IntelOwl: Integrating a Self-Deployed LLM Chatbot for Threat Intelligence

Project Overview

The goal of this project is to revolutionize how analysts interact with IntelOwl by integrating a cutting-edge, self-deployed LLM-based chatbot. This tool will transform static threat intelligence into a conversational experience, allowing users to query complex data using natural language.

By leveraging modern AI frameworks, the project aims to make threat investigation more intuitive, efficient, and accessible.

Key Objectives

• Conversational Interface Development: Utilize Python, LangChain, and ChainLit (or whatever tool is better to be used) to build a seamless chat interface capable of handling complex natural language queries (e.g., “Which campaigns are associated with this IOC?”).

• IntelOwl Module Integration: Deeply interface the chatbot with IntelOwl’s enrichment modules, allowing the AI to trigger deeper investigations and pull real-time data when required.

• Self-Deployed Architecture: Ensure the LLM infrastructure is self-hosted to maintain data privacy and security—a critical requirement for threat intelligence environments.

• Adaptive Investigative Workflows: Design the system to adapt to evolving user needs, streamlining the communication between the analyst and the platform’s underlying data.

Contributor Profile & Required Soft Skills

The LLM and AI landscape is moving at an incredible pace. To succeed in this project, we are looking for a contributor who demonstrates:

• Proactivity: you propose creative solutions and architectural improvements to overcome technical hurdles.

• Technical Autonomy: You are comfortable diving into dense documentation and managing your development cycles with minimal supervision.

• High Adaptability: You stay updated with the latest open-source AI frameworks and are ready to pivot or adapt your approach as new, more efficient models or tools emerge.