Governance that is verified once cannot certify a system that keeps changing after the certificate is issued. The AI systems now running in production retrain on live data, adapt their own weightings, federate learning across organizational boundaries, and in some architectures begin to monitor and correct themselves. The checklist model of oversight breaks on contact with them. This book collects nine technical studies on what replaces it. They cover drift detection and the mechanics of governance drift, adaptive ensemble consensus under a constrained Markov decision process, reproducible explainability audit trails, nonparametric drift auditing for federated learning, causal methods for privacy-preserving compliance verification, evasion-resilience metrics, self-healing security architectures, and meta-adaptive digital twins that anchor their own audit trail on a distributed ledger. Where a result rests on simulation or synthetic data rather than field deployment, the text says so. That candor is what makes the frameworks here worth testing rather than taking on trust.