Security Vulnerability Recommendation via Analyst Expertise Modeling
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Solution Overview
Problem
Current security management systems fail to effectively recommend the most relevant and urgent security vulnerabilities to human analysts, as they lack the ability to prioritize issues based on analyst expertise and issue severity, leading to inefficient resolution of security threats.
Innovation Solution
A data processing system that generates analyst-issue models by analyzing interaction logs and issue data, using machine learning to recommend security vulnerabilities that match an analyst's expertise and criticality, thereby prioritizing issues for efficient resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If automated security testing tools are used to identify vulnerabilities, then the quantity of detected security issues increases, but the ability to prioritize and resolve critical issues efficiently deteriorates due to lack of personalization to analyst expertise
Solution Approach 1:
The system automatically generates analyst models and issue recommendations without manual intervention. The security management system self-services by autonomously analyzing interaction logs, building analyst profiles, and generating personalized vulnerability recommendations, eliminating the need for manual prioritization configuration
Solution Approach 2:
The system monitors and analyzes security analyst interactions with vulnerability issues to generate feedback loops. By continuously learning from analyst behavior patterns, the system refines its understanding of analyst expertise and adjusts recommendations accordingly, improving prioritization accuracy over time
2Loss of information
If security management systems provide comprehensive vulnerability lists to analysts, then complete information is available, but analysts spend excessive time filtering and prioritizing issues, leading to loss of time
Solution Approach 1:
The system performs preliminary prioritization actions by pre-analyzing vulnerability data and matching it with analyst expertise models before presenting issues to analysts. This advance preparation ensures that when analysts receive vulnerability lists, they are already personalized and prioritized according to each analyst's specific expertise and the criticality of issues
Solution Approach 2:
The system extracts and separates the prioritization function from the comprehensive vulnerability list. By using machine learning to identify and extract the most relevant vulnerabilities for each analyst based on their expertise, the system presents only the necessary subset of issues rather than the complete unfiltered list
3Device complexity
If generic vulnerability recommendations are provided to all analysts, then system complexity is reduced, but the relevance and urgency of recommendations to individual analysts deteriorates
Solution Approach 1:
The system dynamically changes recommendation parameters based on individual analyst characteristics. By adjusting recommendation criteria according to each analyst's demonstrated expertise, interaction patterns, and performance metrics, the system transforms static generic recommendations into dynamic personalized suggestions without requiring complex manual configuration
Data Source
AI summary
Mechanisms are provided for accessing security vulnerability issue information. The mechanisms monitor security analyst interactions with security vulnerability issues via the security management system to generate analyst interaction log data, and generate one or more security analyst models corresponding to one or more security analysts by performing a machine learning operation on the analyst interaction log data. The mechanisms generate an analyst-issue model based on the one or more security vulnerability issue models and the one or more security analyst models, and generate an issue recommendation for a security analyst based on the analyst-issue model.