Intelligent compliance risk assessment and monitoring system

Abstract

The invention relates to the technical field of risk assessment, and discloses an intelligent compliance risk assessment and monitoring system, which comprises a threat detection module, a management module, a data protection module and an automatic risk assessment module, the management module is used for identifying potential network attacks, malicious behaviors or abnormal traffic and providing automatic early warning, and the management module is used for docking global and local data protection and privacy laws and regulations, automatically analyzing compliance states of enterprises, generating compliance reports and providing correction suggestions; the data protection module is responsible for encrypting sensitive data, managing data access authority and tracking data flow, and the automatic risk assessment module performs automatic assessment on risk conditions of enterprises by utilizing artificial intelligence and machine learning technologies, predicts future compliance risks and generates a coping scheme. The method has the advantage of monitoring and analyzing potential threats and compliance risks.

Description

Technical Field

[0001] The present invention relates to the technical field of risk assessment, and in particular to an intelligent compliance risk assessment and monitoring system. Background Art

[0002] With the acceleration of digital transformation, the increasing complexity of the network environment is not only reflected in the diversity of technology and the extensiveness of system interconnection, but also in the rapid development of various emerging technologies, such as the widespread application of cloud computing, the Internet of Things, artificial intelligence, and 5G networks. While these technologies bring more opportunities and innovation to enterprises, they also greatly increase the complexity of information systems and potential security vulnerabilities. The forms of threats in cyberspace are constantly evolving, from traditional hacker attacks and malware infections to more targeted and concealed cyber espionage, ransomware, and advanced persistent threats (APT). Enterprises not only have to face a variety of cyber attack methods, but also have to deal with the severe challenges of data leakage and privacy protection.

[0003] In this context, companies must not only protect internal systems and data, but also ensure network security with external partners and supply chains, which increases management difficulty. Globally, regulations such as GDPR, CCPA and HIPAA are becoming increasingly stringent, and countries have also introduced relevant laws requiring companies to take higher protection measures for sensitive personal, financial, health and other information. Violation of these regulations will not only result in huge fines, but may also lead to legal proceedings and reputational damage. Compliance pressure makes network security a key issue in the overall operational strategy of enterprises, not just a technical issue.

[0004] To meet these challenges, enterprises need more intelligent technical solutions. Therefore, it is necessary to design an intelligent compliance risk assessment and monitoring system to monitor and analyze potential threats and compliance risks. Summary of the invention

[0005] 1. Technical issues to be solved

[0006] In view of the deficiencies in the prior art, the present invention provides an intelligent compliance risk assessment and monitoring system, which has the advantages of monitoring and analyzing potential threats and compliance risks, and solves the problems in the above-mentioned background technology.

[0007] (II) Technical solution

[0008] In order to achieve the above-mentioned purpose of monitoring and analyzing potential threats and compliance risks, the present invention provides the following technical solutions: an intelligent compliance risk assessment and monitoring system, including a threat detection module, a management module, a data protection module, and an automated risk assessment module. The threat detection module is used to monitor network activities inside and outside the system in real time, identify potential network attacks, malicious behaviors or abnormal traffic, and provide automatic warnings. The management module is used to connect to global and local data protection and privacy regulations, automatically analyze the compliance status of the enterprise, generate compliance reports and provide correction suggestions. The data protection module is responsible for encrypting sensitive data, managing data access rights and tracking data flow. The automated risk assessment module uses artificial intelligence and machine learning technologies to automatically assess the risk status of the enterprise, predict future compliance risks, and generate response plans.

[0009] Preferably, the threat detection module includes an intrusion detection module, an abnormal behavior detection module and a threat intelligence integration module. The intrusion detection module is responsible for real-time monitoring and system activities and identifying intrusion behaviors. The abnormal behavior detection module analyzes the normal behavior modules of users and systems to identify suspicious activities that deviate from these patterns. The threat intelligence integration module receives external threat information and matches it with the data inside the system.

