Current approaches to AI governance rely primarily on reactive mechanisms that assess behavior post-execution. This paradigm falters as frontier-scale systems scale reasoning and consequences at machine speed, creating a paradox where systems remain formally compliant while failing operationally. We shift the fundamental inquiry to: "What systems are admissible before they can be governed?" This paper introduces a Dependency Architecture for Governance Assurance, asserting that assurance is an emergent property of operational conditions. We formalize this relationship as: GA = f(AG, SS, EI, SC, IG) By identifying Admission Governance (AG) as the foundational layer, we distinguish between sovereignty (at admission), assurance (before execution), and accountability (after execution). We demonstrate that governance is not merely risk management, but the sovereign preservation of the conditions that make lawful action possible. Ultimately, without pre-runtime validation, oversight is merely an autopsy of already compromised environments.
Feria Hernández Pablo Octavio (Thu,) studied this question.