Cloud security vulnerability management platform based on AI

Abstract

The invention relates to the technical field of information security, in particular to a cloud security vulnerability management platform based on AI. Comprising a centralized management module, a distributed event collector network, an automatic vulnerability assessment system, an intelligent repair suggestion module, an interactive user interface, a self-adaptive vulnerability protection mechanism, a continuous learning and updating mechanism, a multi-level identity verification system, an automatic security test tool set and an extensible plug-in architecture. The AI-based cloud security vulnerability management platform provided by the invention is more comprehensive, intelligent and flexible, so that the problems are solved, and the security of a cloud environment and the use experience of a user are improved.

Description

Technical Field

[0001] The present invention relates to the field of information security technology, and particularly to a cloud security vulnerability management platform based on AI. Background Art

[0002] With the rapid development and wide application of cloud computing technology, information security issues in the cloud environment have become increasingly prominent. Cloud services provide powerful computing and storage capabilities, enabling a large number of individual and enterprise users to enjoy convenient online services. However, the complexity, dynamics, and openness of the cloud computing environment have also brought unprecedented challenges to security protection. In particular, the management and repair of security vulnerabilities have become a key link in maintaining the security of the cloud computing environment. Traditional security vulnerability management methods mainly rely on manual analysis and processing, which is inefficient in the face of an increasing number of security incidents and is prone to misjudgment or omission, resulting in serious security risks in the cloud environment.

[0003] In recent years, with the development of artificial intelligence (AI) technology, especially the application of machine learning and deep learning technologies, the security field has begun to introduce AI technology to assist or replace traditional manual methods. AI technology can automatically identify and evaluate potential security threats in the cloud environment by learning a large amount of historical vulnerability data, and provide intelligent repair suggestions, thus greatly improving the efficiency and accuracy of vulnerability management.

[0004] However, existing AI-based security vulnerability management solutions usually have the following problems:

[0005] (1) Incomplete data collection and processing: Most solutions can only collect and process limited data sources, such as specific log files or system calls, and it is difficult to cover all possible security events in the cloud computing environment. In addition, there are often insufficient security measures for data encrypted transmission and storage, which may lead to the risk of data leakage.

[0006] (2) Insufficient accuracy of the automatic evaluation system: Existing automatic evaluation systems often rely on fixed models and evaluation criteria, and cannot flexibly adapt to the security needs of different enterprises or organizations. In addition, there are certain limitations in the extraction and classification of vulnerability information, and it is difficult to accurately evaluate the severity and potential risks of each vulnerability.

[0007] (3) Lack of personalization and pre-verification for repair suggestions: Most security vulnerability management platforms provide general repair suggestions, lacking personalized guidance for the specific environment of users. At the same time, the quality and compatibility of repair solutions are usually difficult to guarantee, resulting in a high failure rate during implementation.

[0008] (4) Immature adaptive protection mechanism: The existing adaptive protection mechanisms mainly rely on pre-set rule libraries, with weak recognition capabilities for unknown attack methods and difficulty in achieving forward-looking security protection.

[0009] (5) Single authentication mechanism: Most current security platforms adopt relatively single authentication methods, such as only password authentication, lacking multi-level authentication means and being difficult to effectively prevent illegal access and internal threats.

[0010] (6) Limited automated testing function: Although some platforms provide certain automated security testing tools, these tools usually have single functions and weak customization capabilities, unable to meet the diverse testing needs of users.

[0011] (7) Closed plugin architecture: Security vulnerability management platforms on the market often adopt closed designs, lacking an extensible plugin architecture, making it difficult for third-party developers to develop new functional modules according to specific requirements. Summary of the Invention

[0012] The present invention provides a more comprehensive, intelligent, and flexible AI-based cloud security vulnerability management platform to solve the above problems and improve the security of the cloud environment and the user experience.

[0013] The technical solution adopted by the present invention is: an AI-based cloud security vulnerability management platform, including a centralized management module, a distributed event collector network, an automatic vulnerability assessment system, an intelligent repair suggestion module, an interactive user interface, an adaptive vulnerability protection mechanism, a continuous learning and updating mechanism, a multi-level authentication system, an automated security testing tool set, and an extensible plugin architecture.

