Your employees are already using AI. The only question is where the data goes.

Much of it goes into consumer AI accounts your company can't see: Cyberhaven Labs found that 73.8% of ChatGPT accounts used from workplace networks were personal ones. That is already happening, whatever your policy says. Blocking the sites moves the same use further out of sight; what works is a sanctioned tool as good as the one people would otherwise use, with a data path you know in writing.

The usage is already universal

Independent surveys converge on the same picture: most employees use AI tools at work whether or not IT has approved them. WalkMe's 2025 survey put the figure at 78% of employees using unauthorized AI tools, and UpGuard's 2025 AI risk research found just over 80%.

Cyberhaven Labs, analyzing real enterprise network traffic, found that 73.8% of ChatGPT accounts used from workplace networks were personal, non-corporate accounts. The enterprise had no visibility into what was typed into them and no contractual protection over what happened to that data. A Salesforce survey of over 14,000 workers across 14 countries found that more than half of generative-AI adopters at work use unapproved tools, and many said they saw the need for a company-approved option but went ahead because none existed.

CybSafe and the National Cybersecurity Alliance's 2025 research found that roughly 38% of employees admit to sharing confidential work information with AI tools without their employer's permission. This isn't a rogue minority. It's the median employee, using the same tools they use at home, for the same reason: those tools are fast and good at their job.

Why the risk is real, not theoretical

The reference case is Samsung's, and retellings often exaggerate it, so here is what happened. In March 2023, within weeks of Samsung's semiconductor division lifting an internal ban on ChatGPT, three separate incidents occurred at its Device Solutions unit in Hwaseong. One engineer pasted the source code of a confidential database into ChatGPT and asked it to check for errors, another shared code for optimization, and a third uploaded a recording of an internal meeting and asked ChatGPT to turn it into minutes.

No attacker extracted that data. Employees trying to do their jobs faster handed it over, into a consumer product whose data-handling terms weren't written with a semiconductor manufacturer's IP in mind. Samsung's response, reported by Forbes and others, was to ban generative AI tools company-wide by May 2023 and open disciplinary reviews into the three employees involved.

Regulators have noticed the same gap between consumer AI products and the obligations that apply to data run through them. Italy's Garante ordered OpenAI in March 2023 to stop processing the personal data of Italian users pending an investigation into transparency and legal-basis failures under GDPR. It lifted the suspension in April 2023 once OpenAI made changes, and in December 2024 the same authority fined OpenAI €15 million over how ChatGPT handles personal data. None of that concerned a leak or a breach. It concerned the default terms consumer AI products operate under, which is exactly what an employee pasting a customer list or a contract into ChatGPT is exposed to.

Why blocking it doesn't work

The instinctive response is to block the domain at the firewall. It doesn't hold up. Personal devices, mobile hotspots, home Wi-Fi and DNS-over-HTTPS all sit outside the corporate perimeter a firewall rule controls, so the block doesn't apply to them. A VPN does the same from inside the office, making traffic look as if it comes from a home connection.

Even where the block holds, it only removes one destination. Employees who used ChatGPT move to Claude, Gemini or Copilot on a personal account, because the underlying need (get this written, summarized or debugged faster) hasn't gone away.

So blocking doesn't reduce AI use. It moves the same use further from IT's view, onto a personal phone, a personal laptop, a personal account with no admin console, no audit log, and no way for you to know what was pasted into it. IBM's 2025 Cost of a Data Breach Report found that breaches involving this kind of unsanctioned "shadow AI" cost organizations an average of $670,000 more than the global average cost of a data breach, a difference plausibly explained by exactly that loss of visibility.

The policy that actually works: give people somewhere sanctioned to go

The pattern across the surveys above is consistent: employees don't want to hide their AI use, and a majority say they'd use a sanctioned tool if one existed. Salesforce's respondents said as much directly. Prohibition without an alternative pushes usage somewhere you can't see; an approved replacement that is actually useful gives people no reason to reach for their own phone. A workable policy has these pieces:

• Provide a sanctioned AI tool before you restrict anything. A ban issued without a replacement is a ban on visibility, not on usage.
• Make the sanctioned tool at least as fast and capable as what people already use. If it's slower or worse, the policy will be ignored exactly like the network block was.
• Keep the data path contractually and technically closed. Know, in writing, whether prompts are used for training, how long they're retained, and where they're processed, for the exact plan your organization is actually on, which often differs from the vendor's headline enterprise product.
• Cover personal accounts explicitly. Most of the exposure Cyberhaven found was personal accounts used on company time and company networks; a device policy alone misses all of it.
• Write down what can and can't go into any AI tool, sanctioned or not. Source code, customer PII, contracts, and unreleased financials belong on a short, explicit list, not left to individual judgment under deadline pressure.
• Explain the reasoning, then the rule. The Samsung engineers weren't malicious; they were solving a real problem the fastest way they knew. A rule without the reasoning behind it gets worked around the first time it's inconvenient.
• Revisit the policy as usage shifts. Shadow AI moves fast (new tools, new browser extensions, new defaults in productivity suites), so a policy written once and left alone is out of date within a year.

Where a private, EU-hosted assistant fits

"Provide a sanctioned tool" carries the list because it is the only item that competes with the convenience employees already choose. A dedicated, self-hosted assistant closes the gap the surveys describe. People get the chat-style interface they already reach for, running in the EU on a model your organization chose, on a machine no other customer uses, with prompts that don't go to train someone else's model. That is what a private ChatGPT alternative is for: the same day-to-day usefulness, without the account you can't see into. For cost, performance and governance of running models on owned or dedicated hardware, see the private LLM hosting guide.