Information security risk assessment whole-process management system

Abstract

The invention belongs to the technical field of information security management, and relates to an information security risk assessment whole-process management system. Comprising an omnibearing risk data acquisition module, an intelligent risk assessment engine module, a collaborative rectification execution module, a continuous monitoring and early warning module, a security policy configuration module, a risk knowledge graph construction module and a simulated attack and defense drilling module. According to the information security risk assessment whole-process management system, accurate risk management and control are realized through multi-module collaborative operation, a personalized scheme is accurately generated and strictly tracked by a collaborative rectification execution module according to an assessment conclusion, the rectification efficiency and quality are greatly improved, the risk is timely solved, for example, energy enterprises can quickly repair industrial control system vulnerabilities, and the risk assessment efficiency is improved. And the continuous monitoring and early warning module sensitively captures risk recurrence and new signs by virtue of a real-time architecture and an intelligent algorithm, and carries out multi-channel instant notification and starts a plan so as to ensure that the risk is eliminated in time.

Description

Technical Field

[0001] The present invention belongs to the technical field of information security management, and in particular relates to a full-process management system for information security risk assessment. Background Art

[0002] In today's digital age, information security is of vital importance to enterprises and organizations, and the whole process management system of information security risk assessment has become a key research field. With the development of information technology, enterprise network architecture has become more complex, with more components such as hosts, network devices, and databases, and the amount of data and the frequency of interaction have increased. The sources of security risks are extensive and varied.

[0003] Traditional risk assessment and management systems are becoming increasingly inadequate and unable to meet the needs of dynamic, all-round security management and control. On the one hand, some systems focus on the assessment of a single security factor, such as vulnerability scanning or intrusion detection only, lack a comprehensive perspective, resulting in one-sided risk assessment, inability to accurately locate and quantify the overall security situation, and the tendency to ignore factor-related risks. On the other hand, in terms of management processes, many systems have not formed a closed loop, and risk assessment, rectification, verification, and monitoring are disconnected. After the risk is discovered, the rectification plan may be inaccurate, the implementation is not effectively tracked, or there is no verification and continuous monitoring after the rectification, resulting in repeated risks and affecting business continuity and stability.

[0004] For example, in the financial sector, business systems process massive amounts of transaction data every day, involving customer privacy and financial security. Traditional systems have decentralized vulnerability management, resulting in system attacks and data leaks, causing significant losses to customers and institutions; in the medical industry, medical information systems are interconnected. If the risk assessment and rectification process is broken, patient information security will be threatened, affecting the quality and trust of medical services.

[0005] Although there have been attempts to improve the existing systems, the problem has not been fundamentally solved. Although some systems integrate multiple security technologies, the modules do not work well together and data fusion is insufficient, which affects the efficiency and accuracy of the assessment. Although other systems have optimized the process, they are limited by the static rule base and assessment methods and are difficult to adapt to new risk scenarios. In order to effectively respond to complex security challenges and fill technical gaps, it is urgent to develop a comprehensive, efficient and dynamically adaptable information security risk assessment full-process management system, which should achieve multi-source risk data integration, accurate assessment, closed-loop management and dynamic optimization to ensure the security of information assets. Summary of the invention

[0006] The present invention provides an information security risk assessment full-process management system to solve the problems raised in the background technology.

[0007] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: an information security risk assessment full-process management system, including: a comprehensive risk data collection module, an intelligent risk assessment engine module, a collaborative rectification execution module, a continuous monitoring and early warning module, a security policy configuration module, a risk knowledge graph construction module, and a simulated attack and defense drill module:

[0008] The all-round risk data collection module integrates device port monitoring technology and dynamic risk intelligence capture capabilities to comprehensively collect host device parameters, network traffic characteristics, application vulnerability details, user behavior trajectories, and external threat intelligence source information, and conducts standardized processing;

[0009] The intelligent risk assessment engine module introduces machine learning algorithms, builds a dynamic risk assessment model based on historical risk data and real-time monitoring data as training sets, integrates rule-based auditing and simulated attack assessment strategies, accurately quantifies risk levels, analyzes causes, and predicts potential diffusion paths;

[0010] The collaborative rectification execution module generates personalized rectification plans based on risk assessment conclusions and distributes them to the responsible team through the work order system. It integrates automatic patch distribution, automatic configuration optimization, and process redesign functions, and tracks the rectification process throughout the process through real-time communication protocols and visual dashboards.

