Pre-Inference Governance: Legitimacy, Maturity, and Authorization in High-Risk AI Systems

Contemporary AI safety and governance frameworks predominantly address risk at the level of system outputs, focusing on hallucinations, bias, erroneous decisions, and harmful actions. Accordingly, most mitigation strategies intervene after inference, relying on monitoring, testing, human-in-the-loop review, auditing, and post-hoc correction. While these measures are necessary, this paper argues that they are structurally insufficient.The most consequential failures in AI systems do not originate at the level of generation or execution, but prior to inference—at the level of informational maturity, semantic legitimacy, and authorization to act. By implicitly assuming that the presence of an input justifies the production of an output, prevailing AI architectures bypass a foundational governance question: whether a system is epistemically and structurally permitted to act under given conditions.This paper introduces Pre-Inference Governance as an architectural framework that reframes AI safety from reactive error control to preventive legitimacy enforcement. Drawing on the A7SEM (Akarkach 7-Stage Emergence Model) and ASiSO (Action-Specific Oversight), it proposes a formal legitimacy layer that evaluates informational maturity, semantic coherence, unresolved contradictions, and action-specific authorization before inference and execution occur.Central mechanisms include a maturity membrane that distinguishes decision-ready from transitional information, semantic routing that prioritizes meaning over statistical fluency, contradiction retention as a safeguard against false certainty, and explicit authorization thresholds for high-risk actions. Within this framework, non-action is treated not as system failure, but as a valid and necessary operational state.The paper argues that hallucinations and related AI failures are not primarily model defects, but governance failures resulting from premature system action. By embedding legitimacy checks upstream, Pre-Inference Governance reduces downstream failure propagation, clarifies institutional responsibility, and scales more efficiently than post-hoc safeguards. Trustworthy AI, the paper concludes, depends less on generating better answers than on formally determining when systems must not answer at all.