Journal of Engineering Research and Reports

This research addresses governance and security gaps in autonomous artificial intelligence systems deployed within distributed cloud healthcare environments by proposing an integrated framework combining AI-driven governance, adaptive certification, behavioral authentication, and AI-agent risk monitoring within zero-trust architectures. Using a design science research methodology, three frameworks were developed and validated through simulation experiments, public healthcare datasets, and synthetic conversational AI traces, optimized for home-based resource-constrained setups. The adaptive certification lifecycle framework achieved a 4.7/5.0 compliance score against the NIST AI Risk Management Framework, with 94% latency reduction. The behavioral authentication framework, leveraging Isolation Forest and Mahalanobis distance, attained 94.2% F1-score and 1.2% false positive rate for non-human identity verification. The AI-agent risk monitoring framework, utilizing autoencoders and Q-learning, achieved 93.7% anomaly detection accuracy with 97.6% AUROC. Integrated multi-domain healthcare validation demonstrated 96.1% overall performance, reducing mean time to detection from 72.3 to 0.8 hours. These results highlight that continuous verification and adaptive mechanisms within zero-trust infrastructures substantially enhance security, regulatory compliance, and operational efficiency, providing a novel, quantifiable approach for resilient AI governance in healthcare.