  Machine learning (ML) is crucial in network anomaly detection for proactive
threat hunting, reducing detection and response times significantly. However,
challenges in model training, maintenance, and frequent false positives impact
its acceptance and reliability. Explainable AI (XAI) attempts to mitigate these
issues, allowing cybersecurity teams to assess AI-generated alerts with
confidence, but has seen limited acceptance from incident responders. Large
Language Models (LLMs) present a solution through discerning patterns in
extensive information and adapting to different functional requirements. We
present HuntGPT, a specialized intrusion detection dashboard applying a Random
Forest classifier using the KDD99 dataset, integrating XAI frameworks like SHAP
and Lime for user-friendly and intuitive model interaction, and combined with a
GPT-3.5 Turbo, it delivers threats in an understandable format. The paper
delves into the system's architecture, components, and technical accuracy,
assessed through Certified Information Security Manager (CISM) Practice Exams,
evaluating response quality across six metrics. The results demonstrate that
conversational agents, supported by LLM and integrated with XAI, provide
robust, explainable, and actionable AI solutions in intrusion detection,
enhancing user understanding and interactive experience.