[0014] The centralized management module is used to collect, process, and manage vulnerability information from multiple resources in the cloud computing environment, including but not limited to servers, storage devices, network services, and security devices. Through AI algorithms, it analyzes a large amount of data and automatically identifies potential security vulnerabilities to improve the security of the cloud environment and reduce human errors.

[0015] The distributed event collector network consists of lightweight agents located at different nodes in the cloud environment, responsible for real-time monitoring and collection of security event data on the nodes, including but not limited to log files, system calls, and network traffic, and transmitting it to the centralized management module through an encrypted channel to ensure the integrity and security of the data.

[0016] The automatic vulnerability assessment system uses machine learning algorithms to score and classify each identified security vulnerability according to the severity, impact range, and possible risks of the vulnerability information, providing priority guidance for subsequent vulnerability repairs.

[0017] The intelligent repair suggestion module provides detailed repair solutions for different types of vulnerabilities based on the security vulnerability cases stored in the database and their corresponding repair measures, combined with the latest security research results. The solutions include, but are not limited to, code-level modification suggestions, configuration adjustment guidelines, and patch installation steps, and can give personalized repair suggestions according to the specific environment of the user.

[0018] The interactive user interface is provided for administrators to monitor the platform operation status, view vulnerability assessment reports, manage repair suggestions, and implement security policies, and supports multi-language switching with a good user experience design.

[0019] When the platform detects a vulnerability, the adaptive vulnerability protection mechanism can automatically learn and adjust the protection strategy to cope with unknown threats and achieve proactive security protection.

[0020] The continuous learning and updating mechanism regularly updates the internal vulnerability information database and repair measure database of the platform through automatic synchronization with external security information sources to ensure the timeliness and accuracy of the data used.

[0021] The multi-level authentication system protects the security of the platform and data by implementing a multi-level authentication scheme from login authentication to operation authorization, including but not limited to two-factor authentication and biometric authentication, effectively preventing illegal access and operations.

[0022] The automated security testing toolset integrates automated tools for simulating attack and penetration testing scenarios to help users discover and solve vulnerabilities before they are exploited, and also supports custom testing scenarios to meet the needs of different users.

[0023] The extensible plug-in architecture designs an open plug-in interface that allows third-party developers to develop and integrate new function modules according to specific requirements, including specific types of vulnerability scanners and customized report generators, enhancing the functionality and flexibility of the platform.

[0024] As a further improvement of the present invention, the AI algorithm includes learning from historical vulnerability handling records to predict possible future security threat patterns; continuously tracking the subsequent impacts of repaired vulnerabilities to evaluate the repair effects; and understanding complex network environments and application program structures through deep learning technology to improve the vulnerability identification rate and accuracy.

[0025] As a further improvement of the present invention, the event collector network supports fine-grained log collection level settings, can adjust the data collection scope according to different requirements, is equipped with a data desensitization processing function to ensure that sensitive information will not be leaked during data transmission and storage, and can intelligently judge abnormal activities, including abnormal login attempts and illegal access requests, and immediately alarm the centralized management module.

[0026] As a further improvement of the present invention, the automatic vulnerability assessment system utilizes natural language processing technology to read and parse security bulletins, automatically extract vulnerability information and incorporate it into the assessment; supports users to customize assessment criteria to adapt to the security requirements and strategies of different enterprises; integrates a vulnerability duplicate checking function to avoid reporting the same vulnerability repeatedly and improve work efficiency.

[0027] As a further improvement of the present invention, the intelligent repair suggestion module provides a pre-verification function for repair solutions in an experimental environment to ensure the effectiveness and compatibility of repair measures; the repair solutions include detailed descriptions of implementation steps, so that even non-professionals can complete them according to the guidance; proposes an emergency response plan for high-risk vulnerabilities to guide users to solve problems in the shortest time and reduce losses.

[0028] As a further improvement of the present invention, the interactive user interface realizes data visualization, displays complex vulnerability assessment results in the form of charts for easy user understanding and decision-making; supports access from mobile device terminals to view alarm information and perform key operations; embeds online learning resources and forum links to promote technical communication and support among users.

