption verification unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0089] The encryption verification unit can perform encryption method verification while considering the geographical distribution of cloud services. For example, the encryption verification unit can verify the encryption method while considering the location of the cloud service's data center. For example, the encryption verification unit maps the location of the cloud service's data center on a map and evaluates the impact of the location on security risks. The encryption verification unit can also verify the encryption method while considering the geographical distribution of cloud service users. For example, the encryption verification unit analyzes the locations of cloud service users and evaluates the impact of geographical distribution on security risks. Furthermore, the encryption verification unit can analyze the data transfer path of the cloud service, evaluate geographical risks, and verify the encryption method. For example, the encryption verification unit analyzes the data transfer path of the cloud service and evaluates the impact of geographical risks on security. This improves the accuracy of encryption method verification by considering the geographical distribution of cloud services. Some or all of the above processing in the encryption verification unit may be performed using AI, for example, or without AI. For example, the encryption verification unit can input geographical distribution data of cloud services into a generating AI and have the generating AI perform the encryption method verification.

[0090] The access control verification unit can estimate the user's emotions and adjust the access control verification criteria based on the estimated emotions. For example, if the user is stressed, the access control verification unit can relax the verification criteria and perform verification quickly. For example, the access control verification unit can capture the user's facial expression with a camera and use an emotion estimation algorithm to determine if the user is stressed. The access control verification unit can also apply strict verification criteria and perform detailed verification if the user is relaxed. For example, the access control verification unit can record the user's voice and use voice analysis technology to determine if the user is relaxed. Furthermore, if the user is in a hurry, the access control verification unit can optimize the verification criteria and perform verification quickly. For example, the access control verification unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and use an emotion estimation algorithm to determine if the user is in a hurry. This allows for efficient verification by adjusting the access control verification criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the access control verification unit may be performed using AI, or not using AI. For example, the access control verification unit can input user emotion data into the generation AI and have the generation AI perform emotion estimation.

[0091] The access control verification unit can improve the accuracy of access control verification by considering the user attributes of the cloud service. For example, the access control verification unit selects and verifies an appropriate access control method based on the user attributes of the cloud service. For example, if the user of the cloud service is an administrator, the access control verification unit verifies whether administrator privileges are set appropriately. The access control verification unit can also verify whether general user privileges are set appropriately if the user of the cloud service is a general user. Furthermore, if the user of the cloud service is a guest user, the access control verification unit can also verify whether guest user privileges are set appropriately. For example, if the user of the cloud service is an administrator, the access control verification unit verifies whether administrator privileges are set appropriately. The access control verification unit can also verify whether general user privileges are set appropriately if the user of the cloud service is a general user. Furthermore, if the user of the cloud service is a guest user, the access control verification unit can also verify whether guest user privileges are set appropriately. This improves the accuracy of access control verification by considering the user attributes of the cloud service. Some or all of the above processing in the access control verification unit may be performed using AI, for example, or without using AI. For example, the access control verification unit can input user attribute data from the cloud service into the generating AI and have the generating AI perform access control verification.

[0092] The access control verification unit can estimate the user's emotions and adjust the order in which it displays access control verification results based on the estimated emotions. For example, if the user is stressed, the access control verification unit can display important verification results first to enable a quick response. For example, the access control verification unit can capture the user's facial expression with a camera and use an emotion estimation algorithm to determine if the user is stressed. The access control verification unit can also display detailed verification results in a sequential manner if the user is relaxed. For example, the access control verification unit can record the user's voice and use voice analysis technology to determine if the user is relaxed. Furthermore, if the user is in a hurry, the access control verification unit can prioritize displaying the most important verification results. For example, the access control verification unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and use an emotion estimation algorithm to determine if the user is in a hurry. This allows for an efficient response by adjusting the display order of verification results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the access control verification unit may be performed using AI, or not using AI. For example, the access control verification unit can input user emotion data into the generation AI and have the generation AI perform emotion estimation.

