Vulnerability Correlation Engine Using CWE and CVE Identifiers
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Solution Overview
Problem
Current network security systems face challenges in reconciling vulnerability data from multiple independent assessment technologies, leading to incomplete or misleading risk assessments due to limited automated data reconciliation capabilities.
Innovation Solution
A method that correlates Static Application Security Testing (SAST) assessments with network vulnerability scans using Common Weakness Enumeration (CWE-ID) and Common Vulnerabilities and Exposures (CVE-ID) identifiers, and instruments software applications to log vulnerability locations, enabling precise matching of SAST and network vulnerabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple independent vulnerability assessment technologies are employed, then vulnerability assessment coverage is improved, but data reconciliation capability deteriorates
Solution Approach 1:
The patent introduces a reconciliation engine as an intermediary component that receives vulnerability data from multiple independent assessment technologies (network vulnerability scanner, SAST tool, container vulnerability scanner) and correlates them using standardized identifiers (CVE-ID, CWE-ID). This mediator translates and harmonizes data from different sources into a unified vulnerability assessment, resolving the contradiction between comprehensive coverage and reconciliation capability.
2Measurement precision
If multiple independent vulnerability assessment technologies are employed, then vulnerability detection accuracy is improved, but system complexity increases
Solution Approach 1:
The reconciliation engine is designed as a universal system that can process and correlate vulnerability data from multiple different assessment technologies (network scanners, SAST tools, container scanners) through a common interface and standardized identifier system. This multi-functional approach enables accurate vulnerability detection across diverse sources while managing system complexity through a unified correlation framework.
3Measurement precision
If automated data reconciliation intelligence is enhanced, then vulnerability matching accuracy is improved, but processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-processing vulnerability data from multiple sources and organizing it according to standardized identifiers (CVE-ID, CWE-ID) before the actual correlation process. The reconciliation engine prepares reference data and establishes mapping relationships in advance, which accelerates the matching process while maintaining high accuracy in vulnerability correlation.
Data Source
AI summary
The present disclosure relates to methods for correlating security vulnerability assessment data from a network vulnerability assessment, a static application security test (SAST) assessment and/or a zero day vulnerability metadata source.