[0011] The continuous monitoring and early warning module uses the big data real-time processing architecture and intelligent early warning algorithm to monitor the system after rectification all the time, compare the risk threshold with the dynamic baseline to detect the potential risk recurrence and new risk signs, and push early warning notifications in real time through multiple channels to trigger emergency response plans;

[0012] The security policy configuration module intelligently generates customized security policies based on system architecture, business characteristics and risk assessment results, including network access control policies, data encryption policies, and permission management policies, to ensure that the policies accurately adapt to business security needs and dynamically adjust according to business and risk;

[0013] The risk knowledge graph construction module integrates multi-source risk data, builds a visual risk knowledge graph, deeply explores the correlation between risk entities and potential transmission paths, assists security personnel in gaining a global perspective on risk situations, accurately traces the root causes of risks, and predicts the risk evolution trend, thus improving the scientificity and foresight of risk decision-making;

[0014] The simulated attack and defense drill module regularly builds simulation scenarios based on real network environments and business processes, organizes internal and external teams to conduct attack and defense drills, automatically evaluates the drill results, discovers protection shortcomings, optimizes attack and defense strategies, and enhances the system's practical defense capabilities and emergency response coordination level.

[0015] As a further solution of the present invention: the comprehensive risk data collection module adopts SNMP, Syslog, and Agent technologies to collect system logs and performance indicators from network devices, servers, and databases, captures vulnerability intelligence through vulnerability scanners and intrusion detection systems, parses user authentication and authorization logs to analyze behavioral data, subscribes to threat intelligence platforms to update external threat information, and cleans, converts, and encrypts data according to predefined rules.

[0016] As a further solution of the present invention: the intelligent risk assessment engine module selects logistic regression, decision tree, and neural network algorithms to build a model framework, optimizes hyperparameters through cross-validation and grid search, regularly expands the training set with new risk data and fine-tunes the model structure weights according to business changes, and combines unsupervised learning to monitor data distribution drift to adaptively optimize the model.

[0017] As a further solution of the present invention: the collaborative rectification execution module generates a rectification plan including patch installation, parameter adjustment, architecture reconstruction and process reengineering measures according to the risk level, type and business impact, clarifies the task priority, responsible person and time node, and pushes the work order system after review and approval, and uses the workflow engine to track the progress of task execution, collect operation logs and system status change optimization plans.

[0018] As a further solution of the present invention: the continuous monitoring and early warning module processes system data in real time according to the big data streaming computing framework, calculates the risk value by comparing the risk model with the business rules, monitors the risk status according to the dynamic threshold and the baseline model, pushes the early warning information to the security operation team via E-mail, SMS, and system message, handles the risks according to the preset emergency response plan, and reviews and optimizes the protection strategy.

[0019] As a further solution of the present invention: the security policy configuration module generates initial policies based on the business process risk matrix and compliance requirements using policy templates and expert rule engines, optimizes policies through simulated deployment testing and risk simulation verification, ensures policy consistency and dynamic adaptability through automated deployment tools and policy update mechanisms, and monitors policy execution performance in real time to provide feedback and optimize policy parameters.

[0020] As a further solution of the present invention: the risk knowledge graph construction module uses ontology modeling to define risk entities, relationships and attributes, integrates multi-source data mining association rules to construct a graph architecture, uses graph algorithms to analyze node importance, path accessibility and community structure, and uses visualization technology to intuitively present risk topology and dynamic evolution, providing deep knowledge support for risk insight and decision-making.

[0021] As a further solution of the present invention: the simulated attack and defense drill module formulates the drill rule process according to the business logic and network topology simulation scenario environment according to industry standards and best practices, collects attack and defense data through automated attack tools and monitoring systems, quantitatively evaluates the drill results according to a predefined indicator system, and drives the iteration of protection strategies and the improvement of team capabilities based on review and summary.

