Self-Learning Alert Prioritization for SOC Threat Triage

The patent replaces manual mechanical filtering by SOC analysts with an automated machine learning-based alert triage system. The system uses trained machine learning models to automatically analyze alert characteristics, assess threat levels, and prioritize alerts, substituting human manual processes with automated computational analysis that maintains high precision in threat identification while significantly reducing alert volume presented to analysts.

The alert triage system operates autonomously without requiring continuous human intervention. The machine learning models automatically process incoming alerts, apply filtering criteria, assess threat levels, and generate prioritized alert queues. The system serves itself by continuously learning from feedback and improving its triage capabilities, reducing dependency on manual analyst involvement in the filtering process.

The system replaces the mechanical process of manual alert review with automated machine learning-based triage. Machine learning models rapidly analyze alert characteristics, assess threat levels, and prioritize alerts without the time constraints of human analysts. This substitution maintains high-quality filtering while dramatically improving processing speed and analyst productivity, as analysts only need to review the prioritized subset of alerts rather than manually filtering all alerts.

The machine learning system performs preliminary triage and filtering actions before alerts are presented to analysts. By pre-assessing threat levels, pre-prioritizing alerts, and pre-filtering low-priority items using automated models, the system prepares alerts in advance for analyst review. This preliminary automated action reduces the workload on analysts and improves overall productivity by eliminating the need for manual review of obviously low-priority alerts.

The alert triage system is designed as a multi-functional platform that combines machine learning model training, alert analysis, threat assessment, prioritization, and feedback processing into a single integrated system. The same infrastructure supports multiple machine learning models for different threat types, handles various alert sources, and provides both automated triage and analyst interfaces. This universal design consolidates complexity into a unified system rather than requiring separate systems for each function, making the complexity more manageable.

The system employs a nested architecture where machine learning models are embedded within the alert processing pipeline, which is itself nested within a broader security operations framework. The knowledge base and training data structures are nested within the model training processes, which are nested within the overall triage system. This nested organization allows the system to manage complexity by structuring components hierarchically, with each layer building upon and utilizing the layers below it.