[0029] As a further improvement of the present invention, the adaptive vulnerability protection mechanism has the ability of self-evolution, can identify new attack methods without clear instances; by real-time monitoring network traffic and system behavior, quickly responds to abnormal activities, and automatically blocks suspicious IP addresses and ports; is well integrated with existing security devices and can be seamlessly connected to the existing security framework of enterprises.

[0030] As a further improvement of the present invention, the continuous learning and updating mechanism supports incremental updates to avoid resource waste caused by full data refreshing; designs an intelligent screening algorithm to preferentially download and process more urgent and relevant security information; establishes cooperation relationships with many leading security research institutions at home and abroad to obtain cutting-edge research results.

[0031] As a further improvement of the present invention, the multi-level authentication system introduces behavior-based authentication technology to identify potential abnormal behaviors by analyzing user operation habits; supports role-based permission management and the principle of least privilege to ensure that each account can only access necessary system resources; provides a powerful log auditing function to track and record all operation processes for easy post-event responsibility tracing.

[0032] As a further improvement of the present invention, the extensible plug-in architecture defines clear API interface specifications to ensure the compatibility of third-party plug-ins with the core functions of the platform; establishes a plug-in market to collect and review third-party plug-ins for users to download and use; performs strict compatibility checks and security tests during the plug-in installation process to ensure the overall stability and security of the platform. Description of the Drawings

[0034] Figure 1 is the overall architecture diagram of a cloud security vulnerability management platform based on AI of the present invention Detailed implementation manners

[0036] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0037] The present invention provides a cloud security vulnerability management platform based on AI, including a centralized management module, a distributed event collector network, an automatic vulnerability assessment system, an intelligent repair suggestion module, an interactive user interface, an adaptive vulnerability protection mechanism, a continuous learning and updating mechanism, a multi-level authentication system, an automated security testing tool set, and an extensible plug-in architecture.

[0038] In the present invention, the centralized management module is used to collect, process and manage vulnerability information from multiple resources in the cloud computing environment, including but not limited to servers, storage devices, network services, and security devices. Through AI algorithms, a large amount of data is analyzed to automatically identify potential security vulnerabilities, so as to improve the security of the cloud environment and reduce human errors;

[0039] The AI algorithms include learning from historical vulnerability handling records to predict possible future security threat patterns; continuously tracking the subsequent impacts of repaired vulnerabilities and evaluating the repair effects; through deep learning technology, understanding complex network environments and application program structures to improve the vulnerability identification rate and accuracy.

[0040] In the present invention, the distributed event collector network is composed of lightweight agents located at different nodes in the cloud environment, and is responsible for real-time monitoring and collecting security event data on the nodes, including but not limited to log files, system calls, and network traffic, and transmitting them to the centralized management module through an encrypted channel to ensure the integrity and security of the data;

[0041] The event collector network supports the setting of fine-grained log collection levels, and can adjust the data collection scope according to different requirements; it is equipped with a data desensitization processing function to ensure that sensitive information will not be leaked during data transmission and storage; it can intelligently judge abnormal activities, including abnormal login attempts and illegal access requests, and immediately alarm the centralized management module.

[0042] In the present invention, the automatic vulnerability assessment system uses machine learning algorithms to score and classify each identified security vulnerability according to the severity, impact scope and possible risks of the vulnerability information, providing a priority guide for subsequent vulnerability repair;

[0043] The automatic vulnerability assessment system utilizes natural language processing technology to read and parse security bulletins, automatically extract vulnerability information and incorporate it into the assessment; supports users to customize assessment criteria to adapt to the security requirements and policies of different enterprises; integrates a vulnerability duplicate checking function to avoid reporting the same vulnerability repeatedly and improve work efficiency.

[0044] In the present invention, the intelligent repair suggestion module, based on the security vulnerability cases stored in the database and their corresponding repair measures, combines the latest security research results to provide detailed repair solutions for different types of vulnerabilities, including but not limited to code-level modification suggestions, configuration adjustment guidelines, and patch installation steps, and can give personalized repair suggestions according to the specific environment of the user;

[0045] The intelligent repair suggestion module provides a pre-verification function for repair solutions in the experimental environment to ensure the effectiveness and compatibility of the repair measures; the repair solutions include detailed implementation step descriptions, so that even non-professionals can complete according to the guidance; for high-risk vulnerabilities, an emergency response plan is proposed to guide users to solve problems in the shortest time and reduce losses.

