Pre-Inference Governance & Authorization-Before-Execution A Legal Interpretation Compendium for Preventive Governance Duties in High-Risk AI Systems

Pre-Inference Governance & Authorization-Before-Execution presents a consolidated legal interpretation compendium addressing preventive governance duties in high-risk artificial intelligence systems. As artificial intelligence systems increasingly operate with autonomous or semi-autonomous execution capabilities, governance approaches based primarily on post-hoc monitoring, output evaluation, or retrospective explainability may become structurally limited in preventing unauthorized automated actions before they occur. This report provides a non-normative legal interpretation framework examining authorization prior to inference and execution as an emerging preventive governance approach. Rather than proposing regulatory requirements or implementation mandates, the document maps how authorization-before-execution mechanisms may relate to established legal concepts including: duty of care foreseeability of harm reasonable preventive safeguards accountability-by-design principles evidence-producing governance controls The compendium integrates interpretative specifications covering legal context analysis, liability mapping, authorization conditions, epistemic maturity requirements, and emerging discussions concerning reasonable governance practices for high-risk AI environments. Pre-Inference Governance is described as a governance posture in which inference and automated execution are treated as conditional capabilities subject to explicit authorization states rather than automatic system rights. A fail-closed operational condition, in which computation does not proceed in the absence of authorization, is interpreted as a preventive governance control rather than system malfunction. This publication is strictly interpretive and informational in nature. It does not constitute legal advice, does not define compliance obligations, and does not prescribe technical implementation methods. Important Notice:Publication of this document grants permission for reading, academic citation, and analytical discussion only. Operational, commercial, or institutional deployment of the described governance concepts requires separate written authorization and a valid license from the rights holder.