AI Threat Intelligence With Collaborative LLM Agents
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Solution Overview
Problem
Conventional systems lack an automated approach to efficiently process and analyze vast amounts of threat intelligence data, failing to adapt dynamically and provide accurate, timely insights that are customized to an organization's specific context, leading to inefficiencies in cybersecurity threat detection and response.
Innovation Solution
An AI threat engine utilizing collaborative agents with large language models (LLMs) to automate threat intelligence analysis, correlating data across structured and unstructured sources, and dynamically adapting to provide timely and context-specific threat insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated AI processing is implemented, then analysis efficiency and speed improve, but system complexity increases
Solution Approach 1:
The system segments threat intelligence analysis into multiple specialized agents (threat hunting agent, threat analysis agent, report generation agent) that each handle specific tasks. This modular architecture improves productivity by enabling parallel processing while managing complexity through clear separation of concerns and defined agent responsibilities.
Solution Approach 2:
The patent introduces an orchestrator component as an intermediary that coordinates between multiple AI agents and the threat intelligence data sources. This mediator manages the complexity of automated processing by centralizing control logic, routing queries appropriately, and synthesizing results, thereby enabling high productivity without proportionally increasing overall system complexity.
2Measurement precision
If comprehensive threat intelligence data is collected from multiple sources, then analysis accuracy improves, but data processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-collecting and indexing threat intelligence data from multiple sources (dark web, clear web, threat feeds) before actual threat analysis is needed. This advance preparation enables rapid querying and accurate detection when threats are identified, without requiring time-consuming data collection during incident response.
Solution Approach 2:
The patent applies partial action by using selective data retrieval strategies that query only relevant portions of the comprehensive threat intelligence database based on the specific threat context. This approach maintains high detection accuracy by focusing on pertinent data while reducing overall processing time by avoiding unnecessary queries across the entire dataset.
3Adaptability or versatility
If threat intelligence analysis is customized to organizational context, then relevance and effectiveness improve, but analysis complexity increases
Solution Approach 1:
The system applies local quality by customizing threat intelligence analysis to specific organizational contexts through the threat hunting agent, which adapts queries and analysis parameters based on the organization's particular assets, threats, and risk profile. This targeted customization improves relevance and effectiveness while managing complexity by applying customization only where needed rather than across the entire system uniformly.
Data Source
AI summary
A system includes a processing device, operatively coupled to memory, to receive a prompt that is associated with a potential security threat on a computer network. The system applies a first large language model (LLM) to the prompt to generate a first instruction that is associated with a first agent that is to handle the first instruction, and routes the first instruction to the first agent. The first agent applies a second LLM in association with a first data source to obtain a first data that is associated with the potential security threat. The system applies a third LLM at least to the first data, to generate a data output that is associated with the potential security threat on the computer network.