GPT-based multi-agent logging system for automated data protection audits

Abstract

GPT-based multi-agent logging system for automated data protection audits, consisting of: a data analysis module configured to: Input data obtained from a questionnaire completed by an IT department manager; to analyze the received input data in order to identify data protection-related problems; Use natural language processing techniques to analyze and understand the input data; a module for building expert knowledge, configured to: Providing sector-specific expertise on data protection based on a curated database of data protection laws, regulations and best practices; Regularly maintain and update the curated database to ensure current and relevant information; to provide the system with expertise in privacy protection in order to generate accurate recommendations; a module for generating recommendations, configured to: Combine the analyzed data from the data analysis module with the expert knowledge from the "Expert Knowledge" module; Apply rule-based decision-making processes to develop initial recommendations; to improve the recommendations using AI-driven algorithms to tailor them to the specific needs of the company; a module for creating formatted reports, configured to: compile the recommendations generated by the recommendation generator module; to summarize the collected recommendations in a structured and professional report; Use of formatting templates to ensure consistency, readability, and easy interpretation of the final report.

Description

[0001] The present invention relates to a field of artificial intelligence and, more particularly, to a GPT-based multi-agent protocol system for automatic privacy checking.

[0002] Data protection audits are essential for compliance with data protection regulations and the protection of sensitive information. Traditional data protection review processes are often manual, time-consuming, and require a high level of expertise. There is a need for an automated system that can conduct data protection audits efficiently and accurately, providing comprehensive reports tailored to the needs of different sectors. Performing these audits manually not only consumes valuable time and resources, but also increases the risk of overlooking critical data protection aspects, which can lead to non-compliance and data breaches.

[0003] To address these challenges, the invention proposes an automated system that leverages AI and natural language processing (NLP) techniques to streamline data protection audits. By leveraging a multi-agent protocol built on GPT-4 language models, the system can efficiently analyze data, apply expert knowledge, generate tailored privacy recommendations, and produce comprehensive, formatted reports. This approach not only improves the accuracy and efficiency of data protection audits but also makes the process scalable and adaptable to the specific requirements of different sectors, ultimately providing companies with a reliable tool for protecting sensitive data and complying with regulatory requirements.

[0004] To solve this problem, the present invention provides a GPT-based multi-agent protocol system for automated privacy checking.

[0005] The GPT-based multi-agent protocol system for automated privacy review is designed to streamline and improve the privacy review process by leveraging artificial intelligence to analyze data, apply expert knowledge, generate recommendations, and produce formatted reports.

[0006] The GPT-based multi-agent protocol system for automated privacy auditing can efficiently analyze input data, identify privacy issues, and generate comprehensive reports without requiring extensive human intervention.

[0007] The GPT-based multi-agent protocol system for automated privacy checks is designed to apply domain-specific data protection laws, regulations, and best practices, ensuring that the generated recommendations are accurate, relevant, and compliant with current standards.

[0008] The GPT-based multi-agent protocol system for automated privacy audits can perform thorough privacy audits faster, allowing you to use your resources more effectively.

[0009] The GPT-based multi-agent protocol system for automated privacy checks was developed to meet the specific privacy needs of various sectors, including small businesses, schools, hospitals, and public services.

[0010] The GPT-based multi-agent protocol system for automated privacy audits can be effectively implemented in various types of organizations, from small businesses to large public services, by adapting the privacy audit process according to their specific requirements.

[0011] In one embodiment, a GPT-based multi-agent protocol system for automated privacy audits is provided. This system addresses the inefficiencies of traditional, manual privacy audit processes, which are often time-consuming and require significant expertise. The system consists of four interconnected modules: the data analyzer, the expert knowledge, the recommendation generator, and the formatted report generator. These modules work together to analyze input data, apply expert knowledge of data protection laws and best practices, generate tailored recommendations, and produce a comprehensive, formatted privacy audit report.

[0012] In one embodiment, the system offers several benefits through the use of artificial intelligence, including increased efficiency, accuracy, and scalability. It is designed to meet the specific data protection needs of various sectors, such as small businesses, schools, hospitals, and public services. The automation offered by this system not only saves time and resources but also ensures that the reports generated are consistent, easy to understand, and actionable, making it a valuable tool for organizations seeking to comply with data protection regulations.

