Semantic Parser for SIEM Log Analysis

The patent segments the centralized SIEM system into multiple distributed SIEM nodes deployed across different locations. Each node independently collects, indexes, and processes log information locally, eliminating the single centralized collection point that causes latency. This segmentation allows parallel data processing and reduces the time required to analyze security events across the entire environment.

The patent transitions from a single-point centralized architecture to a multi-point distributed architecture, adding spatial dimensionality to the system. By deploying SIEM nodes across multiple dimensions (different locations, networks, or cloud environments), the system achieves both comprehensive data collection and reduced latency through parallel processing at multiple points simultaneously.

The patent divides the single large centralized database into multiple distributed databases at different SIEM nodes. Each node maintains local context information and indexes relevant to its deployed applications or services, enabling fast local queries without scanning the entire environment database. This segmentation preserves necessary environment context while dramatically improving query performance and scalability.

The patent implements local quality by having each SIEM node maintain and process only the context information relevant to its specific deployment area. Instead of uniformly processing all environment context across the entire system, each node optimizes for local relevance, improving overall system performance while maintaining adequate context for security correlations at each location.

The patent implements automated self-service security analysis through machine learning models and correlation engines that automatically analyze suspicious events without requiring manual human intervention. The system performs self-diagnosis, automatically determines the nature of security events, and generates appropriate responses, thereby maintaining high analysis accuracy while dramatically increasing throughput and productivity.

The patent incorporates feedback mechanisms where the automated analysis results are continuously refined through learning from analyst feedback and actual incident outcomes. This feedback loop allows the automated system to improve its accuracy over time while maintaining high productivity, as the machine learning models become increasingly proficient at security event analysis without requiring constant manual intervention.