[0022] Compared with the prior art, the present invention has the following beneficial effects:

[0023] 1. The information security risk assessment full-process management system realizes accurate risk control through the collaborative operation of multiple modules. The all-round risk data collection module collects diversified data to lay a solid foundation for the assessment and make risks nowhere to hide. For example, the financial transaction system can accurately capture transaction anomalies and potential fraud risk source data. The intelligent risk assessment engine integrates algorithms and strategies, accurately quantifies risk levels, analyzes causes and predicts diffusion paths, and provides support for accurate decision-making and efficient resource allocation in the financial, medical and other industries to avoid losses caused by blindly responding to risks. The collaborative rectification execution module accurately generates personalized plans based on the assessment conclusions and strictly tracks them, greatly improving the efficiency and quality of rectification and timely resolving risks. For example, energy companies can quickly repair industrial control system vulnerabilities to ensure the continuity of energy supply. The continuous monitoring and early warning module relies on real-time architecture and intelligent algorithms to keenly capture risk recurrence and new signs, and instantly notify and initiate plans through multiple channels to ensure that risks are eliminated in a timely manner.

[0024] 2. The information security risk assessment management system has a security policy configuration module that customizes policies based on architecture, business and risk intelligence, dynamically optimizes and adapts, ensures policy effectiveness and adaptability through simulation testing and verification, monitors execution performance feedback and adjustments in real time, and has excellent proactive defense capabilities. For example, e-commerce companies can flexibly allocate network access rights and encryption policies based on business peaks and valleys and risk trends to improve transaction security and efficiency. The risk knowledge graph construction module integrates data to build graphs, deeply mines associations and transmission paths, and visualizes the full risk picture. It assists in tracing and predicting sources, provides key basis for strategic planning, precise resource allocation and innovative security mechanisms, and helps companies to plan security protection in advance. The simulated attack and defense drill module constructs actual combat scenarios to comprehensively test and hone the system's defense and team emergency coordination capabilities, deeply optimizes attack and defense strategies based on drill data, effectively responds to complex and changing attacks, and enhances the system's actual combat resilience. DETAILED DESCRIPTION

[0025] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. 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.

[0026] Example

[0027] The present invention provides the following technical solutions: an information security risk assessment full-process management system, including: a comprehensive risk data collection module, an intelligent risk assessment engine module, a collaborative rectification execution module, a continuous monitoring and early warning module, a security policy configuration module, a risk knowledge graph construction module, and a simulated attack and defense drill module:

[0028] The all-round risk data collection module integrates device port monitoring technology and dynamic risk intelligence capture capabilities to comprehensively collect host device parameters, network traffic characteristics, application vulnerability details, user behavior trajectories, and external threat intelligence source information, and conducts standardized processing;

[0029] Multi-technology integrated collection: The all-round risk data collection module uses the SNMP (Simple Network Management Protocol) protocol to poll network devices to obtain basic device information and performance data, collects server and network device log information through the Syslog protocol, and uses Agent technology to actively collect system operation status data, application logs and user operation records on key hosts and application servers. It combines professional vulnerability scanning tools (such as OpenVAS and Nessus) to regularly conduct in-depth scans of network and application system vulnerabilities. The intrusion detection system (IDS) monitors suspicious attack behavior characteristics in network traffic in real time to capture intrusion event information, parses user authentication and authorization logs (such as Windows domain controller logs and Linux PAM logs) to analyze user access behavior trajectories and abnormal operation modes, subscribes to threat intelligence platforms (such as FireEye and ThreatConnect) to update external real-time threat intelligence (including new malware characteristics, global security event dynamics, and industry-specific attack trends), and builds a comprehensive, multi-level risk data source system to ensure that no risk data is missed.

[0030] Data preprocessing process: The collected data is cleaned, converted and encrypted according to predefined rules. In the data cleaning stage, data filtering technology is used to remove duplicate, invalid and erroneous data records, such as filtering out garbled logs and erroneous system performance indicators caused by network failures; the missing values ​​of key fields are filled in according to data distribution characteristics and business logic through data completion algorithms, such as filling in temporarily missing traffic data points in network traffic monitoring according to historical data trends; in the data conversion stage, heterogeneous data is standardized according to unified standard format specifications, such as unifying different date formats into "YYYY-MM-DDHH:MM:SS" format, and converting network traffic units into bytes per second. At the same time, data encoding technology is used to encrypt sensitive data (such as user passwords, bank card numbers, and corporate trade secrets), and hash algorithms (such as SHA-256) are used for encrypted storage to ensure data confidentiality and integrity, providing high-quality data raw materials for subsequent risk analysis.

