Pre-Inference Governance Beyond Medicine: Authorization Architectures for High-Risk Autonomous Systems

This paper extends Pre-Inference Governance beyond medical AI and establishes it as a universal authorization architecture for high-risk autonomous systems. Building on the Akarkach 7-Stage Emergence Model (A7SEM) and Action-Specific Oversight (ASiSO), it argues that the most severe AI failures will arise not from inaccuracy, but from unauthorized action. Through cross-domain analysis of autonomous mobility, critical infrastructure, algorithmic finance, and security-adjacent systems, the paper demonstrates how epistemic gating, action-specific authorization, and veto architectures enable liability attribution, institutional accountability, and insurability. The framework reframes AI governance from sector-specific compliance toward a unified pre-action legitimacy infrastructure.