[0010] The detection process includes collecting network traffic and host log data, denoising and standardizing the data, and extracting traffic features. The data is analyzed in real time according to the SVM classification algorithm. The traffic is classified according to the detection results, abnormal or malicious behavior is identified, the alarm mechanism is triggered, and response measures are automatically taken. SVM distinguishes normal traffic from abnormal traffic by finding a hyperplane. The decision function of SVM is:

[0011] f(x)=w T x+b

[0012] Among them, x is the input feature vector, w is the weight vector obtained through training, b is the bias term, and f(x) is the classification function that determines which category the sample belongs to. When f(x)>0, it is classified as normal traffic, and when f(x)<0, it is classified as abnormal traffic.

[0013] Preferably, the management module includes a policy management module and a rights management module. The policy management module is responsible for formulating, maintaining and managing security and compliance policies, allowing administrators to define security standards, access control, data processing procedures and compliance requirements within the system. The rights management system is used to control the access rights of users and systems to resources, and define different user roles and permission levels by implementing role-based access control or attribute-based access control.

[0014] The entire process of data processing includes the preprocessing stage after the data is collected from the system or external sources. Before the data is stored in the system or transmitted to the external system, it needs to be encrypted. The encrypted data is stored in a secure database and the encrypted data is transmitted on the network through a secure communication protocol. When a user requests access to the data, the system checks the user's permissions and decrypts and presents the data according to the access policy. By monitoring data flow, storage and user behavior, the system identifies and blocks abnormal data access or leakage attempts. Data encryption methods include symmetric encryption and asymmetric encryption. Symmetric encryption is used for data in storage or large-scale file encryption, and asymmetric encryption is used for sensitive information encryption in data transmission. The data loss prevention process includes real-time monitoring of data flow and user behavior, analyzing potential leakage risks, automatically triggering blocking strategies, and regularly backing up encrypted data.

[0015] Preferably, the data protection module includes a data encryption module, a data loss prevention module and a data backup module. The data encryption module is used to encrypt sensitive data and use symmetric encryption and asymmetric encryption algorithms to protect data. The data loss prevention module is used to prevent sensitive data from being leaked or out of the system without authorization, and identifies and prevents potential leakage incidents by monitoring the flow of data in the network, storage within the system and user access behavior. The data backup module is responsible for regularly backing up data, and can quickly resume operations in the event of data loss to maintain data integrity and availability.

[0016] Preferably, the automated risk assessment module includes a risk classification module and an automated decision-making module, wherein the risk classification module is used to perform quantitative and qualitative assessments on identified risks, analyze their potential impacts and probability of occurrence, classify and sort risks according to the severity of the risks and financial losses, and reasonably allocate resources;

[0017] Enterprise risk assessment can predict future compliance risks based on machine learning classification algorithms. The core formula is as follows:

[0018] P(R|X)=f(X,W)

[0019] Where P(R|X) is the predicted probability value of the enterprise risk R given the feature set X, X is the feature vector, W is the weight parameter of the model, and f(X,W) is a machine learning model that generates a risk probability value based on feature X;

[0020] The risk assessment classification process includes collecting various data sources inside and outside the enterprise, including financial reports, market data, historical compliance records, and industry policies, extracting and converting important features, converting time series data into a feature matrix, and using cross-validation methods to evaluate the generalization ability of the model. Based on the prediction results of the model, risks are divided into multiple levels, each level corresponding to different response plans. Low risks continue to maintain current compliance behaviors, medium risks strengthen internal audits and supervision, and high risks immediately take corrective measures, strengthen compliance training, and adjust business processes.

[0021] A risk assessment and monitoring method of an intelligent compliance risk assessment and monitoring system comprises the following steps:

[0022] S1: Monitor network and system activities in real time and automatically identify potential security threats and compliance risks;

[0023] S2: Conduct qualitative and quantitative analysis on identified threats and risks, assess their impact, and determine priorities;

[0024] S3: Prepare and implement compliance strategies based on regulations and industry standards;

[0025] S4: Automatically implement defense and mitigation measures to fix vulnerabilities based on risk assessment results;

[0026] S5: Continuously monitor system activity, dynamically adjust policies, and update the system regularly.

