RAGRecon: Supercharging Explainable Threat Intelligence with LLMs and RAG
By Tiago Dinis, Miguel Correia, Roger Tavares
Published on November 10, 2025| Vol. 1, Issue No. 1
Content Source
This is a curated briefing. The original article was published on cs.CL updates on arXiv.org.
Summary
This briefing introduces RAGRecon, a novel system leveraging Large Language Models (LLMs) combined with Retrieval-Augmented Generation (RAG) to enhance cybersecurity threat intelligence. RAGRecon addresses the increasing complexity of cyber threats by integrating real-time information retrieval with domain-specific data to answer cybersecurity-related questions. A key innovation of RAGRecon is its explainability feature: it generates and visually presents knowledge graphs for every response, significantly increasing the transparency and interpretability of the model's reasoning. Experimental evaluations across two datasets and seven different LLMs demonstrated high accuracy, with the best combinations matching reference responses over 91% of the time.
Why It Matters
This development is critically important for AI professionals, particularly those focused on applications in high-stakes environments like cybersecurity. It underscores a powerful trend: the imperative to move beyond mere AI capability towards explainable and trustworthy AI systems. In cybersecurity, where analysts must rapidly understand complex threats and make informed decisions, a \"black box\" AI is often more of a liability than an asset. RAGRecon's approach of generating knowledge graphs directly addresses this by demystifying the LLM's reasoning, allowing human analysts to validate, scrutinize, and ultimately trust the intelligence provided.
Furthermore, this work highlights the increasing sophistication of LLM applications. It's not just about generating text, but about leveraging LLMs as intelligent reasoning engines augmented by RAG to retrieve and synthesize highly relevant, up-to-date information. This combination is crucial for dynamic fields like cybersecurity, where threat landscapes evolve daily. The high accuracy achieved (over 91%) suggests that such integrated, explainable AI systems are not just theoretical but are becoming robust enough for practical deployment, augmenting human capabilities and potentially revolutionizing how organizations anticipate and respond to cyber threats. It sets a precedent for how future AI solutions in critical domains must integrate transparency and interpretability as core features, not just afterthoughts.