LLM Threat Hunting Tree Interface for Context and Skill Tracking

The patent introduces an intermediary layer between the LLM system and the investigation environment. This intermediary consists of a knowledge base that stores recent public context and private contextual information, and a retrieval mechanism that selectively feeds this information back to the LLM. This mediator resolves the contradiction by enabling the LLM to access up-to-date information without requiring direct modification of its training data or architecture.

The system implements feedback loops where the LLM's investigation outputs are fed back into the knowledge base, which then provides updated context to subsequent LLM queries. This feedback mechanism ensures the system continuously learns from recent investigations and maintains accurate context, resolving the limitation of static training data while preserving the ease of LLM-based operation.

The patent implements self-service by having the LLM automatically manage skill selection and execution. Instead of requiring users to manually track and select from extensive skill lists, the system uses the LLM's natural language understanding to automatically identify appropriate skills, retrieve associated code, and execute them. This self-service approach maintains comprehensive system capability while eliminating the complexity of manual skill management.

The patent replaces the mechanical system of manual skill tracking and selection with an automated LLM-based system. The LLM processes natural language descriptions of investigation needs, automatically maps them to appropriate skills, and manages execution without human intervention. This substitution eliminates the cognitive load of tracking numerous skills while preserving full system capability.

The patent extracts only the essential information from large data sets and stores it in a compressed knowledge base format. Instead of feeding entire data sets to the LLM, the system extracts key insights, summaries, and critical data points, then feeds only these condensed representations to the LLM for analysis. This extraction approach maintains information completeness while staying within token limits.

The patent segments large data sets into manageable portions and organizes them in the knowledge base by relevance and importance. The system processes data in segments, extracting insights from each portion and synthesizing them into a condensed representation. This segmentation allows the LLM to work with manageable data quantities while maintaining comprehensive analysis capability through iterative processing.