From Inference to Governed Consequence: An Axiomatic Framework for Governance in Generative AI Systems

This paper develops an axiomatic framework for the governance of generative AI systems by asking what probabilistic inference cannot legitimately establish by itself once its outputs acquire institutional or operational consequence. It proposes three governance axioms: institutional validity is not a product of inference; generation does not establish epistemic sufficiency; and inference does not create authority. The paper argues that these propositions are analytically distinct and traces their consequences through institutional responsibility, system architecture, and assurance. The resulting derivation is: inferential limit → governance axiom → institutional obligation → architectural invariant → assurance of non-bypassability. The framework explains why requirements such as governed release, explicit evidentiary sufficiency, separation of capability from authority, legitimate non-proceeding states, commit control, provenance, and non-bypassable mediation recur across contemporary approaches to generative and agentic AI governance. The paper does not propose these mechanisms as individually novel. Its contribution is the common derivation that explains why they belong together when probabilistic inference is allowed to acquire institutional consequence.