[0046] In the present invention, the interactive user interface is provided for administrators to monitor the platform operation status, view vulnerability assessment reports, manage repair suggestions, and implement security policies, and supports multi-language switching with a good user experience design;

[0047] The interactive user interface realizes data visualization, displays complex vulnerability assessment results in the form of charts, which is convenient for users to understand and make decisions; supports access from the mobile device side to view alarm information and perform key operations; embeds online learning resources and forum links to promote technical communication and support among users.

[0048] In the present invention, the adaptive vulnerability protection mechanism can automatically learn and adjust the protection strategy when the platform detects a vulnerability to cope with unknown threats and achieve proactive security protection;

[0049] The adaptive vulnerability protection mechanism has the ability of self-evolution, and can identify new attack methods without clear instances; by real-time monitoring of network traffic and system behavior, it can quickly respond to abnormal activities and automatically block suspicious IP addresses and ports; it is well integrated with existing security devices and can be seamlessly connected to the existing security framework of enterprises.

[0050] In the present invention, the continuous learning and updating mechanism regularly updates the vulnerability information database and repair measure database inside the platform through automatic synchronization with external security information sources to ensure the timeliness and accuracy of the data used;

[0051] The continuous learning and updating mechanism supports incremental updates, avoiding resource waste caused by full-scale data refreshing; designs an intelligent screening algorithm to prioritize the download and processing of more urgent and relevant security information; and establishes cooperative relationships with many leading security research institutions at home and abroad to obtain cutting-edge research results.

[0052] The multi-level authentication system described in the present invention protects the security of the platform and data, implementing a multi-level authentication scheme from login authentication to operation authorization, including but not limited to two-factor authentication and biometric authentication, effectively preventing illegal access and operations;

[0053] The multi-level authentication system introduces behavior-based authentication technology to identify potential abnormal behaviors by analyzing users' operation habits; supports role-based permission management and the principle of least privilege to ensure that each account can only access necessary system resources; provides a powerful log auditing function to track and record all operation processes for easy post-event responsibility tracing.

[0054] The automated security testing toolset described in the present invention integrates automated tools for simulating attack and penetration testing scenarios, helping users discover and resolve vulnerabilities before they are exploited, and at the same time supporting customized testing scenarios to meet different user needs;

[0055] The extensible plug-in architecture described in the present invention designs an open plug-in interface, allowing third-party developers to develop and integrate new functional modules according to specific requirements, including specific types of vulnerability scanners and customized report generators, enhancing the functionality and flexibility of the platform;

[0056] The extensible plug-in architecture defines clear API interface specifications to ensure the compatibility of third-party plug-ins with the core functions of the platform; establishes a plug-in market to collect and review third-party plug-ins for users to download and use; performs strict compatibility checks and security tests during the plug-in installation process to ensure the overall stability and security of the platform.

[0057] Example 1:

[0058] This example describes a specific application scenario and implementation method of an AI-based cloud security vulnerability management platform, which includes the following main modules:

[0059] (1) Centralized management module: responsible for collecting, processing, and managing vulnerability information from multiple resources in the cloud computing environment.

[0060] (2) Distributed event collector network: composed of lightweight agents located at different nodes in the cloud environment, which monitors and collects security event data on the nodes in real time.

[0061] (3) Automatic vulnerability assessment system: uses machine learning algorithms to score and classify vulnerability information.

[0062] (4) Intelligent Repair Suggestion Module: Provide detailed repair solutions and give personalized suggestions according to the user environment.

[0063] (5) Interactive User Interface: Provide functions for administrators to monitor the running status of the platform, view evaluation reports, manage repair suggestions, and implement security policies.

[0064] (6) Adaptive Vulnerability Protection Mechanism: Automatically adjust protection policies according to detected vulnerabilities.

[0065] (7) Continuous Learning and Update Mechanism: Regularly update the internal vulnerability information database and repair measure database of the platform through automatic synchronization with external security information sources.

[0066] (8) Multi-level Authentication System: Protect the security of the platform and data by implementing a multi-level authentication scheme.

