Your employees are using AI tools right now. Some of those tools have access to customer data, contracts, or credentials. You may not know which ones, how broadly they're used, or what the terms say about data retention.
This isn't a discipline problem — it's a visibility problem. And the standard response (prohibit everything, wait for compliance) makes it worse, not better.
Why Prohibition Backfires
When organizations issue blanket bans, usage doesn't stop — it moves. Employees switch to personal devices, personal accounts, and workarounds that your monitoring can't reach. The tools that were occasionally visible in browser history or expense reports become fully invisible.
There's also a behavioral side effect worth considering: the people who ask permission before trying something learn to stop asking. The people who never asked keep going. You end up penalizing thoughtful employees while the actual risk continues unchanged.
AI is also increasingly embedded in mainstream software — email clients, document editors, CRMs — so drawing a clean line around "AI tools" is no longer meaningful. You need a policy that works in that reality.
Finding What's Actually in Use
Before you can categorize or control anything, you need to know what's there. A few practical discovery methods:
Ask about tasks, not products. "What takes you the longest right now?" often surfaces AI tool usage more reliably than "are you using any AI tools?" People don't always recognize what counts.
Scan three months of expense reports for recurring charges in the $20–40 range. Many AI tools show up as personal subscriptions that employees expense or quietly absorb.
Check your identity provider logs — Google Workspace and Microsoft 365 admin consoles both show third-party app permissions granted via OAuth. These logs frequently reveal tools that were connected once and forgotten.
Review DNS or firewall logs for outbound traffic to known AI vendor domains.
Look at browser extensions on managed devices — several major AI assistants run as extensions rather than standalone apps and can be easy to miss.
Run an amnesty period before you do any of this. Announce clearly that what happened before a specific cutoff date is not a disciplinary matter — you just need an honest picture. Without that, people hide what they know, and the discovery exercise is compromised before it starts.
Sort What You Find Into Three Buckets
Once you have an inventory, triage it. The framework is straightforward:
- Approved for general use — tools with enterprise terms, SSO, admin visibility, and acceptable data retention policies
- Approved with limits — tools that are fine for non-sensitive work (drafting internal docs, brainstorming) but shouldn't touch customer data or confidential materials
- Not for company data — tools where the data handling terms are unclear or unfavorable, regardless of how useful the product is
The third category isn't a condemnation of the tool — it's a data-protection boundary. Read the actual plan terms, not the marketing page. Retention policies, model training language, and whether your organizational tier includes admin controls all matter. The free tier and the business tier of the same product can have meaningfully different terms.
Making the Approved Path the Default
The speed of your approval process matters more than the content of your rules. If employees have to wait three weeks to get access to a sanctioned tool, they'll use whatever works now.
Set and commit to real timelines: same-day access for tools already on the approved list, and a defined turnaround — one week is reasonable — for new tool requests, with a named owner who handles them.
Write your governance document like a colleague wrote it, not like legal drafted it. A single page covering:
- Which tools are approved and what account type is required
- A concrete list — six items or fewer — of data types that never go into any AI tool: customer contact lists, signed contracts, payroll data, passwords, client NDAs, source code under NDA
- One sentence describing how to request a new tool and who owns that process
Vague rules ("use AI responsibly") generate no consistent behavior. Specific ones do.
Keeping Pace as the Landscape Changes
Set a quarterly calendar reminder to re-run discovery. New tools appear constantly, and your workforce changes over time. Treat shadow AI found during re-scans as process feedback — it means either your approved options don't cover a real need, or communication broke down somewhere. Both are fixable.
The organizations that manage AI use effectively aren't the ones with the strictest policies. They're the ones that made it easier to use approved tools than to route around them. You cannot secure AI use you cannot see — and you will not see it if asking gets punished.
This guide originally appeared on agentpalisade.com. Agent Palisade helps small and mid-sized businesses put AI to work inside the tools they already use — practical automation, internal assistants, and AI security reviews. Book a free 30-minute call.