[0027] (III) Beneficial effects

[0028] Compared with the prior art, the present invention provides an intelligent compliance risk assessment and monitoring system, which has the following beneficial effects:

[0029] The present invention automatically identifies potential security threats and compliance risks by monitoring network and system activities in real time; conducts qualitative and quantitative analysis on identified threats and risks, evaluates the impacts, and determines priorities; prepares and executes compliance strategies based on regulations and industry standards; automatically implements defense and mitigation measures and repairs vulnerabilities based on risk assessment results; continuously monitors system activities, dynamically adjusts strategies, and regularly updates the system to monitor and analyze potential threats and compliance risks, which helps enterprises improve their adaptability and competitiveness to global compliance frameworks while responding to evolving network threats. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is a schematic diagram of the structure of the present invention. DETAILED DESCRIPTION

[0031] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0032] Example 1

[0033] The present invention provides a technical solution: an intelligent compliance risk assessment and monitoring system, including a threat detection module, a management module, a data protection module, and an automated risk assessment module. The threat detection module is used to monitor network activities inside and outside the system in real time, identify potential network attacks, malicious behaviors or abnormal traffic, and provide automatic early warnings. The management module is used to connect to global and local data protection and privacy regulations, automatically analyze the compliance status of the enterprise, generate compliance reports and provide correction suggestions. The data protection module is responsible for encrypting sensitive data, managing data access rights and tracking data flow. The automated risk assessment module uses artificial intelligence and machine learning technologies to automatically assess the risk status of the enterprise, predict future compliance risks, and generate response plans.

[0034] The threat detection module includes an intrusion detection module, an abnormal behavior detection module and a threat intelligence integration module. The intrusion detection module is responsible for real-time monitoring and system activities and identifying intrusion behaviors. The abnormal behavior detection module analyzes the normal behavior modules of users and systems and identifies suspicious activities that deviate from these patterns. The threat intelligence integration module receives external threat information and matches it with the data inside the system.

[0035] The management module includes a policy management module and a permission management module. The policy management module is responsible for formulating, maintaining and managing security and compliance policies, allowing administrators to define security standards, access control, data processing processes and compliance requirements within the system. The permission management system is used to control user and system access rights to resources, and define different user roles and permission levels by implementing role-based access control or attribute-based access control.

[0036] The data protection module includes a data encryption module, a data loss prevention module and a data backup module. The data encryption module is used to encrypt sensitive data and use symmetric encryption and asymmetric encryption algorithms to protect data. The data loss prevention module is used to prevent sensitive data from being leaked or out of the system without authorization. It identifies and prevents potential leakage incidents by monitoring the flow of data in the network, storage within the system and user access behavior. The data backup module is responsible for regularly backing up data, and can quickly resume operations in the event of data loss to maintain data integrity and availability.

[0037] The automated risk assessment module includes a risk classification module and an automated decision-making module. The risk classification module is used to perform quantitative and qualitative assessments on identified risks, analyze their potential impacts and probability of occurrence, classify and sort risks according to the severity of the risks and financial losses, and reasonably allocate resources.

[0038] A risk assessment and monitoring method of an intelligent compliance risk assessment and monitoring system comprises the following steps:

[0039] S1: Monitor network and system activities in real time and automatically identify potential security threats and compliance risks;

[0040] The process of real-time monitoring of network and system activities specifically includes continuous tracking and analysis of various activities such as network traffic, user behavior, system logs, file operations, device connections, etc. This monitoring automatically captures abnormal patterns or abnormal operations, such as unauthorized access, abnormal data transmission, high frequency of login failures, malware activities, etc., by using intrusion detection systems, intrusion prevention systems and behavioral analysis tools. The integration of machine learning algorithms and threat intelligence platforms can identify unknown threats and new attack methods. The system will generate alarms or take preventive measures in real time based on preset rules or through self-learning adjustments to ensure that potential security threats and compliance risks can be discovered and effectively defended as early as possible.

