Multi-LLM Debate for Fast, Trade-Off-Aware Incident Response
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Solution Overview
Problem
Existing cybersecurity incident response methods rely heavily on human analysts who are unable to respond quickly enough and may make mistakes under pressure, leading to potential worsening of the situation, and existing technologies fail to effectively weigh the pros and cons of different response actions.
Innovation Solution
Utilizing multiple Large Language Models (LLMs) to debate and determine optimal cybersecurity incident responses, incorporating their outputs into a recommendation that weighs trade-offs, and refining these recommendations based on user feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human analysts are used for incident response, then they can make judgment decisions, but they cannot respond quickly enough and may make mistakes under pressure
Solution Approach 1:
The patent replaces the mechanical system of human analysts with an automated LLM-based system. Multiple LLMs are deployed to automatically analyze security incidents, generate response recommendations, and execute playbooks without human intervention, thereby eliminating response delays and human errors while maintaining or improving decision accuracy through the collaborative reasoning of multiple models.
Solution Approach 2:
The patent changes the operational parameters of the incident response system by transitioning from sequential human analysis to parallel automated LLM processing. Multiple LLMs process incidents simultaneously, with their outputs aggregated and refined through iterative debate, fundamentally changing the speed and scalability parameters of the system while preserving analytical depth.
2Reliability
If multiple LLMs are used to debate and determine solutions, then the system can weigh trade-offs of different resolutions, but the device complexity increases
Solution Approach 1:
The patent segments the incident response system into distinct functional components: multiple specialized LLMs for different aspects of analysis, a coordination layer for managing debates and aggregating outputs, and a playbook execution module. This segmentation allows complex multi-LLM interactions to be managed through modular, independent units with well-defined interfaces, reducing overall system complexity.
Solution Approach 2:
The patent introduces intermediary components that mediate between multiple LLMs and the final output. These intermediaries include aggregation layers that synthesize diverse LLM perspectives, validation modules that verify recommendation quality, and playbook selection mechanisms that translate debates into actionable responses. The intermediaries simplify the complexity by providing structured interfaces between complex LLM interactions and simple output generation.
Data Source
AI summary
A computer-implemented method (CIM), according to one embodiment, includes, tuning a plurality of Large Language Models (LLMs) to debate one another to determine solutions for incidents, and causing data associated with a first incident to be input into the LLMs. The method further includes incorporating solutions output by the LLMs into a recommendation for resolving the first incident. The recommendation weighs trade-offs of different possible resolutions for solving the first incident. The method further includes outputting the recommendation to a user interface of a user device. A computer program product (CPP), according to another embodiment, includes a set of one or more computer-readable storage media, and program instructions, collectively stored in the set of one or more storage media, for causing a processor set to perform the foregoing method.