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Compliance & Governance

AI governance framework: the plain version

ExplainerLast reviewed Jul 8, 20266 min read

In short

An "AI governance framework" sounds heavy. It isn't. It's a written answer to four questions you can settle in an afternoon. What can we use AI for? How do we catch a wrong answer? What do we log? And who's on the hook? You don't need a 50-page binder. You need clear answers people follow.

The word "framework" makes people picture a compliance department. They don't need one. A framework is the written rulebook you'd want before AI drafts refund emails, screens leads, or summarizes contracts. It keeps the same check happening every time, even when the work gets busy.

The four questions every framework answers

  1. What are we allowed to use AI for?

    Name the approved tools and banned uses. Don't wait for someone to paste the client list into a chatbot before drawing the line.

  2. How do we check it's right?

    Decide where a person checks output before it's used. Don't let a confident error approve a refund, quote policy, or email a customer.

  3. What do we log?

    Keep a record of what AI did and decided. You'll need it for a customer question, an audit, or a bad answer review. If it isn't written down, it didn't happen.

  4. Who's accountable?

    Name the owner for each AI use. Don't let "the AI decided" become the answer when a customer asks who approved it.

A minimal framework you can write in an afternoon

Keep it short. For each AI use, write the approved tool, the data rule, the review step, and the owner. That one page is your framework. You'll grow it later, but a short document people follow beats a thick one nobody opens.

Where the big frameworks fit

Use the big frameworks when you need them. They aren't magic. The two most referenced are the NIST AI Risk Management Framework. It's a voluntary US government framework. The other is ISO/IEC 42001, an international standard for AI management systems. They're detailed versions of the same work: map risk, measure it, manage it, and govern who's responsible. For most small companies, start with the one-pager above. Reach for these when a customer, investor, or regulator asks for a recognized standard.

Build it in. Don't bolt it on.

The cheapest time to add governance is while the AI system is being built. Don't wait. Logging, human review, and access rules should sit inside the design from the start. Retrofitting controls onto a black box you already shipped is always harder and more expensive.

Read next

What is AI governance and compliance — and does your company need it yet? → How to write a simple AI use policy for your team →

Apply this

DPR builds the framework into the system from day one. No drama. Logging, human checks, and clear ownership are part of the build, so "governed" and "audit-ready" aren't a later scramble. It's built for regulated teams.

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