Correlated Vulnerability Remediation via Graph Database
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Solution Overview
Problem
Current methods for fixing application vulnerabilities lack a unified approach to correlate static and dynamic security assessments, leading to inefficient and costly remediation processes with high false positives, resulting in many critical vulnerabilities remaining unfixed.
Innovation Solution
A method that integrates knowledge-based risk assessments by identifying vulnerabilities through both static and dynamic security assessments, correlating results to determine the shortest path for remediation, and storing this information in a graph database to automate the remediation process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If static security assessment is performed to identify vulnerabilities, then vulnerabilities can be detected early in SDLC, but false positives increase and manual correlation effort increases
Solution Approach 1:
The system implements feedback loops where dynamic security assessment results are used to validate and filter static assessment findings. The correlation engine continuously refines vulnerability identification by comparing static code analysis results against actual runtime behavior observed in dynamic assessments, reducing false positives through iterative validation.
Solution Approach 2:
A correlation engine acts as an intermediary between static and dynamic security assessment systems. This intermediary component integrates findings from both assessment types, automatically matches vulnerability instances across assessments, and prioritizes remediation based on correlated risk levels, eliminating the need for manual correlation effort.
2Measurement precision
If dynamic security assessment is performed to simulate attacker behavior, then real exploit scenarios are identified, but assessment coverage is limited and remediation guidance is insufficient
Solution Approach 1:
The system merges the strengths of both static and dynamic security assessments into a unified vulnerability management platform. Static assessment provides comprehensive code-level coverage and remediation guidance, while dynamic assessment validates actual exploitability. The correlation engine combines these complementary approaches to deliver both precise exploit detection and actionable remediation instructions.
Solution Approach 2:
The unified correlation platform serves multiple functions: it performs vulnerability identification, validation, prioritization, and remediation guidance generation. By making the system universal, it handles both the precision requirements of exploit detection and the operational requirements of remediation implementation through a single integrated solution.
3Manufacturing precision
If individual assessment recommendations are used for remediation, then each vulnerability type can be addressed specifically, but remediation becomes time and cost intensive
Solution Approach 1:
The system performs preliminary correlation and validation of vulnerability instances before remediation begins. By pre-matching static and dynamic assessment findings and pre-prioritizing vulnerabilities based on correlated risk levels, the system prepares remediation pathways in advance, enabling faster execution without sacrificing precision.
Solution Approach 2:
The correlation engine dynamically changes the prioritization parameters based on the correlation results. Vulnerabilities that appear in both static and dynamic assessments are assigned higher priority scores, while those appearing in only one assessment type receive lower priority. This parameter adjustment enables the system to focus remediation efforts on the most critical vulnerabilities first, improving overall remediation speed and efficiency.
Data Source
AI summary
The invention relates to a system and method for fixing application vulnerabilities through a correlated remediation approach. This invention involves identifying application vulnerabilities through dynamic and static assessment of an application. The vulnerability instances reported in the static assessment are fixed using standard code fixes. The assessment results obtained from the static and the dynamic assessment are then correlated to identify how many vulnerability instances reported in the static assessment are fixed by the standard code fixes. If a vulnerability instance reported in the dynamic assessment corresponds to more than one vulnerability instance reported in the static assessment then the shortest and most cost effective path to fix the vulnerability instance is determined. These results are stored in a graph database and based on the graph database the application vulnerabilities are fixed. An inference engine can be used to identify the correct fix for an application vulnerability.