AI-Enhanced Trust Graph Analytics over Distributed Ledgers for Verifiable Hardware and Software Provenance in US National Security Networks

As the United States Department of Defense (DoD) transitions toward Zero-Trust Architecture, the hardware and software supply chain remains a critical vulnerability. Current provenance models rely on centralized, siloed databases that lack the transparency required to counter sophisticated state-sponsored interdiction. This paper proposes a novel framework: AI-Enhanced Trust Graph Analytics over Distributed Ledgers. The architecture utilizes a permissioned Distributed Ledger Technology (DLT) substrate to host an immutable record of component lifecycles, anchored by Hardware Roots of Trust (RoT) through Physically Unclonable Functions (PUFs). By mapping silicon fingerprints to Software Bill of Materials (SBOM), the system constructs a multi-dimensional Trust Graph. We employ Graph Neural Networks (GNNs) to detect structural anomalies indicative of subversion, while Federated Learning enables inter-agency intelligence sharing without compromising operational security. Our findings demonstrate that this integrated approach significantly reduces the time to detect compromised assets in air-gapped and tactical environments, providing a strategic roadmap for an autonomous, self-healing supply chain.