A risk officer at a UAE financial institution told us, somewhat sheepishly, that she'd discovered an analyst on her own team pasting client financial summaries into a consumer AI chatbot to help draft a report — not maliciously, just because it was fast and the analyst hadn't really thought of it as a risk. There was no formal policy against it, mostly because nobody had gotten around to writing one, which meant there was also no clear line for the analyst to have known they were crossing. This is an extremely common situation right now, and it's worth being honest about why a flat ban doesn't actually solve it.
Banning it doesn't make it go away
The instinct in a lot of organisations, once this kind of usage is discovered, is to issue a blanket policy prohibiting consumer AI tools entirely. In our experience this mostly succeeds at making the usage less visible rather than less common — staff under real time pressure, with a genuinely useful tool sitting right there in a browser tab, will often keep using it quietly rather than stop, because the policy doesn't address the actual underlying need that drove them to it in the first place. A policy that's routinely and invisibly ignored is, in a real sense, worse than no policy, because leadership now believes a risk is controlled that isn't.
What's actually at risk, specifically
The concern isn't that AI chatbots are inherently dangerous. It's that pasting confidential customer data, proprietary internal documents, or anything covered by data protection obligations into a consumer tool means that data is now processed by a third party, under that third party's terms, potentially used to improve their models, potentially stored in ways that don't meet your regulatory obligations — and the staff member doing this rarely has visibility into any of that, because reading a consumer product's terms of service isn't part of anyone's actual job.
A workable policy looks different from a ban
What we've seen work better is a tiered approach: clearly defined categories of information that can never go into any external AI tool, regardless of how convenient — customer PII, anything covered by DPDL or CBUAE confidentiality obligations, unreleased financial information — alongside a sanctioned, properly governed internal AI tool that staff can use for the genuinely useful things they were reaching for a consumer chatbot to do in the first place, like drafting, summarising, or brainstorming on non-sensitive material. Give people a legitimate, safe way to get the convenience they're seeking, and the incentive to route around the policy mostly disappears.
Why this is a governance failure, not just a training problem
It's tempting to frame this entirely as a staff awareness issue — train people better and the problem goes away. Training helps, but the deeper issue is usually that the organisation hasn't actually decided, formally, what its policy is, which means there's nothing concrete to train people on in the first place. The risk officer in the story above wasn't dealing with a rogue employee. She was dealing with an absence of policy that put an ordinary, otherwise careful employee in a position to create real risk without realising it.
What we recommend to clients
Before doing anything else, get a clear, written, specific policy in place — not a vague 'be careful with AI tools' memo, but an actual list of what categories of data can never leave the organisation's controlled environment, paired with a sanctioned internal alternative for the legitimate use cases driving people to consumer tools in the first place. This is a governance and policy exercise, takes a fraction of the time and budget of any AI deployment project, and closes a risk gap that, left unaddressed, tends to be sitting open at most organisations right now whether anyone's noticed it yet or not.