[0013] The invention is explained again below with reference to the figures. They show: Fig. : Illustrates a block diagram of a GPT-based multi-agent protocol system for automated privacy checking. Fig. : Illustrates the interaction between different modules of a GPT-based multi-agent protocol system for automated data protection checks.

[0014] Fig. : Illustrates a block diagram of a GPT-based multi-agent protocol system for automatic privacy checks. The system (100) includes a scalable blockchain network, an advanced cryptographic techniques module, an optimized cryptographic algorithm module, a digital identity verification mechanism module, a decentralized architecture module, a module for interoperability features, a real-time monitoring and auditing module, a user-friendly interface module, and a multi-factor authentication (MFA) module. The scalable blockchain network is configured to handle a large number of users and transactions without compromising security or integrity. The advanced cryptographic techniques module is configured to enhance security and privacy.The optimized cryptographic algorithm module, configured to improve performance and efficiency. The digital identity verification mechanism module, configured to ensure accurate and reliable identity verification. The decentralized architecture module, configured to improve performance and stability. The interoperability features module, configured to enable seamless integration and cross-platform functionality. The real-time monitoring and auditing module, configured to ensure continuous security and compliance. The user-friendly interface module, which improves accessibility and usability. The multi-factor authentication (MFA) module, which increases security against unauthorized access.

[0015] Fig.: Illustrates the interaction (101) between different modules of a GPT-based multi-agent protocol system for automated privacy checks. The diagram shows a process in which a questionnaire is submitted and then analyzed, with expert knowledge provided to improve the analysis. Recommendations are generated and compiled into a formatted report, which is then delivered as the final product. The process emphasizes a collaborative approach that includes data analysis, expert input, recommendation generation, and report formatting to produce a comprehensive report.

[0016] The system (100) consists of four different modules, each with a specific task. The data analysis module begins the process by examining input data provided via a questionnaire completed by a company's head of IT. It identifies the most significant data protection issues that need to be resolved. The expert body of knowledge module acts as a repository of domain-specific knowledge, providing guidance on relevant data protection laws, regulations, and best practices. This expert knowledge is then combined with the analyzed data by the recommendation generator, which creates tailored recommendations for the organization's needs. Finally, the formatted report generation module summarizes these recommendations in a professional, easy-to-understand report that can be used directly by the organization to improve its data protection practices.

[0017] The system's strength lies not only in its ability to automate the data protection audit process, but also in its adaptability and scalability across diverse sectors with varying data protection requirements. By replacing manual effort with AI-driven analysis and expert guidance, the system ensures that data protection audits are conducted with a high degree of accuracy and consistency. The final reports are clear, actionable, and designed to meet each organization's specific regulatory and operational requirements, significantly reducing the burden on internal teams while improving compliance and data protection. List of reference symbols 100 systems

Claims

[1] GPT-based multi-agent protocol system for automated privacy checks, consisting of: a data analysis module configured to: Obtained input data from a questionnaire completed by an IT department manager; analyze the received input data to identify data protection issues; Use natural language processing techniques to analyze and understand the input data; an expert knowledge building module configured to: Providing domain-specific data protection expertise based on a curated database of data protection laws, regulations and best practices; regularly maintain and update the curated database to ensure current and relevant information; provide the system with privacy expertise to make accurate recommendations; a recommendation generation module configured to: combine the analyzed data from the data analysis module with the expert knowledge from the expert knowledge module; apply rule-based decision-making processes to develop initial recommendations; improve recommendations using AI-driven algorithms to tailor them to the company's specific needs; a formatted report generation module configured to: compile the recommendations generated by the recommendation generator module; to summarize the collected recommendations in a structured and professional report; Use of formatting templates to ensure consistency, readability, and easy interpretation of the final report. [2] The system (100) of claim 1, wherein the data analysis module further includes a preprocessing submodule for cleaning and standardizing the input data. [3] The system (100) of claim 1, wherein the expert knowledge module further includes a knowledge retrieval sub-module for searching and applying relevant data protection laws and regulations from a curated database. [4] The system (100) of claim 1, wherein the recommendation generator module further includes an AI sub-module that refines the recommendations using machine learning based on previous rating data. [5] The system (100) of claim 1, wherein the formatted report generator module further includes a layout management sub-module that customizes the format of the final report according to organizational preferences.

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