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AI governance templatesJun 07, 2026Source: Maetra research

AI governance policy template

Editorial cover for AI governance policy template, showing AI governance research and compliance operations.

An AI governance policy should give teams a clear path for building, buying, deploying, and operating AI systems. It should be practical enough that product and engineering teams can follow it, and specific enough that compliance and audit teams can test it.

The policy should not try to answer every technical detail. It should define the operating rules and point to procedures for risk assessment, approvals, controls, and evidence.

Purpose and scope

State why the policy exists: to manage AI risk while enabling responsible use. Define covered systems, including internally built AI, vendor AI, generative AI tools, model-based product features, workflow automations, and AI agents.

Clarify whether experiments, pilots, and production systems have different requirements.

Roles and accountability

Define business owner, technical owner, compliance reviewer, security reviewer, privacy reviewer, legal reviewer, risk owner, and audit role. Every production AI system should have a named owner accountable for accuracy of the record and ongoing review.

Risk tiers and approvals

Describe the factors used for classification: autonomy, data sensitivity, user impact, external exposure, regulated function, geography, and tool access. Define what each tier requires before launch.

Approvals should be risk-based. Low-risk internal tools should not follow the same process as high-impact agents.

Minimum controls

Set baseline controls: inventory record, approved purpose, data classification, access control, human oversight where required, logging, monitoring, incident path, vendor review, and change management.

For agents, require tool inventory, least privilege, approval for sensitive actions, and prompt injection risk review.

Evidence and review

Require records for classification, approvals, controls, incidents, exceptions, changes, and periodic reviews. Define retention expectations and where evidence should be stored.

A useful policy makes governance visible in daily work. It should help teams know what they can do, what needs review, and what proof must exist afterward.

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