Constraint Specification Matrix for Faster Security Data Interpretation
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Solution Overview
Problem
Existing systems struggle to efficiently and accurately detect unauthorized access, system errors, or data loss events in large computing environments due to the reliance on AI/ML patterns that require human expertise for interpretation, leading to delayed responses and security breaches.
Innovation Solution
Integration of AI/ML techniques with a dynamic Constraint Specification Matrix that adapts to real-world experiences, incorporating human expertise and domain-specific knowledge to refine vulnerability detection, using a customizable template that continuously updates and optimizes parameters for enhanced security posture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If AI/ML techniques are used to identify patterns in security data, then pattern recognition capability is improved, but response time deteriorates due to requirement for human expertise interpretation
Solution Approach 1:
The patent introduces a constraint specification matrix as an intermediary layer between AI/ML pattern recognition and human analysis. The matrix automatically evaluates identified patterns against predefined constraints and parameters, providing structured output that reduces the time required for human expertise interpretation while maintaining accurate pattern recognition.
Solution Approach 2:
The system performs preliminary actions by pre-defining constraint specifications, parameters, and evaluation criteria in the matrix before security events occur. This preparation enables rapid automated assessment of AI/ML identified patterns without requiring real-time human expertise, thus reducing response time while preserving detection accuracy.
2Reliability
If comprehensive security monitoring is implemented across large computing environments, then detection capability is improved, but resource consumption increases
Solution Approach 1:
The patent segments the comprehensive security monitoring task into modular components: data collection from multiple sources, AI/ML pattern identification, constraint specification matrix evaluation, and prioritized alert generation. This segmentation enables efficient resource utilization by processing only relevant data through each stage rather than analyzing all data comprehensively.
Solution Approach 2:
The constraint specification matrix applies different evaluation criteria and parameters to different types of security patterns and data sources. This local quality approach optimizes resource consumption by tailoring the depth and method of analysis to the specific characteristics of each security event, rather than applying uniform comprehensive monitoring to all data.
3Measurement precision
If human expertise is integrated into vulnerability detection, then detection accuracy is improved, but system complexity increases
Solution Approach 1:
The constraint specification matrix is designed to be self-updating and self-optimizing based on feedback from detected vulnerabilities and emerging threat patterns. This self-service capability reduces system complexity by automating the integration of human expertise through machine learning that continuously refines constraint specifications without requiring manual reconfiguration of the entire system.
Data Source
AI summary
The present invention encompasses systems, computer program products, and methods for machine interpretation of security data. It identifies various data sources providing metrics and parameters, which include system logs, network traffic data, user activity records, and application logs. The system retrieves these metrics and parameters through an application programming interface and transforms them from unstructured to structured data. This structured data is then featured and stored. A dynamic consolidated matrix, known as the Constraint Specification Matrix (CSM), is generated from these features. A machine learning model is trained to discern correlations and patterns within the CSM's features. Lastly, the system transmits instructions to present these correlations and patterns via a user interface on a user device, allowing for user-friendly visualization and interpretation of the analyzed security data.