Shadow AI Is Already In Your Organisation. Here's How to Deal With It.

Employees aren't waiting for IT to approve an AI tool. They're already using it. ChatGPT for drafting emails, Claude for summarising documents, some random browser extension that claims to boost productivity. By the time your security team hears about it, the data's already been pasted somewhere you don't control.

This is shadow AI, and it's the natural successor to shadow IT. Same problem, higher stakes. The difference is that modern AI tools actively ingest whatever you feed them, often retain it, and may use it to improve their models. That changes the risk calculus considerably.

The instinct in most organisations is to block everything and ask questions later. That approach has a perfect track record of being ignored. Block a tool and watch employees route around it on personal devices. Congratulations, you've now got the same risk with zero visibility.

A more functional approach starts with knowing what's actually in use. You can't manage what you can't see, and most organisations genuinely have no idea how many AI tools are quietly embedded in their workflows. Browser extensions, third-party integrations, SaaS platforms with AI features bolted on. Audit first.

Once you have a picture of what's being used and why, the next step is understanding the actual business need. People don't adopt shadow tools for fun. They adopt them because approved tools are slow, clunky, or simply don't exist. If your employees are using an unapproved AI writing assistant, that's a signal about your approved toolset, not just a compliance failure.

From there, you can start building a rational policy rather than a reactionary one. That means defining what kinds of data can be fed into external AI tools, what tools have been assessed and cleared, and what the process is for requesting evaluation of new ones. The last part matters. If getting a tool approved takes six months and three committee reviews, people will continue ignoring the process.

Training is the piece most organisations get wrong. A one-time security awareness module that nobody reads is not training. People need to understand concretely what the risks are, not in the abstract but in terms of their actual job. What happens if a client contract gets pasted into a consumer AI tool? What does the terms of service actually say about data retention? Make it specific and the message lands better.

Finally, build a feedback loop. Shadow AI adoption tends to spike when official channels feel useless. Regular check-ins between security teams and the people actually doing the work surfaces problems early and, more importantly, builds the kind of trust that makes employees more likely to flag concerns rather than quietly work around policy.

None of this is glamorous. It's mostly process design and honest conversation. But it's considerably more effective than playing whack-a-mole with browser extensions.