[0093] The access control verification unit can perform access control verification while considering the geographical distribution of cloud services. For example, the access control verification unit can verify the access control method while considering the location of the cloud service's data center. For example, the access control verification unit maps the location of the cloud service's data center on a map and evaluates the impact of the location on security risks. The access control verification unit can also verify the access control method while considering the geographical distribution of cloud service users. For example, the access control verification unit analyzes the location of cloud service users and evaluates the impact of geographical distribution on security risks. Furthermore, the access control verification unit can analyze the data transfer path of the cloud service, evaluate geographical risks, and verify the access control method. For example, the access control verification unit analyzes the data transfer path of the cloud service and evaluates the impact of geographical risks on security. As a result, considering the geographical distribution of cloud services improves the accuracy of access control verification. Some or all of the above processing in the access control verification unit may be performed using AI, for example, or without AI. For example, the access control verification unit can input geographical distribution data of cloud services into a generating AI and have the generating AI perform the access control method verification.

[0094] The audit log verification unit can estimate the user's emotions and adjust the audit log verification criteria based on the estimated emotions. For example, if the user is stressed, the audit log verification unit can relax the verification criteria and perform a quicker verification. For example, the audit log verification unit can capture the user's facial expression with a camera and use an emotion estimation algorithm to determine if the user is stressed. Conversely, if the user is relaxed, the audit log verification unit can apply stricter verification criteria for a more detailed verification. For example, the audit log verification unit can record the user's voice and use voice analysis technology to determine if the user is relaxed. Furthermore, if the user is in a hurry, the audit log verification unit can optimize the verification criteria for a quicker verification. For example, the audit log verification unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and use an emotion estimation algorithm to determine if the user is in a hurry. This allows for efficient verification by adjusting the audit log verification criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generating AI may be a text generating AI (e.g., LLM) or a multimodal generating AI, but is not limited to such examples. Some or all of the processing described above in the audit log verification unit may be performed using AI, or not using AI. For example, the audit log verification unit may input user sentiment data into the generating AI and have the generating AI perform sentiment estimation.

[0095] The audit log verification unit can improve the accuracy of audit log verification by considering the log characteristics of cloud services. For example, the audit log verification unit selects and verifies an appropriate verification method based on the log characteristics of the cloud service. For example, if the cloud service log is an access log, the audit log verification unit verifies the retention period and analysis method of the access log. The audit log verification unit can also verify the retention period and analysis method of the error log if the cloud service log is an error log. Furthermore, if the cloud service log is an audit log, the audit log verification unit can also verify the retention period and analysis method of the audit log. For example, if the cloud service log is an access log, the audit log verification unit verifies the retention period and analysis method of the access log. Furthermore, if the cloud service log is an error log, the audit log verification unit can also verify the retention period and analysis method of the error log. Furthermore, if the cloud service log is an audit log, the audit log verification unit can also verify the retention period and analysis method of the audit log. In this way, the accuracy of audit log verification is improved by considering the log characteristics of cloud services. Some or all of the above-described processes in the audit log verification unit may be performed using AI, for example, or without AI. For example, the audit log verification unit can input cloud service log characteristic data into a generating AI and have the generating AI perform audit log verification.

[0096] The audit log verification unit can estimate the user's emotions and adjust the order in which the audit log verification results are displayed based on the estimated emotions. For example, if the user is stressed, the audit log verification unit can display important verification results first to enable a quick response. For example, the audit log verification unit can capture the user's facial expression with a camera and use an emotion estimation algorithm to determine if the user is stressed. The audit log verification unit can also display detailed verification results in order if the user is relaxed. For example, the audit log verification unit can record the user's voice and use voice analysis technology to determine if the user is relaxed. Furthermore, if the user is in a hurry, the audit log verification unit can display the most important verification results with the highest priority. For example, the audit log verification unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and use an emotion estimation algorithm to determine if the user is in a hurry. This allows for an efficient response by adjusting the display order of verification results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generating AI may be a text generating AI (e.g., LLM) or a multimodal generating AI, but is not limited to such examples. Some or all of the processing described above in the audit log verification unit may be performed using AI, or not using AI. For example, the audit log verification unit may input user sentiment data into the generating AI and have the generating AI perform sentiment estimation.