[0067] (9) Automated Security Testing Toolset: Integrate automated tools for simulating attacks and penetration testing.

[0068] (10) Extensible Plugin Architecture: Allow third-party developers to develop and integrate new functional modules.

[0069] Specific implementation steps:

[0070] (I) Data collection and processing

[0071] (1) Distributed Event Collector Network: Deploy lightweight agents on each node of the cloud environment. These agents can monitor and collect various types of security event data in real time, including log files, system calls, network traffic, etc.

[0072] (2) Encrypted transmission: All collected data is transmitted to the centralized management module through an encrypted channel to ensure data integrity and security.

[0073] (3) Fine-grained settings: Administrators can set different log collection levels and adjust the data collection scope according to actual needs. At the same time, a data desensitization function is equipped to prevent the leakage of sensitive information.

[0074] (4) Detection of abnormal activities: The event collector can intelligently judge abnormal activities, such as abnormal login attempts and illegal access requests, and immediately alarm the centralized management module.

[0075] (II) Automatic vulnerability assessment

[0076] (1) Machine learning algorithm: The centralized management module uses machine learning algorithms to analyze the collected data and automatically identify potential security vulnerabilities.

[0077] (2) Scoring and Classification: Each identified security vulnerability is scored and classified according to its severity, scope of impact, and potential risks, providing priority guidance for subsequent vulnerability remediation.

[0078] (3) Natural Language Processing: The automated vulnerability assessment system uses natural language processing technology to read and parse security bulletins, automatically extracting vulnerability information and incorporating it into the assessment.

[0079] (4) User-Defined Criteria: Supports users in defining their own assessment criteria to adapt to the security requirements and strategies of different enterprises.

[0080] (5) Duplicate Vulnerability Detection: The system integrates a function to detect duplicate vulnerabilities, avoiding the reporting of the same vulnerability repeatedly and improving work efficiency.

[0081] (III) Intelligent Remediation Suggestions

[0082] (1) Case Library: Based on the security vulnerability cases stored in the database and their corresponding remediation measures, combined with the latest security research results, provides detailed remediation solutions for different types of vulnerabilities.

[0083] (2) Personalized Suggestions: The remediation solution can give personalized remediation suggestions according to the user's specific environment (such as operating system version, application configuration, etc.).

[0084] (3) Pre-Verification Function: Pre-verifies the remediation solution in a test environment to ensure its effectiveness and compatibility.

[0085] (4) Detailed Implementation Steps: The remediation solution includes detailed descriptions of the implementation steps, enabling even non-professionals to complete it according to the guidance.

[0086] (5) Emergency Response Plan: Proposes an emergency response plan for high-risk vulnerabilities, guiding users to solve problems within the shortest time and reducing losses.

[0087] (IV) Interactive User Interface

[0088] (1) Data Visualization: Displays complex vulnerability assessment results in the form of charts, facilitating user understanding and decision-making.

[0089] (2) Multilingual Support: Supports multilingual switching to meet the needs of users in different countries and regions.

[0090] (3) Mobile Device Access: Supports access from mobile devices, allowing users to view alert information and perform critical operations on mobile phones or tablets.

[0091] (4) Online Learning and Communication: Embeds online learning resources and forum links to promote technical communication and support among users.

[0092] (5) Adaptive Vulnerability Protection

[0093] (1) Self-Evolution: The platform has the ability of self-evolution and can identify new attack methods without clear instances.

[0094] (2) Real-Time Monitoring: By monitoring network traffic and system behavior in real time, it can quickly respond to abnormal activities and automatically block suspicious IP addresses and ports.

[0095] (3) Integration with Existing Devices: It can be well integrated with existing security devices and seamlessly access the enterprise's existing security framework.

[0096] (6) Continuous Learning and Update

[0097] (1) Incremental Update: It supports incremental update to avoid resource waste caused by full data refresh.

[0098] (2) Intelligent Screening: Design intelligent screening algorithms to preferentially download and process more urgent and relevant security information.

[0099] (3) Partner Institutions: Establish cooperation relationships with many leading domestic and foreign security research institutions to obtain cutting-edge research results.

[0100] (7) Multi-Level Authentication

[0101] (1) Two-Factor Authentication: Support two-factor authentication, such as SMS verification codes, hardware tokens, etc.

