Enterprise adoption of AI governance tools has accelerated across regulated industries, yet methods for evaluating whether those tools deliver evidentiary compliance remain underdeveloped. This paper introduces a structural taxonomy of governance failure modes—policy theater and six related families—that arise when governance artifacts are mistaken for governance evidence. We term this practice governance laundering. We define seven evidence-grade governance requirements (R1–R7) and classify failure modes according to the structural property each violates. Building on the structural distinctions introduced in A Taxonomy of AI Governance Approaches: Distinguishing Visibility, Alignment, and Authorization (Zenodo DOI: 10.5281/zenodo.18275969), we formalize the Expanded Anti-Laundering Protocol (EALP): a reproducible, seven-test diagnostic framework designed to determine whether a system produces verifiable, per-decision policy evidence. The paper contributes:• A precise definitional boundary between governance artifacts and governance evidence• A seven-family structural taxonomy of failure modes• A reproducible diagnostic protocol with worked examples and graded outcomes• Architectural susceptibility mapping across eight categories of governance products• Practical implications for procurement, audit, and regulatory evaluation The framework is vendor-neutral and architecture-focused. It does not propose a new governance architecture; rather, it provides an operational detection methodology for evaluating whether existing architectures satisfy evidentiary standards required in regulated environments. This preprint is part of a broader research program on evidence-grade AI governance and authorization-layer compliance.
Edward Meyman (Sat,) studied this question.