Why Banning AI Tools Backfires | AI Governance Guide

Sourced from a live conversation between Cyberleaf and Kairoi, October 14, 2026.

If you went looking today, you would probably find more AI running in your business than anyone has documented. It arrived when someone tried a tool, found it saved them meaningful time, and now half the team is using it a few weeks later. Your team set out to do their jobs faster and better, the intentions were good.

The first instinct when this surfaces is often to shut it down, pick one approved tool, block the rest, and send the memo. But it rarely works, and it tends to make the original problem harder to see.

What happens when you ban AI tools in the workplace?

In most cases we've observed, the usage doesn’t stop when you tell people to stop. We all want to be productive members of the team, and these tools have been helping them this far. Instead, usage becomes quiet and moves somewhere you aren’t looking.

“Once you start banning it, people are going to start using their personal accounts, or using it on their phone. Now all of your data is not contained within the protections you put in place.”

Harrison Welsh, Director of Professional Services, Cyberleaf

The enterprise license you bought, with its admin controls and logging and retention settings, doesn't cover any of that activity, so your data is still going into an AI tool while you lose the ability to see it, log it, or stop it. 

None of this is new. Bob Elfanbaum, founder of Kairoi, recently joined Cyberleaf on a LinkedIn Live to discuss AI and compared it to the arrival of spreadsheets in the 1980s, when people could suddenly run their own analysis without going through IT. The productivity came from individuals deciding for themselves faster than policy could keep up. AI has arrived the same way.

What works better is a culture where no one has to hide what they’re using, but getting there takes four things in a specific order.

How do you set up AI governance in your business?

Start by finding what’s already running, then write the rules, publish what’s allowed, and decide how new tools get added, with each step depending on the one before it.

Step 1: Build an inventory of the AI that's already in your environment

You can’t govern what you don't know about, and most organizations underestimate how much AI is running in their environment because they weren't involved in the purchasing process. It may have be a feature inside software you already own or a trial is something someone in your organization started and then kept. 

Here's a good example: no one wants to take meeting minutes anymore, so there’s a good chance a note-taker is joining your calls and listening to everything discussed. 

“People are literally dragging files into tools. Now you have file systems that are not in your central repositories anymore. You have file systems that are everywhere.”

Andrew Montgomery, CTO and COO, Kairoi

The full picture includes four things:

1. A software list. Depending on how your systems are configured, there are ways to extract what’s installed and in use across the environment, and your IT team or managed provider can do this.
   
   
2. Anything on that list with AI in it. Standalone tools first, then existing software with AI features or embedded third-party agents that someone switched on.
   
   
3. What’s reaching the browser. Plenty of AI usage never installs anything, so protective DNS and similar controls show you where people are going online.
   
   
4. A written record of what you found. The tools you didn’t know about are the ones that matter most.
   

Most teams find more than they expected, which is the point of running the exercise.

Step 2: Write an AI acceptable use policy and have people sign it

A policy tells employees where the lines are, and it gives you something to show a customer or an auditor later. When a prospect asks how your company uses AI, a signed policy spelling out what employees can’t do with customer data is the first piece of evidence you hand over, which is why a line like “use AI responsibly” won't hold up. It leaves everyone to decide for themselves what that means.

Be specific about what can’t go into a tool:

• Personally identifiable information

• Sensitive financial data

• Customer data covered by contract or regulation

• Anything covered by a confidentiality agreement

• Credentials, keys, or access tokens

Then have people sign it, because a signature changes how a policy is read and gives you the artifact to produce when someone asks how you govern AI use.

Step 3: Publish an approved tool list and keep it growing

Once the policy exists, people need to know what they’re allowed to use, so put the approved list somewhere employees will find it and pair it with controls that block what isn’t on it. A list nobody has seen or enforces won't do you any good.

The more common failure is treating that list as a decision you make once.

“One week Claude might do the best PowerPoints, but two months later ChatGPT is doing the best PowerPoints. Each of the tools has different strengths and weaknesses, and it almost encourages the people who really want to be the superstars to bring in their own tools.”

Bob Elfanbaum, Founder and CEO, Kairoi

A frozen, on-time list pushes those requests back into the shadows and costs you the people most likely to find something useful. Having an active list that grows through a known process keeps the conversation where you can see it. The only requirement is that new tools go through the same check as the ones already on it.

Step 4: Check these six things before approving a new AI tool

Adding a tool means handing a vendor access to your data, so scale the review to how sensitive that data is and what the tool will be used for. 

For all six checks, the enterprise version of a tool is a materially different product from the free or personal one. The protections, admin controls, and logging that make a tool safe to approve generally exist only on the enterprise tier, so a tool someone is using on a personal login isn’t really the same tool.

What does good AI governance look like?

The four steps each leave you with something. An inventory of what’s running, a signed acceptable use policy, a published list of approved tools, and a record of how each one was evaluated before it was added. Together they answer the questions most people will ask about AI inside your business, which is what’s running, where the data ends up, and who approved it.

Packaging those into a one-page overview of your AI security program is worth the hour it takes, because it turns the answer into something you hand over rather than something you have to walk a reviewer through.

Larger organizations tend to formalize further. One private equity firm Welsh works with runs an AI steering committee with a written charter, bringing together IT, a director of AI, the CFO, the CEO, and legal counsel.

When governance is in place, you tend to see when someone finds a tool that does the job better because they bring it to you. They do this because there’s a known process AND they know it won’t end in a no by default.

Where should you start?

Start with the inventory, since everything else depends on knowing what’s already running and the other three steps are hard to scope until you have that list. One message to your IT team or managed provider, asking for a software inventory and a list of AI tools and AI-enabled features in the environment, is enough to begin.

As always, we’re here to be a resource for you and your team. If this raised a question about where your own organization stands, book a time with one of our security practitioners.

Want the full conversation this came from?

Cyberleaf and Kairoi hosted a live session on where AI is paying off inside mid-market companies and private equity firms, and what has to be in place before it scales.

Watch the replay · Get the one-page recap