[0097] The audit log verification unit can perform audit log verification while considering the geographical distribution of cloud services. For example, the audit log verification unit can perform audit log verification while considering the location of the cloud service's data center. For example, the audit log verification unit can map the locations of the cloud service's data centers on a map and evaluate the impact of the location on security risks. The audit log verification unit can also perform audit log verification while considering the geographical distribution of cloud service users. For example, the audit log verification unit can analyze the locations of cloud service users and evaluate the impact of geographical distribution on security risks. Furthermore, the audit log verification unit can analyze the data transfer paths of cloud services and evaluate geographical risks before performing audit log verification. For example, the audit log verification unit can analyze the data transfer paths of cloud services and evaluate the impact of geographical risks on security. This improves the accuracy of audit log verification by considering the geographical distribution of cloud services. Some or all of the above processing in the audit log verification unit may be performed using AI, for example, or without AI. For example, the audit log verification unit can input geographical distribution data of cloud services into a generating AI and have the generating AI perform audit log verification. === Hard Collateral 1-1 === Each of the multiple elements described above, including the reception unit, judgment unit, and generation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14, which inputs information about the cloud service into an input form and transmits the information by pressing the send button. The judgment unit is implemented by the identification processing unit 290 of the data processing unit 12, which analyzes the input information and determines the security compliance requirements. Furthermore, the generation unit is implemented by the identification processing unit 290 of the data processing unit 12, which generates guidelines based on the determined results. === Hard Collateral 1-2 === Each of the multiple elements described above, including the reception unit, judgment unit, and generation unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214, which inputs information about the cloud service into an input form and transmits the information by pressing the send button. The judgment unit is implemented by the identification processing unit 290 of the data processing unit 12, which analyzes the input information and determines the security compliance requirements. Furthermore, the generation unit is implemented by the identification processing unit 290 of the data processing unit 12, which generates guidelines based on the determined results. === Hard Collateral 1-3 === Each of the multiple elements described above, including the reception unit, judgment unit, and generation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314, which inputs information about the cloud service into an input form and transmits the information by pressing the send button. The judgment unit is implemented by the identification processing unit 290 of the data processing unit 12, which analyzes the input information and determines the security compliance requirements. Furthermore, the generation unit is implemented by the identification processing unit 290 of the data processing unit 12, which generates guidelines based on the determined results. === Hard Collateral 1-4 === Each of the multiple elements described above, including the reception unit, judgment unit, and generation unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414, which inputs information about the cloud service into an input form and transmits the information by pressing the send button. The judgment unit is implemented by the identification processing unit 290 of the data processing unit 12, which analyzes the input information and determines the security compliance requirements. Furthermore, the generation unit is implemented by the identification processing unit 290 of the data processing unit 12, which generates guidelines based on the determined results.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The reception desk can analyze a user's past input history and propose the optimal input method. For example, the reception desk can store past input history in a database and analyze successful cases. It can also analyze past failures and select input methods to avoid similar mistakes. Furthermore, the reception desk can propose input methods suitable for specific industries or company sizes based on past input history. In this way, the optimal information input method can be selected by analyzing past input history. Some or all of the above processes in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input past input history data into a generating AI and have the generating AI select the optimal information input method.

[0100] The encryption verification unit can select and verify an appropriate encryption method based on the data characteristics of the cloud service. For example, if the data of the cloud service is text data, the encryption verification unit can verify whether AES encryption is being used. It can also verify whether RSA encryption is being used if the data of the cloud service is image data. Furthermore, if the data of the cloud service is video data, the encryption verification unit can verify whether TLS encryption is being used. This improves the accuracy of encryption method verification by considering the data characteristics of the cloud service. Some or all of the above processing in the encryption verification unit may be performed using AI, for example, or without AI. For example, the encryption verification unit can input data characteristic data of the cloud service into a generating AI and have the generating AI perform the encryption method verification.

[0101] The access control verification unit can select and verify an appropriate access control method based on the user attributes of the cloud service. For example, if the user of the cloud service is an administrator, the access control verification unit can verify whether administrator privileges are set appropriately. The access control verification unit can also verify whether general user privileges are set appropriately if the user of the cloud service is a general user. Furthermore, if the user of the cloud service is a guest user, the access control verification unit can verify whether guest user privileges are set appropriately. This improves the accuracy of access control verification by considering the user attributes of the cloud service. Some or all of the above processing in the access control verification unit may be performed using AI, for example, or without AI. For example, the access control verification unit can input cloud service user attribute data into a generating AI and have the generating AI perform the access control verification.

