AI governance that works is build-time architecture — permissions, audit trails, human gates, and evaluation records — not a policy PDF reviewed
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Know every AI touchpoint (features, agents, shadow tools) and tier by consequence: what it can read, write, and spend determines its controls.
Least-privilege tool access, approval gates on irreversible actions, injection-aware design on external content, and audit logs answering who/what/why for every AI action.
Pre-launch accuracy measurement and post-launch drift monitoring are your documentation-of-diligence — regulators and enterprise buyers increasingly ask for exactly this.
A cross-functional owner group, change review for new AI capabilities, incident playbooks, and vendor/model-update review — lightweight, quarterly, real.
Skipping the discipline this article describes until an incident, audit, or stalled project forces it — every practice above is cheaper adopted early than retrofitted under pressure.
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