Blockchain-Enabled Cybersecurity and AI-Augmented Governance for Trusted Industrial IoT, Healthcare, and Supply Chain Systems

• Ravi K. Menon

  School of Emerging Technologies, University of Edinburgh

  Author

This article examines the intersection of blockchain technologies, Internet of Things (IoT) architectures, and artificial intelligence (AI)-enabled cybersecurity and governance frameworks across three high-stakes domains: industrial IoT (IIoT), healthcare data systems, and supply chain traceability. The need for novel, integrative approaches arises because traditional centralized architectures and conventional security controls struggle to provide provenance, tamper-resistance, and auditable trust in increasingly distributed cyber-physical environments. Drawing on theoretical and empirical work on blockchain platforms for industrial IoT, smart contracts, access control frameworks, and AI-based security analytics, this paper synthesizes a unified conceptual framework and proposes methodological building blocks for practical deployment and evaluation. The article first situates the problem in an historical and technical context, highlighting the nature of threats, regulatory constraints (such as HIPAA and GDPR), and the specific vulnerabilities introduced by scale, heterogeneity, and resource constraints in IoT environments. It then outlines a layered methodology combining permissioned blockchain ledgers, lightweight on-device agents, smart-contract-mediated policy enforcement, and AI-driven anomaly detection and identity & access management (IAM) analytics to provide transactional integrity, provenance, and adaptive response. The results section presents descriptive analyses of how each component contributes to overall resilience—transactional integrity, traceability, access control fidelity, and regulatory auditability—drawing on case exemplars from textile supply chains, electric power materials testing, clinical record protection, and pharma manufacturing governance. The discussion interprets trade-offs—performance, privacy, governance complexity—and addresses limitations, including scalability, consensus cost, and the challenge of aligning AI model explainability with legal accountability. The article concludes with concrete research directions and an agenda for experimental evaluation in production-like contexts, emphasizing interdisciplinary governance, standards alignment, and hybrid on-chain/off-chain architectures to reconcile security, compliance, and operational efficiency.

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