[0102] The audit log verification unit can select and verify an appropriate verification method based on the log characteristics of the cloud service. For example, if the cloud service log is an access log, the audit log verification unit can verify the retention period and analysis method of the access log. Furthermore, if the cloud service log is an error log, the audit log verification unit can also verify the retention period and analysis method of the error log. In addition, if the cloud service log is an audit log, the audit log verification unit can also verify the retention period and analysis method of the audit log. This improves the accuracy of audit log verification by considering the log characteristics of the cloud service. Some or all of the above processing in the audit log verification unit may be performed using AI, for example, or without AI. For example, the audit log verification unit can input cloud service log characteristic data into a generating AI and have the generating AI perform the audit log verification.

[0103] The generation unit can adjust the level of detail in the guidelines based on the importance of the cloud services. For example, it can generate detailed guidelines for highly important cloud services. The generation unit evaluates the importance of cloud services based on criteria such as business impact, data confidentiality, and service availability, and provides detailed guidelines for highly important cloud services. The generation unit can also generate concise guidelines for less important cloud services. For example, it can provide concise guidelines for less important cloud services, including only the minimum necessary security measures. By adjusting the level of detail in the guidelines according to the importance of the cloud services, appropriate guidelines can be provided. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input cloud service importance data into a generation AI and have the generation AI adjust the level of detail in the guidelines.

[0104] The reception desk can estimate the user's emotions and adjust the timing of cloud service information input based on the estimated emotions. For example, if the user is stressed, the input timing can be delayed to provide time for relaxation. The reception desk captures the user's facial expression with a camera and uses an emotion estimation algorithm to determine if the user is stressed. Furthermore, if the user is relaxed, the reception desk can speed up the input timing to efficiently collect information. For example, the reception desk can record the user's voice and use voice analysis technology to determine if the user is relaxed. Additionally, if the user is in a hurry, the reception desk can optimize the input timing to quickly collect information. For example, the reception desk can collect the user's biometric data (heart rate and skin electrical activity) with sensors and use an emotion estimation algorithm to determine if the user is in a hurry. This allows for efficient information collection by adjusting the input timing 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the reception area may be performed using AI, for example, or without AI. For example, the reception area can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0105] The decision-making unit can estimate the user's emotions and adjust security compliance criteria based on those emotions. For example, if the user is stressed, the criteria can be relaxed to make a quick decision. The decision-making unit can capture the user's facial expressions with a camera and use an emotion estimation algorithm to determine if the user is stressed. The decision-making unit can also apply stricter criteria to make a more detailed decision if the user is relaxed. For example, the decision-making unit can record the user's voice and use voice analysis technology to determine if the user is relaxed. Furthermore, if the user is in a hurry, the decision-making unit can optimize the criteria to make a quick decision. For example, the decision-making unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and use an emotion estimation algorithm to determine if the user is in a hurry. This allows for efficient security compliance decisions by adjusting the criteria according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0106] The generation unit can estimate the user's emotions and adjust the way the guidelines are expressed based on those emotions. For example, if the user is stressed, it can adopt a simple and easy-to-understand expression. The generation unit can capture the user's facial expressions with a camera and use an emotion estimation algorithm to determine if the user is stressed. Furthermore, if the user is relaxed, the generation unit can adopt an expression that includes detailed explanations. For example, it can record the user's voice and use voice analysis technology to determine if the user is relaxed. Additionally, if the user is in a hurry, the generation unit can adopt a concise and to-the-point expression. For example, it can collect the user's biometric data (heart rate and skin electrical activity) with sensors and use an emotion estimation algorithm to determine if the user is in a hurry. This allows for the provision of more appropriate guidelines by adjusting their expression 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user emotion data into a generation AI and have the generation AI perform emotion estimation.

