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 through 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 (bare activity records) 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. Update (July 2026, v1.1): Released the EALP Evaluation Worksheet (Appendix A) as a companion procurement-ready template, fulfilling the v1.1 commitment; this record now contains two files. Closed the EALP grading rule so that every test outcome maps to a grade. Aligned citations to the AI Governance Taxonomy v1.7 and the Five Tests Standard (5TS) v1.2.0, and added a Provenance deferral note: 5TS adds Provenance (established origin of the inputs grounding a verdict; origin, not truth) as a fifth normative test; the corresponding failure mode and its EALP diagnostic are deferred until input-origin binding is specified, and are identified as the eighth candidate property. Added a terminological note distinguishing governance artifacts (bare activity records) from authorization artifacts (conforming evidence objects). The failure-mode taxonomy, the seven EALP tests, and the susceptibility analysis are unchanged. Update (July 2026, v1.1.1): Consolidates two parallel v1.1 working revisions into a single release. Renamed requirement R6 to Continuity, with content-hash binding as the base requirement and cryptographic-grade binding where external or adversarial verification requires it. Revised the Related Work characterization of adjacent agentic frameworks and compressed the related-instruments discussion connecting the EALP to the authorization artifact gap. Replaced the withdrawn SEC Predictive Data Analytics proposal with the current banking model-risk guidance (SR 26-2 / OCC Bulletin 2026-13 / FIL-15-2026) and added statute citations for New York’s RAISE Act and California’s SB 53 (TFAIA). Added the CC BY 4.0 license block, suggested citation, and archival metadata. The failure-mode taxonomy, the seven EALP tests, and the susceptibility analysis are unchanged.