What is AI governance? A practical guide

What AI governance is

AI governance is the set of rules, roles and habits that make sure an organization uses AI responsibly and accountably. It decides what is allowed, who is accountable and how risk is kept in check.

Good governance answers a handful of practical questions for every AI use. What are we allowed to do with AI? Who owns each use, and who signs off on the risky ones? What information can go into which tools? Who checks the output, and what happens when something goes wrong? It is less about grand principles and more about concrete rules that let a company adopt AI with confidence.

Why it matters now

Adoption is outrunning oversight. AI tools spread through companies faster than the rules get written, and that gap is where the risk lives. At the same time, clients now ask how you govern AI in security reviews and questionnaires. Governance is how you answer with confidence instead of from memory.

What a program includes

A working program is a set of connected pieces:

• An acceptable use policy that sets the rules everyone follows.
• An inventory of where AI is used across the business.
• Risk assessments for high-impact uses.
• Vendor checks for the third-party tools you adopt.
• Human review for consequential outputs, with a clear escalation path.
• A process for when something goes wrong.
• Clear owners, and a regular review so the program stays current.

How to start

You don’t need everything on day one. Start with three things:

• A written policy. The Free Policy Starter gives you an editable draft.
• A picture of where AI is used. The Free Shadow AI Check shows what you know and what you don’t.
• A simple log of AI decisions and risks, so each approval has an owner and a date.

Those three cover most of what a client first asks for, and they are the foundation the rest builds on. Add vendor checks and risk assessments as your use of AI grows.

A worked example: Meridian Creative’s first month

Meridian Creative is a fictional 42-person agency. Clients had started asking about AI in security reviews, and staff were already using several tools. Here is what its operations lead put in place in the first month, starting from the three steps above.

Week one was the inventory. The operations lead asked each team which AI tools they used and for what. Six uses came back, from a concept drafting assistant to a call recorder one account manager ran on a personal account. Each went on the list with an owner and a note about the information it touches.

Week two was decisions. The operations director reviewed each request. Social post suggestions for the agency’s own channels were approved. The concept drafting assistant was approved with conditions: internal drafts only, with a creative director checking claims and rights before anything reaches a client. The personal call recorder was declined until client consent and retention are agreed.

Week three was the policy. The agency adapted a starting draft, settled the decisions it had marked as open, and adopted it on October 8 with the operations director as owner and a review set for December.

Week four was follow-up. Three risks stayed open, each with an owner and a date, and the rest of the agency was asked to acknowledge the policy.

The test came in week five. A client’s security review asked whether Meridian’s staff could use AI with the client’s information. The operations lead answered from the records: client confidential information is prohibited in tools that aren’t approved, each approved tool has written conditions, and a person reviews AI output before it reaches a client. The answer took an hour, not a week, and every claim pointed to a record.

Nothing here is finished, and that is the point. Meridian can now answer a client’s questions with what it actually does, show who owns each use, and say what is still open and when it will close. That is a working program. It grows from here: impact assessments when a use affects people, and a regular review so the records stay current.

Common mistakes

• Starting with a framework instead of the tools already in use. Begin with what people actually do.
• A policy nobody owns. Without a named owner, questions go unanswered and the policy goes stale.
• Decisions without dates. Tools and terms change, so every decision needs a date to look at it again.
• Records in someone’s inbox. When a client asks, anyone on the team should be able to find the answer.

Using your AI assistant

An AI assistant can help you see where you stand, if it works only from what you tell it. Use your company’s approved AI account and treat the result as a starting list, not an assessment.

Using only the notes below about how our team uses AI today, sort what we already have into: rules, owners, a list of tools, review of outputs, vendor checks, and handling problems. For each, say in place, partial or missing, and quote the note that supports it. Then suggest the three most useful next steps. Do not assume anything the notes don't say.

Notes:
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Common questions

Related guides

• How to write an AI acceptable use policy
• AI risk assessment template: what to assess
• How to answer the AI section of a security questionnaire

Extrasphere Media LLC. Content for general informational purposes, not legal advice and not a substitute for qualified counsel. Independent and not affiliated with ISO, the IEC, NIST, or the European Union.