AI usage policy and governance

It is a short written policy for how your company uses AI, plus a light system that keeps it followed. The policy covers three things: which AI tools are approved, what data can and cannot go into them, and who approves a new tool. The system around it is an approved-tools list everyone can see, a simple request-and-approval flow, a set review cadence, and a record of what was decided. The goal is a rule your team actually follows day to day, plus proof you can show when someone asks how you govern AI, not a thick document that sits in a drawer.

A template is a starting point, but on its own it is a document nobody reads and nothing enforces. This is built for your business and, more importantly, comes with the system that keeps it alive: a named owner, an approved-tools list that gets updated, an approval flow that takes minutes, a review rhythm, and a record you can show. The template answers "what should our policy say." This answers "how do we make sure people actually follow it, and prove that we do." The second part is the hard part, and it is where a template leaves you on your own.

No. This is practical governance, not legal advice, and we are not a law firm. We set up a clear policy, an approved-tools list, an approval flow, and a record that fit how your team works. If you operate in a regulated industry, have specific contractual data terms, or need the policy to satisfy a particular law or standard, involve your own counsel to review the language and confirm it meets your obligations. The setup is designed to make that review easy, because the policy is short and the decisions behind it are documented, not to replace it.

That is the whole point of the system, and it is the part most policies skip. A policy with no owner and no enforcement is theater. So we assign a real owner, keep the approved-tools list somewhere everyone already looks, and make the request flow fast enough that asking is easier than sneaking a tool in. Approvals and reviews are logged, and the review cadence is set on a calendar so the list does not go stale as new AI tools appear. It will not stop every workaround, but it makes the right path the easy one and gives you a record of where the gaps are.

The policy and approved-tools list usually live where your team already works, such as Notion, Google Docs, Slack, Google Workspace, or Microsoft 365. The request-and-approval flow runs through Slack or email with the decisions logged to a shared register. On the identity side, it can lean on your existing SSO, such as Google or Okta, to see who has access to what. Most tools with an API can be wired in. The audit maps your exact stack and picks the setup that fits how your team already operates, so there is no new system to learn.

Usually two to three weeks. The first days go to drafting the policy with you: which tools are approved, what data is off limits, and who owns approvals. Then we stand up the approved-tools list, the request flow, and the record, and test the flow with a few real requests so it is fast and clear before your team relies on it. You are live with a working policy quickly, and the short setup window is what makes it something people follow instead of a document that gets forgotten by month two.

Two parts. Tooling is usually minimal, since the policy and record live in tools you already pay for, such as your docs, Slack, and CRM, so there is rarely a new per-user cost. Implementation is a fixed scope, quoted once the audit maps your team, tools, and how you want approvals to run, so you are pricing a defined setup rather than an open-ended retainer. The audit itself is where the scope and price get set against every other opportunity in your business, so you are not guessing at effort up front.