What You Should Never Hand to a Public AI

Your teams are already using AI. Not in six months, not after you sign off on it: right now, in the next tab, to draft a tricky email, summarize a contract or unblock a spreadsheet. That's good news for productivity. It's much less good if nobody has drawn the line between what can go into a public tool and what never should.

The risk isn't theoretical. A piece of information pasted into a public chat leaves your perimeter. You no longer know where it's stored, for how long, or who will be able to see it. For a business leader, the real question isn't whether to ban AI. It's how to draw that line clearly, so your teams keep the reflex without having to think it through every time.

Why a public tool is not a vault

When you type something into a free or public AI assistant, you make an implicit assumption: that the information stays between you and the screen. In most cases, that assumption is wrong.

Depending on the tool and its settings, what you enter may be retained, reviewed by humans to improve the service, or even reused to train future models. This isn't a hidden trap. It's usually written into the terms of use that nobody reads. So the problem isn't malice on the tool's part, it's the gap between what your teams think they're doing and what's actually happening.

From there, the rule follows on its own. As long as a piece of information can leave the building without harm, the public tool is perfect. The moment a leak would have consequences for a client, an employee or the company, you need a different setup.

The three categories you should never paste into a public tool

Third-party personal data. Anything that can identify a client, a prospect, an employee or a patient is covered by legal protection. Pasting a customer list with contact details to sort it, or an HR file to summarize it, exposes you to both a leak and a regulatory breach. These are the most tempting types of data to hand to AI, because they're often what you want to process in bulk, and they're precisely the ones to protect first.

Company secrets. Source code, formulas, contracts under negotiation, price files, sales strategy, an acquisition plan. Anything that would be valuable to a competitor has no place in a tool whose storage you don't control. A good answer from the AI isn't worth losing an advantage you spent years building.

Information under a confidentiality obligation. Anything covered by a non-disclosure agreement, professional confidentiality or a contractual clause. Here the issue isn't just the risk of a leak, it's that you've committed in writing not to share this information. Pasting it into a third-party tool can constitute a breach of that commitment, regardless of whether any incident occurs.

Public, controlled, internal: three levels for three types of data

The common mistake is putting everything in the same box, either banning it all out of fear or allowing it all for convenience. Both cost you. A blanket ban pushes your teams to work around the rule in secret, which is the worst of both worlds. Blanket permission exposes your most valuable data. The right approach sorts the data upstream.

How to set the framework without killing usage

The goal isn't to produce a fifteen-page charter nobody will read. It's to give your teams a simple reflex and a concrete alternative.

Give a one-sentence rule. Something like: if the information identifies a person, gives a competitor an edge or is covered by a confidentiality commitment, it doesn't go into a public tool. A sentence people remember beats a document they forget.

Offer an alternative for sensitive cases. Banning without offering anything doesn't work. If a team has a genuine need to process client data with AI, that's the signal you need a dedicated solution where the data stays under your control. This is exactly the kind of opportunity an audit can identify and frame.

Check the settings of the tools already in use. Many tools offer a professional version that changes everything about data retention and reuse. Switching on the right setting, or moving to the enterprise plan, turns a risky tool into an acceptable one for part of your use cases.

The real risk isn't AI, it's the blind spot

The danger doesn't come from the tool, it comes from the absence of a framework. A company that has drawn the line and provided an alternative for sensitive cases gets the benefit of AI without exposing its data. A company that has said nothing leaves everyone to decide on their own, with no information and no alternative, and quietly piles up a risk it can't see.

Setting this framework doesn't require a big project. It requires a clear decision, a sentence everyone remembers, and an exit route for the uses that deserve one. It's quick to put in place, and far more effective than a ban nobody respects.

Frequently asked questions

Should public AI tools simply be banned? No, and it's often counterproductive. A blanket ban pushes teams to use these tools outside any control. It's better to allow no-stakes uses, set a framework for internal uses and reserve a dedicated solution for sensitive data.

Is a professional version of an AI tool enough for sensitive data? It's enough for a good share of non-critical internal uses, because it brings contractual guarantees on data retention and non-reuse. For data that's truly sensitive or regulated, the safe move is still to keep the data hosted under your control rather than with a third party.

How do you know if a piece of data is sensitive? Ask three questions. Does this information identify a person? Would it give a competitor an edge? Have I committed in writing not to share it? A single yes is enough to keep it out of public tools.

Where do you actually start? With a quick map of the tools already in use and the data flowing through them. This snapshot of the real situation beats any theoretical charter. It's also the starting point of an audit, which turns that observation into a clear rule and an action plan.