[0031] The intelligent risk assessment engine module introduces machine learning algorithms, builds a dynamic risk assessment model based on historical risk data and real-time monitoring data as training sets, integrates rule-based auditing and simulated attack assessment strategies, accurately quantifies risk levels, analyzes causes, and predicts potential diffusion paths;

[0032] Model construction and training optimization: The intelligent risk assessment engine module selects logistic regression, decision tree, and neural network algorithms to build the initial model framework. The model parameters and structure are determined based on the data feature distribution (such as the proportion and distribution law of numerical and categorical data) and business risk characteristics (such as the financial industry focuses on transaction risks and the medical industry focuses on data privacy risks). The labeled risk cases and risk-free samples are used as training sets. Cross-validation (such as K-fold cross-validation to optimize the generalization ability of the model) and grid search technology (traversing hyperparameter combinations to determine the optimal parameter values ​​to improve model accuracy) are used to optimize hyperparameters, and training is regularly expanded with new risk data. The model collects data (such as new security incident data and potential risk data introduced by system updates) and fine-tunes the model structure weights according to business adjustments (such as business process changes and new system launches) and changes in threat situations (emergence of new attack methods and evolution of industry risk trends). It combines unsupervised learning algorithms (such as clustering analysis to monitor data distribution drift) to adaptively optimize the model to adapt to dynamic risk changes and ensure that the assessment accuracy is persistent and stable. For example, in financial transaction systems, as transaction business innovations and network attack methods upgrade, the model dynamically adjusts the risk assessment indicator weights and thresholds to accurately identify new transaction fraud risks and network attack risk patterns.

[0033] Integrated application of assessment strategies: The integration of rule-based auditing and simulated attack assessment strategies improves the comprehensiveness and depth of assessment. Rule-based auditing formulates detailed compliance inspection rules based on international security standards (such as ISO 27001 Information Security Management System Standard, PCI DSS Payment Card Industry Data Security Standard), national laws and regulations (such as Cybersecurity Law, Data Protection Regulations) and enterprise-defined security policies (such as access control policies, data classification and grading standards), and conducts strict inspections and assessments on system configurations, user permissions, and data access behaviors to ensure that the system meets security baseline requirements; simulated attack assessment strategies use open source or commercial vulnerability exploitation tools (such as Metasploit framework) to simulate real attack scenarios in a controllable isolation environment, test the exploitability of system vulnerabilities and the degree of potential risk exposure, and deeply explore zero-day vulnerability risks and complex attack path risks. Through the organic combination of the two, a full range of assessments from static compliance checks to dynamic risk detection is achieved, and risk levels are accurately quantified, causes are analyzed, and potential diffusion paths are predicted.

[0034] The collaborative rectification execution module generates personalized rectification plans based on risk assessment conclusions and distributes them to the responsible team through the work order system. It integrates automatic patch distribution, automatic configuration optimization, and process redesign functions, and tracks the rectification process throughout the process through real-time communication protocols and visual dashboards.

[0035] Plan generation and task allocation: The collaborative rectification execution module generates personalized rectification plans based on the risk assessment conclusions. Based on the risk level (high, medium and low risk classification standards), risk type (vulnerability risk, configuration risk, operational risk, external threat risk classification) and business impact scope (such as impact on business function availability, data integrity, and confidentiality), it generates a plan that covers a combination of multi-dimensional rectification measures such as patch installation (pushing security patches for system and application vulnerabilities), parameter adjustment (optimizing system configuration parameters to improve security), architecture reconstruction (redesigning and optimizing system architecture with security risks) and process reengineering (improving risky business processes to eliminate security weaknesses). It clarifies the priority of each rectification task (determines the order based on the urgency of the risk and the importance of the business), the responsible person (precisely locates the responsible person based on the organizational structure and authority configuration) and the time node (sets the task start and end time and milestone nodes), and pushes it to the work order system after the review and approval process (reviewed by security experts and business department heads in accordance with the company's internal security management specifications) to ensure that the rectification task allocation is scientific and reasonable, the responsibilities are clear, and the process is standardized.

