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 decentralisedintelligent systems in distributed environments where trust between participants cannot be assumed. Existing AI deployment frameworks lackimmutable audit trails, transparent model governance, and cryptographic integrity guarantees — requirements increasingly mandated by regulatoryframeworks including the EU AI Act and GDPR. This study presents ChainMind, a blockchain-enabled secure intelligent system frameworkintegrating 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 intelligentsystem applications: a decentralised medical AI consortium (6 European hospitals, 284,000 patient records), a cross-border financial fraud detectionnetwork (4 banks, Germany and Italy), and a smart city data marketplace (Stuttgart urban IoT network, 12,400 sensors). ChainMind achieved modeltampering detection accuracy of 99.97%, federated learning convergence within 18.3% fewer rounds than standard FedAvg under adversarialgradient poisoning, and ZKP inference verification latency of 47.3 ms — compatible with real-time deployment. These results establish ChainMind asa technically viable and regulatory-compliant framework for blockchain-enabled secure AI in distributed environments.