Bridging AI-automated Governance, Adaptive Certification, Behavioral Authentication, and AI-agent Risk Monitoring in Zero-trust Digital Infrastructures

Abstract

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.

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