Decentralizing Cybersecurity across Artificial Intelligence, Internet of Things, Blockchain, Machine Learning, and Agentic AI: A Systematic Literature Review

Cybersecurity was built around a perimeter, a trusted central server, and a human analyst who reads the alerts. None of the three holds up well in deployments where billions of constrained devices generate data at the edge, models are trained across organizational borders, ledgers record who did what, and autonomous software agents act at machine speed on behalf of people. This article reviews how decentralization is being used to respond to that shift. We code and synthesize 216 studies, 94 of them drawn from one research programme on federated, blockchain-based, and autonomous defence and 122 from the wider literature on mechanisms, attacks, and standards, across five technology families: the Internet of Things, machine learning, federated and other decentralized learning, blockchain, and agentic artificial intelligence. Four findings stand out. Decentralized learning is the most mature of the strands and now has a credible answer to poisoning and inference threats, though at a cost in complexity that few papers measure. Blockchains contribute auditability and identity more reliably than they contribute detection. Agentic AI is the least settled strand, with evidence of attack running well ahead of evidence of defence. And very few studies combine more than two families, so the integrated architectures the field keeps proposing are mostly untested. We distil three reusable algorithms and a layered reference architecture, and we close with a research agenda built around accountability, evaluation, and post-quantum readiness.