The problem
The gap between “we use AI tools” and “we have a policy for AI tools” is wider than most IT managers realize — and it closes fast once something goes wrong. An employee pastes customer data into a public LLM. A vendor quietly adds an AI feature to a tool you already approved. Someone builds an internal workflow on a free-tier AI service with no data processing agreement in place. None of these incidents require sophisticated threat actors. They require only the absence of clear rules.
The underlying issue isn’t that people are reckless. It’s that most AI tools entered the workplace through the path of least resistance — a free trial, a browser extension, a product update — before anyone had a chance to set expectations. By the time an IT manager asks “what are we actually using?”, the answer is already complicated. You can read more about the scope of that problem in The AI tools your employees are already using.
Writing a policy doesn’t fix the underlying sprawl, but it does two things immediately: it establishes accountability, and it gives you a defensible record that governance existed. Both matter if you’re ever in front of a regulator, a client’s security review team, or your own legal counsel explaining what happened.
What good enough looks like
A policy doesn’t have to be long to be effective. A two-page document that employees can actually read and understand will outperform a thirty-page framework that lives in a SharePoint folder no one opens. The goal isn’t comprehensiveness for its own sake — it’s coverage of the decisions that actually expose you.
At minimum, a functional AI use policy addresses five areas:
- Scope — which tools, systems, and people the policy applies to, and whether it covers only company-issued accounts or also personal accounts used for work purposes.
- Roles and ownership — who approves new AI tools, who reviews incidents, and who is accountable when something breaks. Without named roles, enforcement is theoretical.
- Approved and prohibited uses — a short, concrete list of what’s allowed (summarizing internal documents, drafting external communications with review) and what isn’t (inputting PII, confidential client data, or regulated information into non-approved tools).
- Data handling rules — how employees should classify information before deciding whether it can be used as an AI input, and what to do when they’re unsure.
- Incident reporting — a clear, low-friction path for employees to report when something goes wrong or when they suspect a tool is behaving unexpectedly.
That last item is where most first-draft policies fall short. If the reporting path is unclear or intimidating, incidents go unreported until they become problems. Five things your AI use policy is missing goes deeper on the gaps that show up most often in policies we review.
A policy that covers these five areas, written in plain language, reviewed by legal, and communicated to staff is a policy you can stand behind. It won’t be perfect. Iterate from there.
A practical method
Here’s a straightforward sequence for building your first AI use policy without a dedicated compliance team.
1. Inventory what’s already in use.
You can’t govern tools you don’t know about. Before you write a single sentence of policy, spend a week collecting the actual list — approved software with AI features, unsanctioned tools employees are already using, and AI capabilities embedded in existing vendor products. That inventory becomes the foundation for your scope section. Start with How to build an AI tool inventory without enterprise software if you haven’t done this yet.
2. Define your data classification tiers.
AI policy enforcement lives or dies on data classification. If employees don’t have a simple way to categorize information — public, internal, confidential, regulated — they’ll default to their own judgment, which is inconsistent by definition. Two or three tiers with concrete examples is enough to start.
3. Draft the five sections in plain language.
Use the coverage areas above as your section headers. Write each one in the same language you’d use in a staff meeting, not legal boilerplate. A policy your team can read in ten minutes and actually understand is more valuable than one that’s technically complete but practically ignored. How to write an AI use policy that gets followed covers the communication and adoption side of this in detail.
4. Run each approved tool through a basic vendor risk check.
Before you list a tool as approved, you need to know where your data goes, whether the vendor uses it for model training, and what their data retention and deletion practices are. This doesn’t have to be a lengthy process, but it has to happen before the tool is in the policy. AI tool risk assessment: what to ask before you sign gives you the specific questions. If you want a repeatable process for this, How to run a defensible AI vendor risk assessment walks through the full method.
5. Set a review cadence before you publish.
A policy with no review date becomes stale the moment a new tool enters the stack. Add a review date — quarterly is realistic for most SMBs right now given how fast the tool landscape is moving — and name the person responsible for triggering it. That single line turns your policy from a static document into a living control.
Do this week: Pull a list of every SaaS tool your company pays for and flag every one that has launched or announced an AI feature in the past twelve months. That’s your minimum scope for the policy. It takes an afternoon and gives you a concrete starting point.
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If you want a system to track policy ownership, log AI-related incidents, and export evidence of your governance posture for client audits or regulatory reviews, that’s exactly what InfoDefenders’ AI Governance Manager is built to do. It’s not a prerequisite for writing your first policy — but once the policy exists, it’s how you prove the policy is being followed.
Ready to see how it fits your environment? Start a free trial and you’ll have a working governance structure in place before your next vendor review.