[0036] Execution tracking and optimization adjustment: The work order system uses the workflow engine to drive the execution of rectification tasks, track the progress of task execution in real time (intuitively present the task execution status through task status updates and progress percentage displays), collect rectification operation logs (record the operation steps, command execution results, and system status change information during task execution) and system status change data (real-time monitoring of system performance indicators, vulnerability repair status, and security configuration effectiveness after rectification), and optimize the rectification plan based on task feedback and data monitoring and analysis. If there are difficulties in task execution or changes in risks, adjust the task plan and allocate resources (dynamic allocation of human and technical resources) in a timely manner to ensure that the rectification is completed on time and risks are effectively eliminated, forming a closed-loop management mechanism to improve the quality and efficiency of rectification. For example, during the rectification of key business systems of an enterprise, based on real-time monitoring data, it is found that patch installation causes compatibility issues, and the rectification strategy is adjusted in a timely manner to roll back the patch and re-evaluate and formulate alternative plans to ensure that risk rectification tasks are completed under the premise of stable operation of the business system.

[0037] The continuous monitoring and early warning module uses the big data real-time processing architecture and intelligent early warning algorithm to monitor the system after rectification all the time, compare the risk threshold with the dynamic baseline to detect the potential risk recurrence and new risk signs, and push early warning notifications in real time through multiple channels to trigger emergency response plans;

[0038] Real-time monitoring and risk calculation: The continuous monitoring and early warning module processes massive system data in real time based on a big data streaming computing framework (such as Apache Flink or Spark Streaming), and conducts in-depth review of the cause of the incident based on predefined risk models (integrating statistical analysis models, machine learning models such as decision trees, support vector machines, etc.) and business operations (comprehensively analyzing attack paths, vulnerability exploitation details, system weaknesses and human error factors), optimizing protection strategies (targeted adjustment of security strategies, strengthening system configurations, updating vulnerability patches and improving employee safety awareness training). It continuously improves system security resilience and emergency response coordination levels to ensure that risks are eliminated in a timely manner and that the system continues to operate stably. For example, after a data leak, the source of the attack is traced back to the illegal operation of internal personnel and external phishing email attacks through detailed log analysis. The email security gateway filtering rules are then strengthened, internal data access audit upgrades and all-staff safety awareness training are implemented to plug security loopholes in all directions to prevent similar incidents from happening again.

[0039] The security policy configuration module intelligently generates customized security policies based on system architecture, business characteristics and risk assessment results, including network access control policies, data encryption policies, and permission management policies, to ensure that the policies accurately adapt to business security needs and dynamically adjust according to business and risk;

[0040] Strategy generation and optimization mechanism: The security policy configuration module generates initial policies based on the business process risk matrix (a matrix is ​​constructed based on the risk assessment results of each link in the business process to quantify the risk distribution) and compliance requirements (complying with domestic and international security regulations and industry specifications) using policy templates (predefined general security policy frameworks) and expert rule engines (built-in security expert experience rule bases). The policy parameters and rule logic are optimized through simulated deployment testing (simulating the effect of policy implementation in a virtual test environment to evaluate the impact on business system functions, performance and security) and risk simulation verification (using attack simulation tools to test the ability of policies to resist potential risks). For example, in the formulation of financial data encryption policies, according to data sensitivity classification and transmission and storage scenarios, the performance and security of different encryption algorithms are simulated and tested, and the national secret SM4 algorithm is selected for domestic data encryption and the AES algorithm is selected for cross-border data encryption. The key management mechanism is optimized to ensure the security of data throughout its life cycle.

[0041] Deployment and dynamic update maintenance: Use automated deployment tools (such as Ansible and Puppet to automatically push and remotely execute policy scripts) to seamlessly deploy optimized policies to each node of the production system to ensure policy consistency. Automatically update policy versions based on risk monitoring feedback and business changes (such as new business launches, system architecture upgrades, and external threat intelligence updates). Real-time monitoring of policy execution performance (through statistical analysis of the number of policy hits, the number of illegal access interceptions, and the incidence of system security incidents) to provide feedback and optimize policy parameters, forming a closed-loop policy management system to continuously improve the system's security protection level. For example, when an enterprise expands its overseas business and introduces new cloud computing service scenarios, the security policy configuration module automatically updates network access control policies, data sovereignty protection policies, and cloud security configuration baselines based on cloud service characteristics and international compliance differences to ensure the security and compliance of global business expansion.

