Anomaly Prioritization Engine for Security Alert Management
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Solution Overview
Problem
Security operations centers (SOCs) face challenges in managing and addressing the vast number of security alerts generated by SIEM systems, as the sheer volume of behavior anomalies exceeds the capacity of even a large, highly trained staff, leading to potential overwhelm and inefficient resource allocation.
Innovation Solution
A behavior anomaly management engine that identifies and correlates weak indicators, selectively groups them based on connections among entities, and reports aggregated collections to the SOC only when their aggregate score exceeds a predefined threshold, thereby prioritizing and reducing the number of alerts for further investigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a SIEM system generates security alerts for all detected behavior anomalies, then security monitoring coverage is improved, but the volume of alerts overwhelms SOC staff capacity
Solution Approach 1:
The patent introduces an anomaly prioritization engine as an intermediary component between the SIEM system and SOC analysts. This engine receives all detected anomalies from the SIEM system, processes them through machine learning models to determine priority levels, and then presents a filtered, prioritized view to SOC staff. The intermediary resolves the contradiction by maintaining comprehensive monitoring coverage while reducing the effective alert volume that consumes staff capacity.
Solution Approach 2:
The system implements self-service capabilities through automated anomaly prioritization using machine learning models. Instead of relying on SOC staff to manually triage all alerts, the system autonomously analyzes anomaly characteristics, compares them against learned patterns, and automatically assigns priority levels. This self-service approach to alert triage preserves comprehensive security monitoring while eliminating the manual workload that would otherwise overwhelm staff.
2Measurement precision
If SOC staff manually investigate all security alerts, then identification accuracy is improved, but time consumption and resource allocation efficiency deteriorate
Solution Approach 1:
The patent applies local quality by providing different levels of analysis and attention to different anomalies based on their characteristics and priority levels. High-priority anomalies receive more thorough automated analysis and are presented prominently to analysts, while low-priority anomalies receive minimal processing. This localized approach to quality assurance maintains high identification accuracy for critical threats while reducing time investment on less significant alerts.
Solution Approach 2:
The system performs partial action by selectively applying full investigative resources only to anomalies that meet certain priority thresholds. For lower-priority anomalies, the system performs minimal automated analysis and presents them for quick analyst review without extensive manual investigation. This partial action approach maintains high accuracy for critical threats while significantly reducing overall investigation time and resource consumption.
3Reliability
If the system reports all behavior anomalies to the SOC, then completeness of security monitoring is improved, but the ability to prioritize critical threats deteriorates
Solution Approach 1:
The patent segments the stream of security anomalies into distinct priority groups using machine learning-based classification. The anomaly prioritization engine divides all detected anomalies into multiple priority tiers (e.g., critical, high, medium, low) based on their characteristics and risk levels. This segmentation preserves the complete set of monitored threats while organizing them in a way that makes prioritization trivial for SOC staff, as they can focus on higher-priority segments first.
Solution Approach 2:
The system changes the parameter of anomaly presentation by transforming raw anomaly data into prioritized rankings with associated risk scores and confidence levels. Instead of presenting anomalies in arbitrary or chronological order, the engine transforms the data to highlight the most critical threats first based on multiple parameters including anomaly frequency, severity, and contextual risk factors. This parameter transformation maintains complete monitoring coverage while dramatically improving the ease of threat prioritization for analysts.
Data Source
AI summary
A technique includes determining relations among a plurality of entities that are associated with a computer system; and selectively grouping behavior anomalies that are exhibited by the plurality of entities into collections based at least in part on the determined relations among the entities. The technique includes selectively reporting the collections to a security operations center.