A Terminology Translation Layer for AI Governance Architectures

AI governance discourse is increasingly fragmented by inconsistent terminology. Concepts such as admissibility, authority, continuity, legitimacy, runtime governance, auditability, and consequence are frequently invoked across engineering, governance, safety, and regulatory discussions, yet often refer to different architectural layers, runtime semantics, or evidentiary assumptions. This paper argues that the central problem is not ontology itself, but the absence of a disciplined translation between ontological commitments and execution semantics. It proposes a terminology translation framework connecting four levels of governance description: Ontological commitment → Runtime semantic → Implementation primitive → Audit consequence The paper examines why governance architectures frequently operate at cross purposes, why engineering systems already contain implicit ontological assumptions, and why consequence-bearing systems force governance concepts to become executable. A canonical translation framework is presented alongside an illustrative runtime governance example (Aurora-Lens) demonstrating how governance concepts may be translated into operational semantics and reconstructable audit surfaces. Relevant to researchers and practitioners working in AI governance, runtime systems, formal methods, safety, compliance, legal/medical/financial AI, agentic systems, and execution-bound architectures.