[0042] The risk knowledge graph construction module integrates multi-source risk data, builds a visual risk knowledge graph, deeply explores the correlation between risk entities and potential transmission paths, assists security personnel in gaining a global perspective on risk situations, accurately traces the root causes of risks, and predicts the risk evolution trend, thus improving the scientificity and foresight of risk decision-making;

[0043] Foundation for knowledge graph construction: The risk knowledge graph construction module uses ontology modeling technology to define risk entities (accurately abstracting key system elements such as network device type, server operating system version, application function module, user role permission level, vulnerability number classification, attack method name and other entity concepts), relationships (depicting semantic connections such as network connection relationships, data read and write access relationships, vulnerability exploitation relationships, risk propagation impact relationships, etc.) and attributes (describing entity inherent characteristics such as device hardware parameters, vulnerability severity scores, attack occurrence timestamps, and dynamic state attributes such as device online and offline status, vulnerability repair progress, and risk disposal stages). It integrates multi-source data (deep analysis of system logs, vulnerability scanning reports, threat intelligence data, business process data, user behavior data and other structured and unstructured data) to mine association rules and build a graph architecture. For example, it mines the association between users' frequent abnormal access to applications and specific vulnerability exploits from system logs, and analyzes the flow of high-value data assets and potential risk aggregation nodes from business data associations, injecting rich semantic knowledge into the graph.

[0044] Graph analysis and application expansion: Use graph algorithms (such as PageRank algorithm to evaluate node importance and accurately locate key risk sources such as core database nodes and key network service nodes; shortest path algorithm to analyze the shortest risk propagation path in the system to assist in the formulation of blocking strategies; community discovery algorithm to identify risk aggregation areas such as high-risk vulnerability-related application clusters and malicious user behavior communities) to analyze node importance, path accessibility and community structure, and use visualization technology (using D3.js, Echarts and other visualization libraries to draw interactive graphs) to intuitively present risk topology and dynamic evolution. In security decision-making, security personnel use the graph to gain a global perspective on the risk situation, accurately trace the root cause of the risk (quickly trace back the attack link to determine the initiator) and predict the risk evolution trend (predict the potential impact range and severity based on the risk correlation propagation trend), so as to provide in-depth knowledge support for the formulation of precise risk prevention and control strategies, optimization of resource allocation and emergency response, such as adjusting security resources to focus on protecting key risk areas and potential diffusion path nodes based on graph analysis.

[0045] The simulated attack and defense drill module regularly builds simulation scenarios based on real network environments and business processes, organizes internal and external teams to conduct attack and defense confrontation drills, automatically evaluates the drill results, discovers protection shortcomings, optimizes attack and defense strategies, and enhances the system's practical defense capabilities and emergency response coordination level;

[0046] Construction and organization of the drill environment: The simulated attack and defense drill module simulates the scenario environment based on the company's real business logic (accurately reproduces business process steps, transaction rules, and data interaction relationships) and network topology (one-to-one restoration of network architecture levels, device connection relationships, and IP allocation rules). It formulates drill rules and processes based on industry standards (such as payment security standards in the financial industry and industrial control security specifications in the energy industry) and best practices (drawing on excellent case experience in global industry attack and defense drills) (clarifying the attacker's goals and tasks, attack method restrictions, defender's defense responsibilities, monitoring and evaluation indicators, and drill time periods), organizes the company's internal security team, key personnel in the business department, and external professional security vendors' attack and defense teams to participate in the drill, builds an actual combat environment for internal and external collaboration and attack and defense confrontation, and comprehensively tests the system's security defense capabilities and the level of coordination between personnel in emergency response.

