Shadow AI: Staff Already Use It, Pretending Otherwise Is the Real Risk
3 min read
The Samsung case became a textbook example in 2023: semiconductor engineers pasted confidential source code and internal meeting minutes into ChatGPT to get help with their work, and the company — once it found out — banned external chatbots outright. Three years on, the evidence converges on one point: in most organisations, more than half of those who use AI at work do so with unauthorised personal tools, and largely without declaring it. It’s called shadow AI, and the paradox is that companies with an outright ban have more of it, not less: the ban simply shifts usage onto personal phones, where no corporate control can reach.
Why the Ban Doesn’t Work
Someone who pastes a contract into a chatbot doesn’t want to harm the company: they want to finish a tedious task sooner. Generic AI delivers an immediately perceptible time saving, and no internal memo competes with that. A ban produces three effects, all of them negative: usage continues, but invisibly; data still leaves the building, but towards consumer accounts with no guarantees whatsoever; and the company gives up the chance to understand which tasks its staff are actually trying to automate — which is the single most valuable piece of information there is, a free map of inefficient processes.
What Actually Leaves, and Where It Ends Up
The concrete risk needs to be understood without hysteria. On free consumer accounts, the content entered can be used to train the models and is retained under terms you never negotiated. On enterprise accounts, as a rule, data is not used for training and contractual and data-residency guarantees apply. The difference between the two worlds is enormous — and it is exactly the difference that shadow AI erases: the employee using their own account applies the lowest level of guarantees available to the most sensitive data they have to hand.
Governing It in Four Moves
- Offer the alternative before regulating. A policy without an authorised tool is a ban in disguise. What’s needed is corporate access to an AI assistant with adequate guarantees (no training on your data, logging, European data residency) — or, for more serious cases, an internal assistant that works on connected company data, where answers are grounded and traceable.
- A short policy, organised by data category. One page, not thirty: what can be entered into authorised external tools (public information, text to be reworded), and what never can (personal data belonging to customers and colleagues, code, prices, contracts, know-how). People remember three rules, not an entire regulation.
- Provide training — now also a legal obligation. Under the EU AI Act, anyone using AI systems at work must be trained in how to use them (the AI-literacy obligation has been in force since February 2025). Do it properly: half an hour on how to write a prompt without confidential data prevents more incidents than any firewall.
- Measure and update. Traffic towards AI services can be observed at network level; requests for new tools should be welcomed as a signal, not dismissed as a nuisance. Every instance of shadow AI is a request for automation that the company hasn’t yet listened to.
The Useful Reversal
The last move is the most profitable one: treat the shadow-AI map as an automation backlog. If half the procurement office is pasting orders into a chatbot to extract data, that’s a process crying out to be properly automated — with system grounding, controls and traceability. This is often how our operational trials come about: not from a strategic plan, but from what people were already trying to do on the quiet.
Want to turn shadow AI from a risk into a map? Let’s talk: half an hour to set policy and priorities.