AI Alert Enrichment for Context-Aware Cyber Incident Analysis
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Solution Overview
Problem
Existing cyber security systems struggle to differentiate between alerts and events triggered by cybersecurity attacks and those caused by harmless, unusual network or employee behavior, leading to inefficiencies in investigations.
Innovation Solution
An AI-based cyber security system utilizing a clustering foundational AI model to analyze and output contextual information about the role and function of entities within a network, combined with electronic communication analysis and aggregation components to enrich alerts and events, providing enhanced contextual understanding for faster and more accurate threat assessments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional cyber security systems monitor network activity and generate alerts, then security coverage is improved, but the volume of false positive alerts increases making investigations inefficient
Solution Approach 1:
The system performs preliminary actions by automatically enriching alerts with contextual information about entities (users, devices, networks) before human investigation. This includes gathering organizational role data, communication patterns, and relationship graphs in advance, so that when analysts receive alerts, the contextual framework is already in place to enable rapid differentiation between benign and malicious activity.
Solution Approach 2:
The system introduces an intermediary layer of automated contextual enrichment between the alert generation and human analysis stages. This intermediary component processes alerts through multiple data sources and enrichment pipelines, transforming raw alerts into enriched intelligence packages that include entity roles, communication contexts, and risk assessments, thereby filtering false positives before they reach analysts.
2Measurement precision
If more contextual information is gathered about entities, then alert accuracy is improved, but the time required for analysis increases
Solution Approach 1:
The system performs preliminary gathering and processing of contextual information about entities continuously in the background. Organizational data, communication patterns, and entity relationship graphs are pre-computed and stored, allowing the system to quickly retrieve relevant context when alerts occur without requiring real-time analysis of all available data.
Solution Approach 2:
The system applies local quality by retrieving and processing only the specific contextual information relevant to each alert's entities rather than analyzing all available organizational data. The enrichment process targets only the necessary attributes (user roles, device functions, communication patterns) related to the alerting entities, making the analysis time-efficient while maintaining high accuracy.
Data Source
AI summary
A clustering foundational AI model analyzes for, collects data about, and then outputs the role and/or function of an entity in a network and/or in an organization. The clustering foundational AI model clusters data together so that similar roles and/or functions can be readily identified to supply additional contextual information about the entity involved in the alert and/or event, and then outputs the role and/or function for the entity associated with the alert and/or event to assist in an investigation. The clustering foundational AI model adds the additional contextual information about the role and/or function of the entity upon receiving the alert and/or event. A UI receives the additional contextual information about the role and/or function of the entity in the network and/or organization and then presents both the alert and/or event and the additional contextual information that allows a user to gain contextual information about the alert and/or event.