AI-Assisted Cyber Threat Intelligence Fusion Using Large Language Models and Knowledge Graphs
Keywords:
Cyber Threat Intelligence, Large Language Models, Knowledge Graphs, Information Fusion, Socio-Technical Systems, AI GovernanceAbstract
The fusion of heterogeneous cyber threat intelligence (CTI) sources into coherent, actionable knowledge remains a critical challenge for defensive cyber operations. Traditional approaches relying on rule-based correlation and manual analysis cannot scale with the volume, velocity, and variety of threat data. This paper presents a system-level examination of an AI-assisted CTI fusion architecture that integrates large language models (LLMs) and knowledge graphs (KGs) to enable automated extraction, normalization, and reasoning over threat indicators and adversary behaviors. We argue that the complementary strengths of LLMs in natural language understanding and KGs in structured semantic representation can be harnessed through a layered pipeline that includes ingestion, entity and relation extraction, graph construction, and query-based reasoning. However, such an architecture introduces significant structural trade-offs in terms of computational cost, latency, interpretability, robustness to adversarial manipulation, and alignment with existing threat intelligence standards. This paper explores these trade-offs from an interdisciplinary perspective, considering not only technical performance but also governance, policy, and socio-technical sustainability. Through conceptual analysis and illustrative case comparisons across domains such as financial fraud detection and healthcare data integration, we highlight the importance of modular design, human-in-the-loop oversight, and adaptive policy frameworks. We conclude by outlining future research directions that emphasize transparent AI models, federated threat sharing, and ethical safeguards for automated intelligence fusion.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
