Context Repository Curation for LLM-Based Security Event Classification
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Solution Overview
Problem
Managing and monitoring complex computing environments with heterogeneous systems generates overwhelming amounts of information, making it difficult for organizations to efficiently utilize valuable data for security and incident investigation.
Innovation Solution
Implementing a context repository management system that includes context records with supplemental information, severity scores, and quality filters to organize and enhance prompts for event classification, using large language models and automated analysis processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If automated monitoring and incident investigation are implemented to protect technology infrastructure, then security protection capability is improved, but information overload occurs making it difficult to efficiently utilize valuable data
Solution Approach 1:
The patent segments the overwhelming information by organizing it into structured context records with specific fields (event type, severity, timestamp, source). This segmentation allows analysts to process information in manageable units rather than facing an undifferentiated mass of data, directly addressing the information overload problem while maintaining comprehensive security monitoring.
Solution Approach 2:
The patent introduces context repository management as an intermediary layer between monitoring systems and analysts. This intermediary automatically collects, stores, and organizes context records from multiple monitoring sources, filtering and structuring the data before presentation to analysts. This mediator resolves the contradiction by enabling comprehensive security monitoring while preventing information overload through automated organization.
2Measurement precision
If comprehensive context records with supplemental information are collected for event classification, then analysis accuracy is improved, but data management complexity increases
Solution Approach 1:
The patent creates a universal context record structure that can accommodate multiple types of supplemental information (event details, metadata, contextual data) in a standardized format. This multi-functional record structure enables accurate event classification across diverse security events without requiring separate management systems for each data type, thus improving accuracy while controlling complexity through standardization.
Solution Approach 2:
The patent manages complexity by dynamically adjusting parameters such as record retention periods, retrieval priorities, and filtering criteria based on event severity and type. Rather than maintaining all possible context records indefinitely, the system selectively retains and prioritizes records based on changing parameters, improving classification accuracy for critical events while reducing overall data management burden.
3Productivity
If context records are organized and filtered using quality filters and severity scores, then decision-making efficiency is improved, but processing time and computational resources increase
Solution Approach 1:
The patent applies quality filters and severity score calculations in advance during context record ingestion and initial processing, rather than performing these operations in real-time during incident response. By pre-processing and pre-ranking context records, the system enables rapid decision-making during security events without the computational overhead being incurred at the critical moment, thus improving decision-making efficiency while managing processing time.
Solution Approach 2:
The patent implements selective processing where low-severity or routine context records are quickly filtered and skipped, allowing the system to focus computational resources on high-severity events requiring detailed analysis. This selective approach rushes through large volumes of low-priority data while applying comprehensive processing only where necessary, improving overall decision-making efficiency without proportionally increasing total processing time.
Data Source
AI summary
Embodiments manage context repositories in computing environments to enhance automated security analysis. Embodiments obtain context records containing supplemental information associated with security events and integrates them into prompts for large language models (LLMs) to generate severity scores for event classification. Embodiments apply criteria to invalidate outdated or unreliable context records based on age, source reliability, and usage frequency, then modifies prompts and repositories accordingly. Enhanced prompts incorporate context record summaries and entity relationship mappings to improve subsequent event analysis. Embodiments dynamically evaluates context records through quality filters, consolidates duplicates, and maintains audit trails with provenance tracking. User interfaces are dynamically transformed based on telemetry metrics and user feedback to optimize analyst workflows. Embodiments enable organizations to maintain curated, high-quality context repositories that continuously improve AI-assisted security analysis while reducing false positives and enhancing incident response effectiveness.