Non-Bypassable Runtime Governance for Autonomous Systems: Toward Continuous Authorization in Physical AI Systems

This paper introduces a conceptual runtime governance architecture for autonomous systems and physical AI environments. The framework explores continuous authorization, runtime validation, governance-aware execution and non-bypassable governance relationships for safety-critical autonomous systems. The proposed architecture separates autonomous decision generation from execution authorization while maintaining continuously relevant governance conditions during runtime operation. Potential application domains include industrial robotics, autonomous vehicles, distributed robotic systems, physical AI ecosystems and safety-critical cyber-physical infrastructure. This work is intentionally conceptual and implementation-independent and aims to support future research regarding runtime governance infrastructures for autonomous systems.