Blockchain-Enabled Secure Intelligent Systems for Distributed Environments

Abstract

The convergence of artificial intelligence and blockchain technology offers a compelling paradigm for deploying secure, auditable, and decentralised
intelligent systems in distributed environments where trust between participants cannot be assumed. Existing AI deployment frameworks lack
immutable audit trails, transparent model governance, and cryptographic integrity guarantees — requirements increasingly mandated by regulatory
frameworks including the EU AI Act and GDPR. This study presents ChainMind, a blockchain-enabled secure intelligent system framework
integrating smart contract-governed model lifecycle management, federated learning with on-chain gradient verification, and zero-knowledge proof
(ZKP)-based inference auditing for privacy-preserving accountability. ChainMind was deployed and evaluated across three distributed intelligent
system applications: a decentralised medical AI consortium (6 European hospitals, 284,000 patient records), a cross-border financial fraud detection
network (4 banks, Germany and Italy), and a smart city data marketplace (Stuttgart urban IoT network, 12,400 sensors). ChainMind achieved model
tampering detection accuracy of 99.97%, federated learning convergence within 18.3% fewer rounds than standard FedAvg under adversarial
gradient poisoning, and ZKP inference verification latency of 47.3 ms — compatible with real-time deployment. These results establish ChainMind as
a technically viable and regulatory-compliant framework for blockchain-enabled secure AI in distributed environments.