The Enterprise AI Governance Buyer's Guide

This publication presents a vendor-neutral framework for evaluating enterprise AI governance solutions based on evidentiary rigor rather than marketing claims. It addresses a central challenge in enterprise AI adoption: the inability of most governance approaches to prove that governance controls were functioning correctly at the moment a specific AI decision was made. The guide distinguishes probabilistic governance approaches—such as model-level guardrails, content filtering, and statistical confidence measures—from deterministic, fail-closed governance systems capable of producing reproducible, independently verifiable audit artifacts. It introduces the Four Tests Standard (4TS): Reproducibility, Verifiability, Completeness, and Boundedness, and proposes a baseline qualification gate requiring third-party replay of historical governance decisions. The publication includes practical due-diligence questions for procurement teams, risk officers, General Counsel, auditors, and technical evaluators, along with an illustrative proof-carrying decision artifact and mappings to common regulatory requirements (e.g., audit trails, electronic records, and record retention obligations). The framework is intended to support governance evaluation in regulated and high-stakes environments, with emphasis on auditability, liability allocation, regulatory defensibility, and post-incident verification. While developed by FERZ AI, the evaluation criteria are architecture-agnostic and may be applied to any AI governance solution.