Your team is using AI tools. The question isn't whether they are, it's whether you know about it.
According to IBM, over a third of employees admit to sharing sensitive work information with AI tools without their employer's permission. That number is probably conservative. Most people don't think twice about pasting a customer email into ChatGPT to draft a reply, or uploading a spreadsheet to get help with analysis.
This is shadow AI. And detecting it is harder than you'd expect.
What makes shadow AI different from shadow IT
Shadow IT has been around for years. Employees using Dropbox instead of the approved file storage, or signing up for a project management tool without telling IT. Annoying, but manageable.
Shadow AI is different for three reasons.
First, the tools are free and require no installation. Anyone can open ChatGPT in a browser tab. There's no software to detect, no app to block.
Second, the data exposure is immediate. When someone pastes confidential information into a prompt, that data leaves your network instantly. With traditional shadow IT, data might sit in an unauthorised cloud folder. With shadow AI, it's already been processed by a third-party model.
Third, AI tools are embedded everywhere now. Microsoft Copilot, Salesforce Einstein, Google's Gemini features. Your team might be using AI without even realising it, because it's baked into tools you've already approved.
Why traditional detection methods fail
Most IT security approaches weren't built for this problem.
Network monitoring can flag traffic to known AI domains, but it can't see what's being sent. You might know someone visited claude.ai, but not whether they uploaded your client database.
Endpoint detection looks for installed software. Browser-based AI tools don't install anything, so there's nothing to detect.
Access controls can block certain websites, but blanket bans tend to backfire. Employees find workarounds, or they just use their phones. A Gartner study found that even in organisations with AI bans, half still have unauthorised AI usage happening.
And usage policies? They only work if people follow them. Most employees genuinely don't understand the risk. They're not trying to cause problems. They're trying to work faster.
Practical approaches that actually work
Detection needs to happen where the AI usage happens: in the browser.
Browser-level visibility
The most effective approach is monitoring at the browser level. This lets you see which AI platforms your team accesses and, more importantly, what type of information they're sharing.
Browser-based monitoring can identify which AI tools are being used, from ChatGPT and Claude to Perplexity, Gemini, and newer entrants. It detects when sensitive data patterns appear in prompts, whether that's credit card numbers, personal information, or code snippets. You can see how frequently each team member interacts with AI platforms and track when file uploads are happening.
This isn't about reading every conversation. Good detection systems use pattern matching to flag risky behaviour without logging the actual content of harmless prompts.
Regular usage audits
Even without technical monitoring, you can run periodic audits. Anonymous surveys asking teams about their AI usage often reveal more than you'd expect. People are surprisingly honest when the question is framed around "help us understand what tools would be useful" rather than "tell us what you've been doing wrong."
Data classification first
Before you can detect sensitive data going to AI tools, you need to know what counts as sensitive in your organisation. This sounds obvious, but many companies haven't clearly defined it.
Start with the basics: customer personal information, financial records, proprietary code, strategic documents, legal correspondence. Make sure your team knows these categories exist, even before you implement detection.
Watching the embedded AI
Don't forget about AI features in your existing software stack. Microsoft 365 Copilot, Google Workspace AI, Slack AI, Notion AI. These might be switched on by default. Review your enterprise software settings quarterly to understand what AI features are active.
What to do when you find shadow AI
Detection is only useful if you have a plan for what comes next.
The wrong response is punishment. If you crack down hard on employees who were just trying to do their jobs better, you'll drive the behaviour underground. People will use personal devices, personal accounts, and you'll have even less visibility.
The better response starts with understanding. Why did they turn to AI? What problem were they solving? Often, shadow AI reveals genuine productivity gaps in your approved toolset.
Then assess the actual risk. Not all shadow AI is equally dangerous. Someone using ChatGPT to brainstorm marketing taglines is different from someone uploading client contracts. The response should match the severity.
Finally, provide alternatives. If people need AI to do their jobs well, give them a sanctioned option with appropriate guardrails. Enterprise versions of AI tools, internal AI assistants, or approved workflows that include AI. Make the safe path the easy path.
Building ongoing visibility
One-off detection isn't enough. AI tools evolve constantly, new ones launch every month, and employee behaviour changes.
Effective shadow AI detection requires continuous monitoring rather than periodic audits. You need real-time alerts for high-risk behaviours like file uploads to AI platforms. Dashboard reporting helps you spot trends over time. And integration with your existing security stack means AI risks get managed alongside other data protection concerns.
The goal isn't to become the AI police. It's to understand what's happening so you can make informed decisions about governance.
The visibility-first approach
The companies handling shadow AI best aren't the ones with the strictest bans. They're the ones with the clearest visibility.
When you can see exactly how your team uses AI, everything changes. You can set policies based on evidence rather than guesswork. You can identify which teams need training. You can spot risky patterns before they become incidents. And you can make a genuine business case for enterprise AI tools based on actual usage data.
Shadow AI detection isn't about control. It's about understanding. And understanding is the first step to governance that actually works.