[0102] (2) Biometric Authentication: Support biometric authentication, such as fingerprints, facial recognition, etc.

[0103] (3) Behavior-Based Authentication: Introduce behavior-based authentication technology to identify potential abnormal behaviors by analyzing users' operation habits.

[0104] (4) Permission Management: Support role-based permission management and the principle of least privilege to ensure that each account can only access necessary system resources.

[0105] (5) Log Audit: Provide a powerful log audit function to track and record all operation processes for easy post-event responsibility tracing.

[0106] (8) Automated Security Testing

[0107] (1) Simulated Attack: Integrate automated tools for simulated attacks and penetration testing to help users discover and solve vulnerabilities before they are exploited.

[0108] (2) Customizable Test Scenarios: Support users to customize test scenarios to meet different test requirements.

[0109] (9) Scalable Plug-In Architecture

[0110] (1) API Interface: Design a clear API interface specification to ensure the compatibility between third - party plugins and the core functions of the platform.

[0111] (2) Plugin Market: Establish a plugin market to collect and review third - party plugins for users to download and use.

[0112] (3) Compatibility and Security Checks: Perform strict compatibility checks and security tests during the plugin installation process to ensure the overall stability and security of the platform.

[0113] Example 2:

[0114] This example describes the actual application of the platform in a medium - sized enterprise and how it helps the enterprise improve the security of the cloud environment.

[0115] Application Scenario: A medium - sized enterprise has an e - commerce platform based on public cloud, which involves multiple servers, storage devices, network services, and security devices. Due to the continuous development of the business, the management and repair of security vulnerabilities have become a huge challenge.

[0116] Solution Steps:

[0117] (1) Deploy a Distributed Event Collector Network

[0118] The enterprise deploys lightweight agents on each cloud node. These agents can monitor and collect various security event data in real - time, including log files, system calls, network traffic, etc. The data is transmitted to the centralized management module through an encrypted channel to ensure the security of data transmission.

[0119] (2) Data Collection and Processing

[0120] The centralized management module collects and processes data from each node. Administrators can adjust the log collection level through the user interface to meet different security requirements. The event collector is equipped with a data masking function to ensure that sensitive information in the log files will not be leaked. The agent can intelligently judge abnormal activities, such as abnormal login attempts and illegal access requests, and immediately alarm the centralized management module.

[0121] (3) Automatic Vulnerability Assessment

[0122] The centralized management module uses machine - learning algorithms to analyze the collected data, automatically identify potential security vulnerabilities. Score and classify each identified security vulnerability to provide priority guidance. The system parses security bulletins, automatically extracts vulnerability information and incorporates it into the assessment to avoid missing important information. Enterprise administrators can customize the assessment criteria and evaluate vulnerabilities according to the company's security policies.

[0123] (4) Intelligent repair suggestions

[0124] Based on the security vulnerability cases and repair measures in the database, the platform provides detailed repair solutions for each identified vulnerability. The repair solutions include code-level modification suggestions, configuration adjustment guides, patch installation steps, etc. Enterprise administrators can choose to pre-verify the repair solutions in the experimental environment to ensure their effectiveness and compatibility. For high-risk vulnerabilities, the platform provides emergency response plans to guide administrators to solve problems in the shortest time.

[0125] (5) Interactive user interface

[0126] Enterprise administrators monitor the running status of the platform, view vulnerability assessment reports, manage repair suggestions, and implement security policies through the interactive user interface. The user interface supports data visualization, presenting complex assessment results in the form of charts for easy understanding and decision-making by administrators. It supports multi-language switching to meet the needs of employees in different countries and regions. The mobile device access function enables administrators to view warning information and perform critical operations on mobile phones or tablets at any time.

[0127] (6) Adaptive vulnerability protection

[0128] After detecting vulnerabilities, the platform can automatically learn and adjust protection strategies to deal with unknown threats. It monitors network traffic and system behaviors in real time, quickly responds to abnormal activities, and automatically blocks suspicious IP addresses and ports. It integrates well with the enterprise's existing security devices (such as firewalls, IDS, etc.) and seamlessly accesses the existing security framework.

