AI Filtering Endpoint Detection Response Data for Threat Analysis
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Solution Overview
Problem
Existing security systems struggle to efficiently analyze large volumes of endpoint detect and response (EDR) data in real-time, making it difficult to detect advanced persistent threats (APTs) that use fileless or 'living off the land' exploits.
Innovation Solution
The implementation of an AI and/or machine learning (ML) model that analyzes EDR data to identify uncommon patterns, allowing for real-time or near real-time analysis and filtering out normal activity to focus on abnormal behavior specific to a particular enterprise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional security systems analyze all EDR data manually, then detection accuracy is maintained, but analysis time and resource consumption increase significantly
Solution Approach 1:
An AI/ML model is introduced as an intermediary between raw EDR data and human analysts. The model automatically processes and filters EDR data, identifying and prioritizing suspicious activities while removing normal background noise. This intermediary layer maintains detection accuracy by preserving true positives while dramatically reducing the time required for manual analysis of the remaining suspicious data.
Solution Approach 2:
The patent replaces manual mechanical analysis of EDR data with automated AI/ML-based analysis. The system uses machine learning algorithms to automatically detect patterns, anomalies, and threats in EDR data that would be time-consuming for human analysts to identify manually, thereby reducing analysis time while maintaining or improving detection accuracy.
2Productivity
If AI/ML models analyze all EDR data in real-time, then threat detection speed improves, but computational resources and processing complexity increase
Solution Approach 1:
The AI/ML model performs partial analysis by focusing only on the most critical aspects of EDR data. Rather than exhaustively analyzing every single data point in real-time, the system identifies and prioritizes the most suspicious activities and anomalies, achieving effective threat detection speed improvement while managing computational complexity through selective rather than comprehensive analysis.
3Reliability
If the threshold for identifying uncommon EDR records is lowered, then more security threats are detected, but more false positives are generated
Solution Approach 1:
The system employs feedback mechanisms where the AI/ML model continuously learns from analyst corrections and outcomes. When analysts mark certain EDR records as false positives or true positives, this feedback is used to refine the model's threshold and scoring algorithms, improving threat detection reliability while progressively reducing false positive rates through iterative optimization.
Data Source
AI summary
There is disclosed a system and method of detecting security threats for an enterprise, including: filtering a first set of endpoint metadata records to identify a subset of metadata records, wherein filtering includes identifying endpoint security metadata records that are uncommon in context of the enterprise; and designating the subset of metadata records as indicating a potential security threat including designating the subset of metadata records for human analysis.