Temporal Graph Threat Analysis for Insider Risk Scoring

The patent transitions from static rule-based detectors to dynamic machine learning models that continuously learn and adapt to new threat patterns. The system uses temporal graph neural networks that evolve their detection capabilities over time, allowing it to maintain high reliability while detecting long-term sophisticated threats.

The system implements feedback mechanisms where detection results and threat intelligence are continuously fed back into the machine learning models to improve future detections. This closed-loop approach reduces false positives by learning from past mistakes and enhances detection accuracy through continuous refinement of detection algorithms.

The patent merges multiple individual detectors into a unified machine learning framework that processes user behavior, network activity, and threat intelligence together. This consolidation enables contextual analysis by examining multiple data sources simultaneously and performing peer grouping to identify coordinated threats, thereby reducing information loss while maintaining manageable system complexity through integrated architecture.

The system creates a universal detection platform that performs multiple functions including anomaly detection, peer grouping, contextual analysis, and threat scoring within a single framework. This multi-functional approach eliminates the need for separate specialized detectors, reducing overall system complexity while preserving and enhancing contextual information through unified processing.

The patent segments the computational workload into distinct processing stages: data collection from multiple sources, feature extraction and embedding, temporal graph construction, and risk score calculation. This segmentation allows each component to be optimized independently, managing computational complexity while maintaining high detection precision through specialized processing at each stage.

The system performs preliminary actions by pre-processing and embedding user behavior data into compact representations before main detection operations. Temporal graphs are constructed in advance with pre-computed features, reducing the computational burden during real-time threat detection and allowing the system to maintain high precision without excessive resource requirements during critical detection phases.