AI Cyberthreat Alert Correlation System
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Solution Overview
Problem
Enterprise organizations face challenges in manually filtering and correlating cyberthreat alerts from disparate vendor tools due to proprietary formats and overlapping detection coverage, leading to difficulties in identifying false positives and duplicates across diverse computing devices and networks.
Innovation Solution
An artificial intelligence (AI) system employing machine learning techniques and APIs to unify and filter cyberthreat alerts from multiple vendor tools, correlating countermeasures to identify overlapping actions, and translating proprietary formats into standardized descriptions to determine duplicate or false alerts, using algorithms like Naïve Bayes Classifier and Support Vector Machine to prioritize remediation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple vendor tools are deployed to detect cyberthreats across diverse computing devices, then detection coverage is improved, but alert correlation complexity increases
Solution Approach 1:
The patent introduces an AI-powered intermediary system that sits between multiple vendor tools and the security operations center. This intermediary automatically correlates alerts from different vendors by analyzing alert patterns, identifying false positives, and mapping alerts to specific vulnerabilities using machine learning algorithms. The system translates proprietary alert formats into a standardized framework, eliminating the need for manual correlation while maintaining comprehensive detection coverage.
2Measurement precision
If manual filtering of vendor tool outputs is attempted, then false positives may be identified, but the process becomes infeasible due to volume and complexity
Solution Approach 1:
The patent replaces the manual mechanical process of filtering and analyzing vendor alerts with an automated AI-based system. The AI engine processes thousands of alerts simultaneously, using machine learning models to identify false positives, correlate related alerts, and prioritize genuine threats. This substitution enables precise false positive identification at scale, making the filtering process feasible rather than infeasible.
3Adaptability or versatility
If proprietary alert formats from different vendors are used, then vendor-specific detection capabilities are maintained, but alert standardization becomes difficult
Solution Approach 1:
The patent creates a universal alert correlation framework that can process and standardize alerts from multiple different vendor formats simultaneously. The AI system learns the proprietary formats of different vendors and automatically translates them into a standardized internal representation, enabling consistent correlation and analysis across all vendors while preserving the ability to handle vendor-specific alert structures and capabilities.
Data Source
AI summary
Artificial Intelligence (“AI”) apparatus and method are provided that correlate and consolidate operation of discrete vendor tools for detecting cyberthreats on a network. An AI engine may filter false positives and eliminate duplicates within cyberthreats detected by multiple vendor tools. The AI engine provides machine learning solutions to complexities associated with translating vendor-specific cyberthreats to known cyberthreats. The AI engine may ingest data generated by the multiple vendor tools. The AI engine may classify hardware devices or software applications scanned by each vendor tool. The AI engine may decommission vendor tools that provide redundant cyberthreat detection. The AI engine may display operational results on a dashboard directing cyberthreat defense teams to corroborated cyberthreats and away from false positives.