The Authorization Threshold: Defining the Standard for Governance in AI Systems

The resurgence of AI systems capable of initiating actions across institutional and regulatory domains has intensified the need for governance frameworks that extend beyond monitoring, alignment, and post hoc auditability. This paper argues that prevailing approaches to AI governance remain procedural in nature, focusing on capabilities such as observability, control mechanisms, and policy enforcement, rather than on the conditions required for governance to exist. We propose the concept of an authorization threshold, defined as the point at which an action qualifies as governed: when its authorization can be established prior to execution under explicit, evaluable constraints and independently verified. Governance, in this formulation, is not a property of systems but of individual decisions made within a defined context. To formalize this standard, the paper introduces the Canonical Action Frame, specifying the elements required to represent a governable action, and distinguishes between interpretive (judgment-based) systems and authorization (derivation-based) systems. It further develops a foundational distinction between evidence—which explains decisions after the fact—and proof, which constrains the space of permissible decisions before execution. A central contribution is the identification of the constraint representation layer, the transformation of human-readable policy into canonical, machine-evaluable constraints, as the necessary condition for proof-capable governance. Building on this, the paper defines seven conditions of governance validity and five dimensions of governance completeness, including constraint completeness, semantic precision, policy binding, temporal binding, and independent replayability. The paper concludes by specifying the structure of a proof-grade authorization artifact and proposing a testable standard for determining whether an AI-driven action has been governed. The framework provides a basis for evaluating governance claims, distinguishing governance-capable architectures from governance-adjacent systems, and advancing AI governance from interpretive oversight to formal authorization.