Vulnerability Patch Complexity Classification Using AI Indicators
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Solution Overview
Problem
Current vulnerability patch management systems face inefficiencies due to fragmented and inconsistent information from vendor advisories and vulnerability databases, leading to inaccurate assessments of patch complexity and prolonged exposure to cybersecurity threats.
Innovation Solution
An automated system utilizing artificial intelligence and machine learning models to analyze vulnerability data, identifying complexity indicators such as keywords, product types, and operational steps to classify patches into LOW, MEDIUM, and HIGH complexity levels, optimizing resource allocation and minimizing downtime.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated AI/ML models are used to analyze vulnerability data and classify patch complexity, then productivity and measurement precision are improved, but device complexity increases
Solution Approach 1:
The patent introduces an automated AI/ML model as an intermediary between vulnerability data sources and security teams. This intermediary systematically processes fragmented information from vendor advisories and vulnerability databases, extracting complexity indicators and classifying patches into standardized complexity levels. This resolves the contradiction by automating the analysis process (improving productivity) while containing system complexity through modular AI/ML architecture rather than requiring complex manual coordination systems.
Solution Approach 2:
The patent replaces manual mechanical processes of information gathering, analysis, and classification with automated AI/ML models. Instead of security teams manually reviewing vendor advisories and vulnerability database entries to assess patch complexity, the system uses machine learning models to automatically extract relevant features and classify patches. This substitution improves productivity by eliminating manual labor while the modular nature of the AI/ML system keeps device complexity manageable.
2Measurement precision
If comprehensive vulnerability data is collected from multiple sources, then measurement precision is improved, but loss of time increases due to fragmented information processing
Solution Approach 1:
The patent implements preliminary action by pre-processing and structuring vulnerability data from multiple sources before analysis. The AI/ML models are trained on historical vulnerability data with known complexity outcomes, enabling them to quickly assess new patches. Complexity indicators and classification schemas are pre-established, allowing the system to rapidly process fragmented information without requiring time-consuming manual analysis of each new vulnerability report.
Solution Approach 2:
The patent uses copying by creating standardized representations of vulnerability data from diverse sources. Instead of processing raw, heterogeneous data formats directly, the system copies information into a unified structured format with standardized fields and schemas. This copying process preserves measurement precision by maintaining all relevant details while enabling efficient processing through consistent data structures that the AI/ML models can rapidly analyze.
3Device complexity
If manual assessment of patch complexity is performed, then device complexity is reduced, but productivity decreases and measurement precision deteriorates
Solution Approach 1:
The patent implements self-service by enabling the system to automatically assess patch complexity without requiring extensive manual intervention. The AI/ML models autonomously analyze vulnerability data, extract complexity indicators, and classify patches based on pre-established criteria. This self-service capability dramatically improves productivity by eliminating slow manual assessment processes while the modular architecture keeps device complexity manageable through standardized algorithms and data structures.
Data Source
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AI summary
The present disclosure provides an approach of collecting vulnerability data corresponding to a vulnerability of a target product. The approach provides the vulnerability data to an artificial intelligence model that is trained to determine a complexity indicator from the vulnerability data. The complexity indicator corresponds to applying a vulnerability patch to remediate the vulnerability. The approach determines a patch complexity classification by providing the complexity indicator to the artificial intelligence model and, in turn, provides the patch complexity classification to a target system corresponding to the target product.