AI/ML Threat Mitigation Through Event-Based Response Scripts
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Solution Overview
Problem
The complexity of computer attacks is increasing, and existing technologies struggle to effectively detect and mitigate security events in computing platforms using Artificial Intelligence (AI) and Machine Learning (ML) to provide timely and precise responses.
Innovation Solution
A computer-implemented method utilizing AI/ML to detect security events by monitoring various security-relevant subsystems, obtaining object information, and executing response scripts based on event types, including obtaining artifacts and providing remedial actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional security monitoring methods are used, then system simplicity is maintained, but detection precision and response timeliness deteriorate due to increasing attack complexity
Solution Approach 1:
The security system is segmented into multiple independent modules: AI/ML analysis engine, response script executor, artifact collection system, and remedial action modules. Each module handles specific security tasks independently, allowing the system to achieve high detection precision through specialized AI algorithms while maintaining manageable complexity through modular architecture.
Solution Approach 2:
An AI/ML-based intermediary analysis layer is introduced between traditional security monitoring and response execution. This intermediary processes security events, analyzes patterns, and generates intelligent response decisions, thereby improving detection precision without directly increasing the complexity of core security infrastructure.
2Loss of time
If AI/ML technologies are deployed to improve response timeliness, then detection speed is enhanced, but system complexity increases
Solution Approach 1:
Response scripts and remedial actions are pre-configured and prepared in advance based on common security threats. When security events are detected by the AI/ML system, pre-prepared response protocols are immediately executed, significantly reducing response time while avoiding the complexity of real-time decision-making algorithms for every scenario.
Solution Approach 2:
The system implements automated self-service capabilities where the AI/ML engine autonomously analyzes security events, selects appropriate response scripts, executes remedial actions, and collects artifacts without human intervention. This automation reduces response time while the modular self-service architecture keeps system complexity manageable through standardized interfaces.
3Loss of information
If comprehensive artifact collection is performed to improve analysis accuracy, then information completeness is enhanced, but processing time and system complexity increase
Solution Approach 1:
The system implements partial artifact collection by gathering only the most relevant security artifacts based on the type of security event detected. The AI/ML engine analyzes the security context and selectively collects artifacts necessary for that specific threat type, achieving sufficient information completeness without the overhead of collecting all possible artifacts, thereby reducing processing time.
Data Source
AI summary
A computer-implemented method, computer program product and computing system for: a computer-implemented method is executed on a computing device and includes: obtaining object information concerning one or more initial objects within a computing platform in response to a security event; identifying an event type for the security event; and executing a response script based, at least in part, upon the event type.