The AI use policy every business adopting AI needs

A written AI use policy answers three questions for your staff: which AI tools they’re allowed to use, what client or company data can never go into a tool that isn’t approved, and who has to sign off before AI-prepared work reaches a client. Without one, adoption happens anyway — through whatever tool an employee found on their own — and the business has no record of what data went where. A one-page policy, reviewed with the team, closes that gap.

Why a policy has to exist before the tools do

Adoption is already happening in most businesses, whether or not anyone approved it. The National Institute of Standards and Technology’s AI Risk Management Framework, published January 26, 2023, is built around the idea that governance has to be a deliberate, ongoing function — its “Govern” function calls for aligning AI risk management with an organization’s policies and putting accountability structures in place before problems show up, not after. A policy is the plainest form that governance takes for a 20–500 person business: a document that says what’s allowed, in writing, that a new hire can read in ten minutes.

For firms handling client financial, legal, health or personal information, this isn’t optional housekeeping. The FTC’s Standards for Safeguarding Customer Information (16 CFR Part 314) require covered financial institutions — including many tax, accounting and lending-adjacent firms — to oversee the safeguards their service providers use to protect customer data. An AI tool an employee adopted without review is a service provider nobody vetted.

What the policy should cover

1. Which tools are approved, and which aren’t. Name the tools staff can use for work, and say plainly that anything else — a personal consumer AI account, a browser extension, a tool a vendor is piloting — needs approval first. Vague rules (“use good judgment”) don’t hold up when something goes wrong; a named list does.

2. What data can never go into an unapproved tool. Be specific: client names, account numbers, health information, anything under an NDA or engagement letter, anything a client would reasonably expect to stay inside the firm. Staff adopting AI on their own don’t usually intend harm — they usually don’t know a public tool retains what they paste in.

3. Who approves AI-prepared work before it reaches a client. Adoption should never mean an agent’s output goes straight out the door. Define who reviews what, and require that review to be recorded, not just assumed. See human-in-the-loop: why people should approve what AI prepares for what that approval step should look like in practice.

4. How new tools get evaluated and added. Adoption isn’t a single decision; it’s ongoing. Set a simple process — who evaluates a new tool, what questions get asked (see 12 security questions to ask an AI vendor), and how the approved list gets updated and communicated.

5. What happens when the policy is broken. State it plainly, proportionate to the risk: a first conversation for a minor slip, something more serious for repeatedly moving client data into unapproved tools.

Keep it short and specific

A policy that reads like a legal filing won’t get read. One page, plain language, specific examples relevant to the work your people actually do, reviewed at onboarding and at least once a year as tools change. The goal isn’t to cover every hypothetical; it’s to give staff a clear, memorable answer when they’re deciding whether to try a new tool on a Tuesday afternoon.

Where Precision AI OS fits

An AI use policy governs how your people adopt AI day to day; it doesn’t replace the controls a vendor should already have built in. When we build your AI data pipeline and agents, each firm’s data is isolated in the database and encrypted with keys unique to it, and language models can only be called under zero-retention terms — the system enforces what a policy can only ask for. See how we protect your data, or book a discovery call to talk through where your policy and your pipeline should meet.