Most enterprise AI governance programs produce policies, not proof. When regulators examine your AI systems, they ask for decision lineage, audit trails, and version control. They find committees and principles. This guide covers the architecture gap between compliance theater and regulatory reality, with a practical 90-day roadmap for building governance that survives examination.
Beyond AI hallucinations, a more dangerous enterprise risk exists: Version Drift. This quiet failure happens when AI systems, though not creating false information, pull and cite outdated policies that have been officially replaced. In regulated fields like banking and healthcare, this isn’t a small glitch—it’s a compliance time bomb with millions in potential penalties.
Traditional safeguards fail because the issue is structural. The answer is the Trust Layer, a governance-focused architecture that employs a dual-index model to separate policies from their meanings. Before searching for relevant information, it first filters out invalid documents—such as superseded, draft, or expired ones—by design, as shown in the diagram below. This article offers the blueprint for building this layer, turning a major vulnerability into a trust-based competitive advantage. By addressing Version Drift, companies can deploy AI not just confidently but with verifiable proof of compliance.
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