[0047] Effect evaluation and capability improvement strategy: Use automated attack tools (such as vulnerability scanners, penetration testing toolsets, customized malware simulation tools) and monitoring systems (deploy network traffic monitoring, host behavior monitoring, and centralized security event log management systems) to collect attack and defense data (detailed records of attack trajectories, vulnerability discovery and exploitation details, defense response actions and time nodes, and system status change data), and quantitatively evaluate the results of the drill based on a predefined indicator system (comprehensive attack success rate, defense success rate, vulnerability discovery and repair efficiency, business interruption duration, data leakage scale and other quantitative indicators). In-depth review of the drill process summarizes lessons learned, explores protection shortcomings (analyzes defense system vulnerabilities, security policy defects, personnel skill shortcomings and coordination barriers), optimizes attack and defense strategies (targeted adjustments to attack tactical planning, defense technology architecture, security training programs and emergency plan processes), and forms a closed-loop management of drills to continuously improve the system's practical defense capabilities and emergency response coordination levels. For example, financial companies optimize online transaction security protection strategies, strengthen real-time transaction monitoring mechanisms, and improve customer service personnel's emergency response capabilities for security incidents based on drill results to effectively resist real network attack threats.

[0048] Specifically, the comprehensive risk data collection module uses SNMP, Syslog, and Agent technologies to collect system logs and performance indicators from network devices, servers, and databases, captures vulnerability intelligence through vulnerability scanners and intrusion detection systems, parses user authentication and authorization logs to analyze behavioral data, subscribes to threat intelligence platforms to update external threat information, and cleans, converts, and encrypts data according to predefined rules.

[0049] Specifically, the intelligent risk assessment engine module selects logistic regression, decision tree, and neural network algorithms to build a model framework, optimizes hyperparameters through cross-validation and grid search, regularly expands the training set with new risk data and fine-tunes the model structure weights according to business changes, and combines unsupervised learning to monitor data distribution drift to adaptively optimize the model.

[0050] Specifically, the collaborative rectification execution module generates a rectification plan including patch installation, parameter adjustment, architecture reconstruction and process reengineering measures according to the risk level, type and business impact, clarifies the task priority, responsible person and time node, pushes the work order system after review and approval, and uses the workflow engine to track the progress of task execution, collect operation logs and system status change optimization plans.

[0051] Specifically, the continuous monitoring and early warning module processes system data in real time based on the big data streaming computing framework, calculates risk values ​​by comparing risk models and business rules, monitors risk status based on dynamic thresholds and baseline models, pushes early warning information to the security operations team via e-mail, text messages, and system messages, handles risks based on preset emergency response plans, and reviews and optimizes protection strategies.

[0052] Specifically, the security policy configuration module generates initial policies based on the business process risk matrix and compliance requirements using policy templates and expert rule engines, optimizes policies through simulated deployment testing and risk simulation verification, ensures policy consistency and dynamic adaptability through automated deployment tools and policy update mechanisms, and monitors policy execution performance in real time to provide feedback and optimize policy parameters.

[0053] Specifically, the risk knowledge graph construction module uses ontology modeling to define risk entities, relationships and attributes, integrates multi-source data to mine association rules to build a graph architecture, uses graph algorithms to analyze node importance, path accessibility and community structure, and uses visualization technology to intuitively present risk topology and dynamic evolution, providing deep knowledge support for risk insight and decision-making.

[0054] Specifically, the simulated attack and defense drill module simulates the scenario environment based on business logic and network topology, formulates drill rules and processes based on industry standards and best practices, collects attack and defense data through automated attack tools and monitoring systems, quantitatively evaluates drill results based on a predefined indicator system, and drives protection strategy iteration and team capacity improvement based on review and summary.