[0129] (7) Continuous learning and updating

[0130] The platform regularly updates the internal vulnerability information database and repair measure database by automatically synchronizing with external security information sources. It supports incremental updates to reduce resource waste. Through intelligent screening algorithms, it preferentially downloads and processes more urgent and relevant security information. It has established cooperative relationships with many leading domestic and foreign security research institutions to obtain cutting-edge research results.

[0131] (8) Multi-level authentication

[0132] The platform implements a multi-level authentication scheme from login authentication to operation authorization, including two-factor authentication and biometric authentication. It introduces behavior-based authentication technology to identify potential abnormal behaviors by analyzing the operation habits of administrators. It supports role-based permission management and the principle of least privilege to ensure that each account can only access necessary system resources. It provides a powerful log audit function to track and record all operation processes for easy post-event responsibility tracing.

[0133] (9) Automated security testing

[0134] The platform integrates automated tools for simulating attacks and penetration testing, helping enterprises discover and resolve potential security issues before vulnerabilities are exploited. It supports custom test scenarios to adapt to different business requirements.

[0135] (X) Scalable plugin architecture

[0136] The platform is designed with an open plugin interface, allowing third-party developers to develop new functional modules according to specific requirements. Enterprises can download and install vulnerability scanners and customized report generators developed by third parties through the plugin market, enhancing the functionality and flexibility of the platform. Strict compatibility checks and security tests are conducted during the plugin installation process to ensure the reliability of the plugins and the security of the platform.

[0137] By deploying this AI-based cloud security vulnerability management platform, enterprises can more efficiently manage and fix security vulnerabilities in the cloud environment. The platform's adaptive vulnerability protection mechanism improves the security and robustness of the system, and the multi-level authentication system effectively prevents illegal access and internal threats. Administrators can easily monitor the platform status, view assessment reports, and implement security policies through the interactive user interface, greatly enhancing the convenience of management and the user experience. In addition, the platform's continuous learning and update mechanism ensures the timeliness and accuracy of the data used, and the scalable plugin architecture enables enterprises to customize functional modules according to their own needs, further enhancing the flexibility and functionality of the platform.

[0138] In summary, the AI-based cloud security vulnerability management platform provided by the present invention not only solves various problems in the existing solutions, but also provides enterprises with a more comprehensive, intelligent, and flexible solution, effectively enhancing the security of the cloud environment and the user experience.

[0139] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An AI-based cloud security vulnerability management platform, characterized in that, It includes a centralized management module, a distributed event collector network, an automatic vulnerability assessment system, an intelligent repair suggestion module, an interactive user interface, an adaptive vulnerability protection mechanism, a continuous learning and updating mechanism, a multi-level authentication system, an automated security testing toolset, and an extensible plugin architecture. The centralized management module is used to collect, process, and manage vulnerability information from multiple resources in the cloud computing environment, including but not limited to servers, storage devices, network services, and security devices. It analyzes a large amount of data through AI algorithms to automatically identify potential security vulnerabilities, improve the security of the cloud environment, and reduce human errors. The distributed event collector network consists of lightweight agents located at different nodes in the cloud environment. It is responsible for real-time monitoring and collecting security event data on the nodes, including but not limited to log files, system calls, and network traffic, and transmits them to the centralized management module through an encrypted channel to ensure the integrity and security of the data. The automatic vulnerability assessment system uses machine learning algorithms to score and classify each identified security vulnerability according to the severity, impact range, and possible risks of the vulnerability information, providing priority guidance for subsequent vulnerability repairs. The intelligent repair suggestion module, based on the security vulnerability cases stored in the database and their corresponding repair measures, combined with the latest security research results, provides detailed repair solutions for different types of vulnerabilities, including but not limited to code-level modification suggestions, configuration adjustment guides, and patch installation steps, and can give personalized repair suggestions according to the specific environment of the user. The interactive user interface is provided for administrators to monitor the platform operation status, view vulnerability assessment reports, manage repair suggestions, and implement security policies, and supports multi-language switching with a good user experience design. The adaptive vulnerability protection mechanism can automatically learn and adjust the protection strategy when the platform detects a vulnerability to cope with unknown threats and achieve proactive security protection. The continuous learning and updating mechanism regularly updates the internal vulnerability information database and repair measure database of the platform through automatic synchronization with external security information sources to ensure the timeliness and accuracy of the data used. The multi-level authentication system protects the security of the platform and data by implementing a multi-level authentication scheme from login authentication to operation authorization, including but not limited to two-factor authentication and biometric authentication, effectively preventing illegal access and operations. The automated security testing toolset integrates automated tools for simulating attack and penetration testing scenarios, helping users discover and resolve vulnerabilities before they are exploited, and supporting custom test scenarios to meet the needs of different users. The extensible plugin architecture designs open plugin interfaces, allowing third-party developers to develop and integrate new functional modules according to specific requirements, including specific types of vulnerability scanners and customized report generators, enhancing the functionality and flexibility of the platform.