[0041] S2: Conduct qualitative and quantitative analysis on identified threats and risks, assess their impact, and determine priorities;

[0042] Through qualitative analysis, we classify risks, determine the source, type and nature of threats, such as external attacks, internal operational errors or technical failures, and analyze the possible legal, financial and operational impacts. We also conduct quantitative analysis to calculate the probability of risk occurrence and assess the scale of possible losses, such as potential fines for data breaches, financial losses from business interruptions, etc. We use risk scoring models to prioritize risks based on the severity of the threat, the likelihood of occurrence and the impact on the business, to ensure that enterprises prioritize the most serious and urgent risks, so as to reasonably allocate resources and develop targeted defense and mitigation measures.

[0043] S3: Prepare and implement compliance strategies based on regulations and industry standards;

[0044] It is necessary to understand and analyze relevant regulations in detail, such as GDPR, CCPA, HIPAA, and industry standards such as ISO 27001 and NIST, clarify their specific requirements for data protection, privacy management, risk control, etc., and formulate targeted compliance strategies, including establishing data classification and protection measures, setting access control and permission management rules, implementing data encryption and backup strategies, and defining data processing and audit processes. When implementing these strategies, they need to be integrated into the daily operations of the enterprise, including training employees to ensure that they understand and comply with these policies, regularly reviewing and updating compliance measures to adapt to regulatory changes, and monitoring the implementation of strategies through automated tools. Conduct regular compliance reviews and audits to ensure that all operations meet established standards and promptly correct any deviations from compliance requirements.

[0045] S4: Automatically implement defense and mitigation measures to fix vulnerabilities based on risk assessment results;

[0046] The system will automatically generate and deploy targeted defense strategies based on the risk priorities determined in the assessment, such as configuring firewall rules, updating intrusion prevention system settings, applying patches and security updates to fix known vulnerabilities. The system will perform automated vulnerability scanning to identify and fix security vulnerabilities in systems and applications, and use automated tools to implement data encryption, permission adjustment, and access control measures to prevent unauthorized access or data leakage. The system will also trigger emergency response procedures, such as isolating affected systems, blocking malicious traffic, and generating detailed incident reports and audit logs for subsequent analysis and optimization. All of these automated measures will be adjusted under real-time monitoring to ensure their effectiveness, and defense strategies will be dynamically updated based on emerging risks.

[0047] S5: Continuously monitor system activity, dynamically adjust policies, and update the system regularly.

[0048] Continuous monitoring of system activities involves real-time tracking of network traffic, user behavior, system logs, and application performance in order to promptly detect abnormal activities or potential security threats. The system uses advanced monitoring tools and algorithms to automatically analyze data, generate alerts, and identify trends or pattern changes to ensure timely response to emerging threats and changes. Based on these monitoring data, dynamic adjustment strategies include modifying firewall rules, updating the configuration of intrusion detection systems, adjusting access control policies, etc. to respond to new risks or vulnerabilities. Regularly updating the system includes applying the latest security patches, upgrading software versions, optimizing system configurations, and conducting regular security audits and compliance checks to maintain the overall security and compliance of the system. These steps ensure that the system can adapt to the ever-changing threat environment and regulatory requirements, and continue to maintain a high level of security protection.

[0049] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0050] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent compliance risk assessment and monitoring system, characterized in that: It includes a threat detection module, a management module, a data protection module, and an automated risk assessment module. The threat detection module is used to monitor network activities inside and outside the system in real time, identify potential network attacks, malicious behaviors or abnormal traffic, and provide automatic warnings. The management module is used to connect to global and local data protection and privacy regulations, automatically analyze the company's compliance status, generate compliance reports and provide correction suggestions. The data protection module is responsible for encrypting sensitive data, managing data access rights and tracking data flows. The automated risk assessment module uses artificial intelligence and machine learning technologies to automatically assess the company's risk status, predict future compliance risks, and generate response plans.

2. According to claim 1, an intelligent compliance risk assessment and monitoring system is characterized in that: The threat detection module includes an intrusion detection module, an abnormal behavior detection module and a threat intelligence integration module. The intrusion detection module is responsible for real-time monitoring and system activities and identifying intrusion behaviors. The abnormal behavior detection module analyzes the normal behavior modules of users and systems to identify suspicious activities that deviate from these patterns. The threat intelligence integration module receives external threat information and matches it with the data inside the system. The detection process includes collecting network traffic and host log data, denoising and standardizing the data, and extracting traffic features. The data is analyzed in real time according to the SVM classification algorithm. The traffic is classified according to the detection results, abnormal or malicious behavior is identified, the alarm mechanism is triggered, and response measures are automatically taken. SVM distinguishes normal traffic from abnormal traffic by finding a hyperplane. The decision function of SVM is: f(x)=w T x+b Among them, x is the input feature vector, w is the weight vector obtained through training, b is the bias term, and f(x) is the classification function that determines which category the sample belongs to. When f(x)>0, it is classified as normal traffic, and when f(x)<0, it is classified as abnormal traffic.

