Stop your team leaking data into ChatGPT
Your staff paste customer records, source code, and contracts into AI tools to save an hour. The data leaves your control silently, and you cannot recall it.
The leak is quiet.
When people say "data breach", they picture an attacker breaking in. The more common loss is duller. An employee pastes a customer list, a contract, or a block of source code into an AI chat window to save an hour. The work gets done. The data leaves.
There is no alert. No firewall fires. The information has simply been copied out of your control and into a third party's model, logs, or training pipeline. You cannot recall it.
Why AI tools are a new class of channel.
Email, USB, and file-sharing have had controls for years. AI assistants are different for three reasons:
- They feel like a private workspace, so people trust them with material they would never email.
- They accept any format — pasted text, screenshots, uploaded files — which defeats controls that only inspect attachments.
- They multiply fast. A team can adopt a dozen new assistants in a month without IT ever seeing a request.
Cyfriq recognises 40+ AI tools in day-to-day use. The exact number is not the point. The point is that the channel is wide, growing, and mostly invisible to controls built for the last decade.
The problem is not that people use AI. It is that sensitive data uses AI without anyone deciding it should.
What actually leaves.
The leaks that matter are rarely exotic. In practice they cluster into four kinds:
- Customer and employee personal data, pasted in for summarising or drafting.
- Source code and configuration, shared for debugging.
- Contracts, board material, and pricing, pasted in for review.
- Support transcripts and tickets, carrying account details.
Each is a normal task. Each is a normal person trying to move faster. That is exactly why banning AI outright fails — the work still needs doing, so people find a route around the block.
Blocking is not a strategy.
The instinct is to ban the tools. It does not hold. Blanket blocks push usage onto personal devices and personal accounts, where you have no visibility at all. You trade a measurable risk for an unmeasurable one.
A better goal is to let people use AI while stopping specific, sensitive content from leaving. That means inspecting what is about to be shared, not merely which tool is being used.
What good looks like.
Effective AI data protection does three things:
- It sees the AI channel — browser, desktop app, and paste — across the operating systems your staff actually run.
- It reads the content, not just the destination, so it can tell a harmless question from a customer database.
- It redacts or blocks the sensitive part while letting the rest through, so the task still completes.
Redaction matters more than blocking. If an assistant can still answer once the account numbers are masked, the employee gets their answer and you keep your data. Nobody is tempted to route around a control that does not get in their way.
This is where Cyfriq's AI-tool DLP fits. It recognises AI assistants as a channel, inspects what is being pasted or uploaded, and redacts sensitive fields before they leave the device. Email DLP with OCR closes the neighbouring gap — the screenshot of a spreadsheet mailed out, which text-only filters miss. The same content policy covers both routes.
It also connects to the wider picture. A single redacted paste is a coaching moment. The same person doing it forty times in a week is a signal worth scoring — which is where behaviour analytics and insider-risk scoring come in. We cover that in Insider-risk scoring, explained without the jargon.
Where to start.
You do not need a policy committee first. Start by seeing what is already happening:
- Which AI tools are in use, and by whom.
- What categories of data are being pasted into them.
- Which of those would hurt if they left.
Once you can see it, the policy writes itself, because it is grounded in your real traffic rather than a hypothetical.
Read how we approach AI data protection for the full method, or download our guide to AI-channel DLP.
Book a demo and start a 14-day pilot. You will have your first findings on AI-tool usage in 7 days, and you keep them either way.