Runtime Governance and Adaptive AI Systems: A Framework for Continuous Oversight

This paper examines the temporal misalignment between contemporary artificial intelligence governance frameworks and adaptive, self modifying AI systems. It argues that existing regulatory regimes rely on a stability assumption that presumes functional continuity after deployment. Self modifying systems undermine this assumption by evolving during runtime, generating accountability diffusion, evidentiary instability, and regulatory lag. The article conceptualizes this misalignment as a runtime governance gap and proposes the CARG Framework, a modular architecture integrating runtime oversight obligations, persistent liability, adaptive risk classification, and drift triggered reassessment. The paper situates adaptive AI within a socio technical governance framework and advances a model for aligning regulatory structures with continuous system transformation.