[0055] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may alter, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. An information security risk assessment full process management system, characterized in that: include: Comprehensive risk data collection module, intelligent risk assessment engine module, collaborative rectification execution module, continuous monitoring and early warning module, security policy configuration module, risk knowledge graph construction module, simulated attack and defense drill module: The all-round risk data collection module integrates device port monitoring technology and dynamic risk intelligence capture capabilities to comprehensively collect host device parameters, network traffic characteristics, application vulnerability details, user behavior trajectories, and external threat intelligence source information, and conducts standardized processing; The intelligent risk assessment engine module introduces machine learning algorithms, builds a dynamic risk assessment model based on historical risk data and real-time monitoring data as training sets, integrates rule-based auditing and simulated attack assessment strategies, accurately quantifies risk levels, analyzes causes, and predicts potential diffusion paths; The collaborative rectification execution module generates personalized rectification plans based on risk assessment conclusions and distributes them to the responsible team through the work order system. It integrates automatic patch distribution, automatic configuration optimization, and process redesign functions, and tracks the rectification process throughout the process through real-time communication protocols and visual dashboards. The continuous monitoring and early warning module uses the big data real-time processing architecture and intelligent early warning algorithm to monitor the system after rectification all the time, compare the risk threshold with the dynamic baseline to detect the potential risk recurrence and new risk signs, and push early warning notifications in real time through multiple channels to trigger emergency response plans; The security policy configuration module intelligently generates customized security policies based on system architecture, business characteristics and risk assessment results, including network access control policies, data encryption policies, and permission management policies, to ensure that the policies accurately adapt to business security needs and dynamically adjust according to business and risk; The risk knowledge graph construction module integrates multi-source risk data, builds a visual risk knowledge graph, deeply explores the correlation between risk entities and potential transmission paths, assists security personnel in gaining a global perspective on risk situations, accurately traces the root causes of risks, and predicts the risk evolution trend, thus improving the scientificity and foresight of risk decision-making; The simulated attack and defense drill module regularly builds simulation scenarios based on real network environments and business processes, organizes internal and external teams to conduct attack and defense drills, automatically evaluates the drill results, discovers protection shortcomings, optimizes attack and defense strategies, and enhances the system's practical defense capabilities and emergency response coordination level.

2. The information security risk assessment full process management system according to claim 1 is characterized by: The comprehensive risk data collection module uses SNMP, Syslog, and Agent technologies to collect system logs and performance indicators from network devices, servers, and databases, captures vulnerability intelligence through vulnerability scanners and intrusion detection systems, parses user authentication and authorization logs to analyze behavioral data, subscribes to threat intelligence platforms to update external threat information, and cleans, converts, and encrypts data according to predefined rules.

3. The information security risk assessment full process management system according to claim 1 is characterized by: The intelligent risk assessment engine module selects logistic regression, decision tree, and neural network algorithms to build a model framework, optimizes hyperparameters through cross-validation and grid search, regularly expands the training set with new risk data, and fine-tunes the model structure weights according to business changes, and combines unsupervised learning to monitor data distribution drift to adaptively optimize the model.

4. The information security risk assessment full process management system according to claim 1 is characterized by: The collaborative rectification execution module generates a rectification plan including patch installation, parameter adjustment, architecture reconstruction and process reengineering measures according to the risk level, type and business impact, clarifies the task priority, responsible person and time node, and pushes the work order system after review and approval, and uses the workflow engine to track the progress of task execution, collect operation logs and system status change optimization plans.

5. The information security risk assessment full process management system according to claim 1 is characterized by: The continuous monitoring and early warning module processes system data in real time based on the big data streaming computing framework, calculates risk values ​​by comparing risk models and business rules, monitors risk status based on dynamic thresholds and baseline models, pushes early warning information to the security operations team via e-mail, text messages, and system messages, handles risks based on preset emergency response plans, and reviews and optimizes protection strategies.

6. The information security risk assessment full process management system according to claim 1 is characterized by: The security policy configuration module generates initial policies based on the business process risk matrix and compliance requirements using policy templates and expert rule engines, optimizes policies through simulated deployment testing and risk simulation verification, ensures policy consistency and dynamic adaptability through automated deployment tools and policy update mechanisms, and optimizes policy parameters through real-time monitoring of policy execution performance feedback.

7. The information security risk assessment full process management system according to claim 1 is characterized by: The risk knowledge graph construction module uses ontology modeling to define risk entities, relationships and attributes, integrates multi-source data to mine association rules to build a graph architecture, uses graph algorithms to analyze node importance, path accessibility and community structure, and uses visualization technology to intuitively present risk topology and dynamic evolution, providing deep knowledge support for risk insight and decision-making.

8. The information security risk assessment full process management system according to claim 1 is characterized by: The simulated attack and defense drill module simulates the scenario environment based on business logic and network topology, formulates the drill rules and processes based on industry standards and best practices, collects attack and defense data through automated attack tools and monitoring systems, quantitatively evaluates the drill results based on a predefined indicator system, and drives the iteration of protection strategies and the improvement of team capabilities based on review and summary.