Governance Laundering: A Taxonomy of Failure Modes in AI Compliance Architectures

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.