Organization-Specific Cybersecurity Dialogue System with LLMs
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing dialogue systems struggle to generate high-quality responses to cybersecurity-related user utterances that require organization-specific knowledge, as they often rely on general data rather than tailored information relevant to the user's intent and context.
Innovation Solution
A user support pipeline that includes an intent classification model, an entity and metadata extractor, a knowledge retrieval engine, a response ranker, a response type evaluator, and a response generator, which work together to determine user intent, retrieve relevant organization-specific data, and generate responses that incorporate telemetry data for improved relevance and quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If general dialogue systems use general data and rule-based approaches, then device complexity is reduced and ease of operation is improved, but response quality and relevance to organization-specific knowledge deteriorate
Solution Approach 1:
The system segments the dialogue system into multiple functional modules: intent classification model, entity and metadata extractor, knowledge retrieval engine, response ranker, response type evaluator, and response generator. Each module handles a specific aspect of processing user utterances, allowing the system to maintain organizational specificity while managing complexity through modular architecture.
Solution Approach 2:
The system introduces an intermediary knowledge retrieval engine that bridges the gap between general user queries and organization-specific knowledge. This intermediary component retrieves and ranks relevant documents from organizational knowledge bases, enabling the system to access specialized information without requiring the entire system to be redesigned for organization-specific operations.
2Manufacturing precision
If statistical data-driven dialogue systems use large language models, then response quality and contextual understanding are improved, but device complexity and computational resources required worsen
Solution Approach 1:
The system employs a large language model that serves multiple functions within the dialogue system: intent classification, entity extraction, knowledge retrieval guidance, response generation, and contextual understanding. By making the LLM multi-functional, the system reduces the need for separate specialized models for each function, thereby managing overall system complexity while maintaining high response quality.
Solution Approach 2:
The system performs preliminary actions by classifying user intent and extracting entities before generating responses. The intent classification model and entity extractor prepare the input data in advance, allowing the large language model to focus on generating accurate responses rather than processing raw input, thus reducing computational burden and system complexity.
3Loss of information
If the system retrieves and processes organization-specific data and telemetry data, then response relevance to user intent is improved, but processing time and productivity worsen
Solution Approach 1:
The system performs preliminary retrieval and ranking of organization-specific knowledge documents before generating responses. The knowledge retrieval engine pre-processes and ranks relevant documents based on user intent and organizational context, so that when response generation occurs, the most relevant information is already prepared and readily available, reducing overall processing time.
Solution Approach 2:
The system maintains continuous processing of user utterances through a pipeline that operates continuously: intent classification → entity extraction → knowledge retrieval → response generation. This continuous flow of processing actions eliminates idle time between steps and ensures that the system is always ready to handle the next user query efficiently, improving overall productivity.
Data Source
AI summary
An organization-specific cybersecurity dialogue system (“system”) intelligently generates responses to user utterances for cybersecurity by employing documentation and playbook agents for an associated cybersecurity organization. The system extracts an intent and an intent category from a user utterance, extracts relevant entities from the intent and uses them to determine relevant user telemetry data, and matches the intent, intent category, and user telemetry data to documents of the cybersecurity organization. The system then determines whether to generate a response based on matching document, matching playbook agents, or to prompt a user to create a ticket according to logic applied to the intent, intent category, user telemetry data, and matching documents and generates a corresponding response with large language models.