NeuroGraph: an AI graph-driven neuro-symbolic framework for explainable threat reasoning in advanced manufacturing

The growing complexity of cyber-physical attack surfaces in advanced manufacturing environments has made reliable cyber threat intelligence analysis increasingly challenging. While large language models and retrieval-augmented generation have enhanced cyber threat intelligence capabilities, text-based approaches remain prone to hallucinations and lack structured reasoning over interconnected threats. Graph-based retrieval-augmented generation partially addresses this limitation but often fails to support ontology-consistent multi-hop reasoning and transparent evidence tracing across heterogeneous cybersecurity knowledge. This paper proposes a graph-grounded neuro-symbolic framework that integrates ontology-aware symbolic query generation, knowledge graph retrieval, and neural language generation to enable accurate and explainable threat analysis across information technology and operational technology domains. The proposed framework employs a dual-large language model architecture in which the first model translates natural-language queries into executable Cypher statements for symbolic graph retrieval, while the second generates responses exclusively from the retrieved graph evidence. Experimental evaluation on publicly available cyber threat intelligence benchmarks demonstrates consistent improvements over the published baseline in reasoning accuracy while reducing hallucinations, improving multi-hop reasoning, and increasing robustness against adversarial perturbations. Runtime and explainability analyses further show that the framework maintains interactive inference performance while exposing graph-grounded reasoning artifacts that enable analysts to inspect and verify each stage of the reasoning process. These results demonstrate the potential of graph-grounded neuro-symbolic reasoning to provide scalable, interpretable, and reliable cyber threat intelligence for next-generation Industry 5.0 environments.