[0107] The encryption verification unit can estimate the user's emotions and adjust the verification criteria for the encryption method based on the estimated emotions. For example, if the user is stressed, the verification criteria can be relaxed for faster verification. The encryption verification unit captures the user's facial expression with a camera and uses an emotion estimation algorithm to determine if the user is stressed. Furthermore, if the user is relaxed, the encryption verification unit can apply stricter verification criteria for more detailed verification. For example, the encryption verification unit records the user's voice and uses voice analysis technology to determine if the user is relaxed. Additionally, if the user is in a hurry, the encryption verification unit can optimize the verification criteria for faster verification. For example, the encryption verification unit collects the user's biometric data (heart rate and skin electrical activity) with sensors and uses an emotion estimation algorithm to determine if the user is in a hurry. This allows for efficient verification by adjusting the encryption method verification criteria 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the encryption verification unit may be performed using, for example, AI. This process may be carried out without using AI. For example, the encryption verification unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0108] The access control verification unit can estimate the user's emotions and adjust the access control verification criteria based on the estimated emotions. For example, if the user is stressed, the verification criteria can be relaxed for faster verification. The access control verification unit captures the user's facial expression with a camera and uses an emotion estimation algorithm to determine if the user is stressed. Furthermore, if the user is relaxed, the access control verification unit can apply stricter verification criteria for more detailed verification. For example, the access control verification unit records the user's voice and uses voice analysis technology to determine if the user is relaxed. Additionally, if the user is in a hurry, the access control verification unit can optimize the verification criteria for faster verification. For example, the access control verification unit collects the user's biometric data (heart rate and skin electrical activity) with sensors and uses an emotion estimation algorithm to determine if the user is in a hurry. This allows for efficient verification by adjusting the access control verification criteria 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the access control verification unit may be performed using AI, for example, or without AI. For example, the access control verification unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The reception desk enters information about the cloud service. This information includes the service name, provider, features, and price. The reception desk can enter the information into a form, upload it as a file, or retrieve it via an API. For example, the information can be submitted by entering it into an input form and pressing the submit button. Alternatively, it can be uploaded as a CSV file and automatically analyzed by the system. When retrieving information via an API, a request is sent to the API endpoint provided by the cloud service provider to retrieve the information. Step 2: The decision-making unit analyzes the information entered by the reception unit and determines the security compliance requirements. Security compliance requirements include data protection regulations, access control, and encryption standards. For example, it checks whether encryption technologies such as AES or RSA are used as the data encryption method, whether user authentication and permission management are properly implemented as access control settings, and whether the log retention period and analysis method are properly set as audit log management methods. Step 3: The generation unit generates implementation guidelines based on the results determined by the judgment unit. The guidelines include recommendations for security settings and methods for monitoring after implementation. For example, the guidelines may include recommendations for security settings such as password policies, firewall settings, and antivirus software. The guidelines may also include methods for monitoring after implementation such as periodic security scans, log monitoring, and anomaly detection.

[0111] 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.

[0112] 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 the following. 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 (for example, 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. 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 a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0113] 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.

[0114] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.).

[0127] 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.

[0128] 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. 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.

[0129] 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.

[0130] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.).

[0143] 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.

[0144] 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. 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.

[0145] 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.

[0146] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.).

[0160] 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.

[0161] 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. 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.

[0162] 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.

[0163] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] [Explanation of symbols]

[0183] 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. A reception area for entering information about cloud services, A judgment unit analyzes the information entered by the aforementioned reception unit and determines the security compliance requirements, The system includes a generation unit that generates guidelines for implementation based on the results determined by the aforementioned determination unit. A system characterized by the following features.

2. The unit that makes the determination said, It includes an encryption verification unit to check the data encryption method. The system according to feature 1.

3. The unit that makes the determination said, It includes an access control confirmation unit to verify the access control settings. The system according to feature 1.

4. The unit that makes the determination said, It includes an audit log verification section to check how audit logs are managed. The system according to feature 1.

5. The generating unit is Generate guidelines that include security setting recommendations. The system according to feature 1.

6. The generating unit is Generate guidelines that include post-implementation monitoring methods. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of cloud service information input based on the estimated user emotions. The system according to feature 1.

8. The aforementioned reception unit is We analyze past cloud service implementation history and select the optimal information input method. The system according to feature 1.

9. The aforementioned reception unit is When entering cloud service information, filtering is performed based on the user's current work situation and areas of interest. The system according to feature 1.

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