3. The intelligent compliance risk assessment and monitoring system according to claim 1 is characterized in that: The management module includes a policy management module and a permission management module. The policy management module is responsible for formulating, maintaining and managing security and compliance policies, allowing administrators to define security standards, access control, data processing processes and compliance requirements within the system. The permission management system is used to control the access rights of users and systems to resources, and define different user roles and permission levels by implementing role-based access control or attribute-based access control; The entire process of data processing includes the preprocessing stage after the data is collected from the system or external sources. Before the data is stored in the system or transmitted to the external system, it needs to be encrypted. The encrypted data is stored in a secure database and transmitted on the network through a secure communication protocol. When a user requests to access the data, the system checks the user's permissions and decrypts and presents the data according to the access policy. By monitoring data flow, storage and user behavior, the system identifies and prevents abnormal data access or leakage attempts. Data encryption methods include symmetric encryption and asymmetric encryption. Symmetric encryption is used for data in storage or large-scale file encryption, and asymmetric encryption is used for sensitive information encryption in data transmission. The data loss prevention process includes real-time monitoring of data flow and user behavior, analysis of potential leakage risks, automatic triggering of blocking strategies, and regular backup of encrypted data.

4. The intelligent compliance risk assessment and monitoring system according to claim 1 is characterized in that: The data protection module includes a data encryption module, a data loss prevention module and a data backup module. The data encryption module is used to encrypt sensitive data and protect data using symmetric encryption and asymmetric encryption algorithms. The data loss prevention module is used to prevent sensitive data from being leaked or out of the system without authorization, and identifies and prevents potential leakage incidents by monitoring the flow of data in the network, storage within the system and user access behavior. The data backup module is responsible for regularly backing up data, and can quickly resume operations in the event of data loss to maintain data integrity and availability.

5. The intelligent compliance risk assessment and monitoring system according to claim 1 is characterized in that: The automated risk assessment module includes a risk classification module and an automated decision-making module. The risk classification module is used to conduct quantitative and qualitative assessments of identified risks, analyze their potential impacts and probability of occurrence, classify and rank risks according to the severity of the risks and financial losses, and reasonably allocate resources; Enterprise risk assessment can predict future compliance risks based on machine learning classification algorithms. The core formula is as follows: P(R|X)=f(X,W) Where P(R|X) is the predicted probability value of the enterprise risk R given the feature set X, X is the feature vector, W is the weight parameter of the model, and f(X,W) is a machine learning model that generates a risk probability value based on feature X; The risk assessment classification process includes collecting various data sources inside and outside the enterprise, including financial reports, market data, historical compliance records, and industry policies, extracting and converting important features, converting time series data into a feature matrix, and using cross-validation methods to evaluate the generalization ability of the model. Based on the prediction results of the model, risks are divided into multiple levels, each level corresponding to different response plans. Low risks continue to maintain current compliance behaviors, medium risks strengthen internal audits and supervision, and high risks immediately take corrective measures, strengthen compliance training, and adjust business processes.

6. A risk assessment and monitoring method for an intelligent compliance risk assessment and monitoring system according to any one of claims 1 to 5, characterized in that: The following steps are involved: S1: Monitor network and system activities in real time and automatically identify potential security threats and compliance risks; S2: Conduct qualitative and quantitative analysis on identified threats and risks, assess their impact, and determine priorities; S3: Prepare and implement compliance strategies based on regulations and industry standards; S4: Automatically implement defense and mitigation measures to fix vulnerabilities based on risk assessment results; S5: Continuously monitor system activity, dynamically adjust policies, and update the system regularly.

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