AI Use and Governance: 4 Checks You Can Run Yourself
Somewhere in your company right now, someone is probably pasting a client contract, a spreadsheet of financial data, or a chunk of source code into a free AI chatbot to get a faster answer. Not out of malice. Out of the same instinct that's driven every productivity shortcut for the last thirty years: find the tool that makes the work go faster.
The problem isn't that your team is using AI. It's that most companies have no idea which tools, on which accounts, with which data. That gap has a name: shadow AI. It's an easier trap to fall into than the old shadow IT problem ever was, since the barrier to entry is just a free sign-up and a browser tab.
Why this is a different kind of risk
Shadow IT meant an employee spun up an unauthorized app. Shadow AI means an employee may be feeding your company's information straight into a third party's model, depending on that tool's data-retention settings, often without either of you having checked which way that setting is pointed.
A few specific ways this shows up:
Confidential data leaves the building the moment it's pasted into a public AI tool, client records, financial figures, proprietary processes, with no way to pull it back afterward.
Vendor contracts stay vague on who owns the input and the output, which matters a great deal when the input was your data.
AI-generated content goes out under your name with no review step, which is a brand and accuracy risk even before it's a security one.
Leadership finds out after the fact, usually during a customer security questionnaire, a compliance review, or worse, an incident.
None of this requires a malicious actor. It requires an unmanaged default setting and a team trying to move fast.
Four checks you can run yourself, today
You don't need a governance framework to start. You need four honest conversations.
1. Ask each department which AI tools they're actually using. Not the ones IT approved, the ones people have actually opened this week. The gap between those two lists is usually the whole problem in miniature.
2. Check whether those logins are personal or free-tier accounts. A free-tier or personal account almost always means no enterprise data controls, no admin visibility, and terms of service written for an individual, not a business handling client data.
3. Look for a data-retention or training opt-out setting. Most mainstream AI tools have one. Most companies have never checked whether it's turned on. That single toggle is often the difference between "we used this tool" and "we trained someone else's model on our client's information."
4. Write one plain-English rule for what should never be pasted into an AI tool. Not a 20-page policy nobody reads. One sentence your team can actually remember: client data, financial records, and anything under an NDA don't go into a tool you don't control.
If you're finding gaps
You will. Almost every company we've walked through this with does, and it's not a reflection of anyone doing their job badly, adoption just moved faster than anyone's policies did. The difference between companies that get burned by this and companies that don't usually comes down to whether anyone asked these four questions before a customer, an auditor, or an incident asked them first.
We built a short, no-pressure checklist that walks through this in a bit more detail, along with a free self-assessment that shows you where your broader risk posture stands, no sales call required to see your results.
Access the free AI Governance Checklist or take the to see where you stand overall.
