Structured Feedback Graphs for Adaptive AI Alert Investigation
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Solution Overview
Problem
Existing Security Operations Centers (SOCs) face challenges in effectively analyzing complex logs across various compute domains, including endpoint, network, cloud, and security data lakes, to identify threats and vulnerabilities, requiring domain expertise and manual intervention, which is inefficient and prone to errors.
Innovation Solution
Implementing an AI agent that utilizes a structured representation, such as a knowledge graph, to adaptively learn from feedback and perform symbolic reasoning, enabling automated investigation of security alerts through a neural-symbolic model and Large Language Models (LLMs) for log comprehension, plan generation, and execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis of security logs is performed by human experts, then accuracy in identifying threats is improved, but productivity and response time deteriorate due to manual intervention requirements
Solution Approach 1:
The AI agent performs self-learning through feedback mechanisms, automatically improving its threat detection accuracy without requiring continuous human retraining. The system processes feedback from human analysts to autonomously update its knowledge base and refine detection algorithms, enabling it to maintain high accuracy while operating independently at scale
Solution Approach 2:
The system implements a feedback loop where human analyst corrections and approvals are processed to continuously improve the AI agent's performance. Feedback from analyst interactions is used to update the knowledge graph and refine detection models, creating a self-improving system that maintains accuracy while increasing productivity
2Productivity
If AI agents are deployed for automated security alert investigation, then productivity and response time are improved, but adaptability to complex and evolving threats deteriorates
Solution Approach 1:
The AI agent's capabilities are dynamically enhanced through continuous learning from feedback interactions. The system adapts its detection strategies and knowledge base in real-time based on analyst feedback, enabling it to handle complex and evolving threats effectively while maintaining high productivity
Solution Approach 2:
Feedback from human analysts is processed to continuously improve the AI agent's adaptability to complex threats. The system learns from correction patterns and emerging threat types to refine its detection algorithms and knowledge graph, ensuring it remains adaptable to new threat vectors while maintaining automated operation
3Adaptability or versatility
If structured representation and knowledge graphs are used for AI learning, then adaptability and reasoning capability are improved, but device complexity increases
Solution Approach 1:
The system segments the knowledge representation into modular components including entities, attributes, and relationships within the knowledge graph. This segmentation allows the complex adaptive learning system to be broken down into manageable units that can be processed and updated independently, reducing overall system complexity while maintaining adaptability
Solution Approach 2:
The knowledge graph structure serves multiple functions simultaneously: it stores domain knowledge, enables reasoning, facilitates learning from feedback, and supports threat detection. This multi-functionality reduces the need for separate complex subsystems, thereby reducing overall device complexity while maintaining high adaptability
Data Source
AI summary
Systems and methods are provided for enabling adaptive modifications to AI agents using human feedback, particularly in the context of investigating cybersecurity alerts. According to one implementation, a method includes a step of receiving feedback from a human analyst related to results of a task performed by an Artificial Intelligence (AI) agent. The method can include a step of converting the feedback into a structured representation having nodes and edges. Furthermore, the method includes a step of updating a knowledge database associated with the AI agent using the structured representation. Next, the method includes a step of utilizing the structured representation and/or knowledge database to improve performance of the AI agent with respect to subsequent tasks.