Adaptive Incident Prioritization Using BM25 Rarity Scoring
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Solution Overview
Problem
Conventional security management systems lack comprehensive computing logic and infrastructure for effective security incident prioritization, leading to inefficiencies in incident ranking and resource allocation, particularly in large Security Operations Centers (SOCs) handling thousands of incidents daily.
Innovation Solution
An adaptive incident prioritization engine employing a modified BM25 algorithm, utilizing local relevance metrics (Term Frequency) and global rarity metrics (Inverse Document Frequency) to rank security incidents based on their significance, with a dual job pipeline configuration for historical analysis and real-time processing, and incorporating a natural language explanation mechanism.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional security management systems are used for incident prioritization, then system simplicity is maintained, but incident ranking effectiveness and resource allocation efficiency deteriorate
Solution Approach 1:
The system segments incident prioritization into multiple independent scoring components including severity score, rarity score, and impact score. Each component is calculated separately using specific algorithms (e.g., severity based on incident classification, rarity using inverse document frequency), then aggregated to form a comprehensive prioritization score. This segmentation enables precise measurement of different incident attributes while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
The system transforms incident prioritization from a subjective manual process to an objective automated process by introducing quantifiable parameters. It calculates severity scores based on incident classification levels, rarity scores using statistical algorithms (inverse document frequency), and impact scores based on affected resources. These parameter changes enable precise, consistent, and automated incident ranking across large volumes of security incidents.
2Productivity
If automated incident prioritization algorithms are implemented, then productivity and response time are improved, but computing resource consumption and system complexity increase
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing baseline metrics during system initialization, such as the frequency distribution of incident types across the organization. This pre-computed data is stored in caches or databases, eliminating the need to recalculate these statistics for every new incident. When prioritizing incidents, the system only needs to compare new incidents against these pre-established baselines, dramatically reducing per-incident computing resource consumption while maintaining high productivity.
3Measurement precision
If comprehensive incident analysis is performed to improve prioritization accuracy, then measurement precision is improved, but processing time and system complexity increase
Solution Approach 1:
The system applies local quality by tailoring the depth and type of analysis to each incident's specific characteristics. For example, critical incidents affecting multiple systems receive more comprehensive analysis including cross-correlation with other incidents and deeper rarity analysis. Less severe incidents receive streamlined processing focusing on basic severity and impact assessment. This differentiated approach maintains high prioritization accuracy for critical incidents while reducing processing time for lower-priority events.
Data Source
AI summary
Methods, systems, and computer storage media for providing security incident prioritization management using an adaptive incident prioritization engine of a security management system are described. The adaptive incident prioritization engine provides security incident prioritization based on an adaptive incident prioritization (AIP) framework built using a ranking algorithm. In particular, the adaptive incident prioritization framework employs a Best Matching 25 (BM25) algorithm and strategically and programmatically adapts the algorithm (e.g., an adaptive incident prioritization model) to rank security incidents based on a local security incident relevance metric (an adaptation of Term Frequency—TF—in BM25) and a global security incident rarity metric (an adaptation of Inverse Document Frequency—IDF—in BM25) associated with security incidents. A prioritization score for a security incident is calculated based on aggregating weighted frequencies of security incident ranking components (e.g., security incident metadata) within a security incident to determine an overall significance of the security incident.