2. The cloud security vulnerability management platform based on AI according to claim 1, characterized in that The AI algorithm includes learning from historical vulnerability handling records to predict possible future security threat patterns; continuously tracking the subsequent impacts of fixed vulnerabilities to evaluate the repair effects; and understanding complex network environments and application structures through deep learning techniques to improve the vulnerability identification rate and accuracy.

3. The AI-based cloud security vulnerability management platform according to claim 1, wherein The event collector network supports fine-grained log collection level settings, allowing the data collection scope to be adjusted according to different requirements; it is equipped with a data desensitization processing function to ensure that sensitive information is not leaked during data transmission and storage; and it can intelligently detect abnormal activities, including abnormal login attempts and illegal access requests, and immediately alarm the centralized management module.

4. A cloud security vulnerability management platform based on AI according to claim 1, characterized in that, The automatic vulnerability assessment system uses natural language processing technology to read and parse security bulletins, automatically extract vulnerability information and incorporate it into the assessment; it supports user-defined assessment criteria to adapt to the security needs and policies of different enterprises; and it integrates a vulnerability duplicate checking function to avoid repeatedly reporting the same vulnerability and improve work efficiency.

5. An AI-based cloud security vulnerability management platform according to claim 1, characterized in that, The intelligent repair suggestion module provides a pre-verification function for repair solutions in an experimental environment to ensure the effectiveness and compatibility of repair measures; the repair solutions include detailed implementation step descriptions, enabling even non-professionals to complete them according to the guidance; and it proposes an emergency response plan for high-risk vulnerabilities to guide users to solve problems in the shortest time and reduce losses.

6. The cloud security vulnerability management platform based on AI according to claim 1, characterized in that, The interactive user interface realizes data visualization, presenting complex vulnerability assessment results in the form of charts for easy user understanding and decision-making; it supports access from mobile device terminals to view alarm information and perform key operations; and it embeds online learning resources and forum links to promote technical communication and support among users.

7. An AI-based cloud security vulnerability management platform according to claim 1, characterized in that, The adaptive vulnerability protection mechanism has the ability to self-evolve and can identify new attack methods without clear examples; by real-time monitoring network traffic and system behaviors, it can quickly respond to abnormal activities and automatically block suspicious IP addresses and ports. It is well integrated with existing security devices and can be seamlessly connected to the enterprise's existing security framework.

8. An AI-based cloud security vulnerability management platform according to claim 1, characterized in that, The continuous learning and updating mechanism supports incremental updates to avoid resource waste caused by full-scale data refreshing; it designs an intelligent screening algorithm to preferentially download and process more urgent and relevant security information. It has established cooperative relationships with many leading domestic and foreign security research institutions to obtain cutting-edge research results.

9. A cloud security vulnerability management platform based on AI according to claim 1, characterized in that, The multi-level authentication system introduces behavior-based authentication technology to identify potential abnormal behaviors by analyzing users' operation habits. It supports role-based permission management and the principle of least privilege to ensure that each account can only access necessary system resources. It provides a powerful log auditing function to track and record all operation processes for easy post-event responsibility tracing.

10. A cloud security vulnerability management platform based on AI according to claim 1, characterized in that, The extensible plugin architecture defines clear API interface specifications to ensure the compatibility of third-party plugins with the core functions of the platform. It establishes a plugin market to collect and review third-party plugins for users to download and use. During the plugin installation process, strict compatibility checks and security tests are performed to ensure the overall stability and security of the platform.

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