AI Governance Framework: A Starter Template
The short version
AI governance is the set of policies, roles, and controls that let you adopt AI quickly and safely. Good governance is risk-tiered: low-risk uses move fast with light oversight, high-risk uses get real review. The starter template below covers the policy, the roles, a simple risk-tiering model, and the controls that matter — without the bureaucracy that kills adoption.
Most AI governance fails in one of two directions: nonexistent (everyone uses whatever tools they want with company data) or so heavy that nothing ships. The goal is a framework that's proportionate — fast lanes for low-risk use, real guardrails for high-risk use. Here's a practical starting point.
1. The AI use policy
One readable document that answers the questions employees actually have:
- Which AI tools are approved, and for what.
- What data may and may not be put into them (the single most important rule — keep customer data, secrets, and regulated data out of unapproved tools).
- The requirement to review AI output before it's used in anything customer-facing or material.
- Disclosure expectations — when AI involvement must be made clear.
2. Roles & accountability
- An accountable owner for AI governance (often the CTO or a small cross-functional group), not a committee that never meets.
- Clear decision rights for approving new tools and high-risk use cases.
- Named owners for each AI system in production, responsible for its behavior, monitoring, and outcomes.
3. Risk tiering (the heart of it)
Not every AI use deserves the same scrutiny. Tier uses by impact:
- Low risk — internal productivity (drafting, summarizing non-sensitive content). Light-touch: follow the use policy, no approval needed.
- Medium risk — AI that touches customer data or informs internal decisions. Requires data-handling review and a named owner.
- High risk — AI that makes or materially influences decisions about people (hiring, credit, eligibility), or anything customer-facing and automated. Requires formal review: data, model quality, bias/fairness, human oversight, and a rollback plan.
Why tiering matters: a single heavyweight process applied to everything pushes people to route around governance entirely. Tiering keeps the fast lane fast so the guardrails on high-risk use are actually respected.
4. Core controls
- Data protection — rules and technical controls for what data reaches which tools; prefer vendors with no-training-on-your-data terms.
- Human oversight — a human in the loop for any consequential decision; AI assists, it doesn't decide unchecked.
- Model and output quality — evaluation before launch and monitoring after, especially for accuracy and harmful outputs.
- Bias & fairness review for anything affecting people.
- Security of AI systems — prompt-injection, data-leakage, and access controls treated as real threats.
- Vendor management — review AI vendors' security, data handling, and terms before adoption.
- An inventory — a living list of AI systems and tools in use. You can't govern what you can't see.
5. Regulatory awareness
You don't need a law degree, but governance should track the obligations that apply to you — sector rules, data-privacy law, and emerging AI regulation that tends to key off the same risk-tiering logic above. Documenting your high-risk use cases and the oversight around them is the work that pays off when regulation or a customer's security review arrives.
Make it lightweight on purpose
The best AI governance is mostly invisible for low-risk work and genuinely rigorous for high-risk work. Start with the one-page policy, the inventory, and the risk tiers; add controls where the tiering says they're warranted. Governance that's proportionate gets followed; governance that's heavy gets bypassed.
Stand up AI governance that fits
Jimmlr's AI readiness assessment evaluates your governance maturity alongside data, infrastructure, and talent, and gives you a right-sized framework and roadmap.
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