Shadow AI: How GRC Teams Can Find Unapproved AI Use

Your company is probably running more AI than your security team knows about. Some of it was approved. Some arrived through a vendor's quiet product update. And some started when an employee pasted a customer list into a free chatbot because it was faster than asking.

Nobody involved is trying to cause a breach. They are trying to hit a deadline. That is what makes shadow AI so hard to manage: it grows out of good intentions, faster than any approval process.

The numbers back this up. In Microsoft and LinkedIn's 2024 Work Trend Index, 78% of AI users said they bring their own AI tools to work. Gartner predicts that by 2030, more than 40% of enterprises will face security or compliance incidents linked to unauthorized shadow AI.

So the real question for CISOs and GRC leaders is not whether shadow AI exists in your organization. It is how much of it you can see. Here is a practical playbook for finding it and bringing it under control.

Where does shadow AI hide?

Most security teams picture shadow AI as an employee using a public chatbot. That is only one of three places it hides, and rarely the hardest to find.

Shadow AI usually shows up in three forms:

• Standalone AI apps: public chatbots, writing assistants, image generators, and note-taking bots that employees sign up for with a work email.

• AI features inside approved tools: a SaaS platform you already use quietly adds an AI assistant, and data starts flowing to a new model provider.

• Homegrown AI: teams connect internal data to AI models through APIs, scripts, or browser extensions, often outside any review.

The second form is the easiest to miss. A vendor you approved years ago may now send your data to a new AI model, and your inventory will not flag it.

Why is shadow AI a risk for GRC teams?

Most employees use unapproved AI to work faster, not to cause harm. The risk is what you cannot see: a tool outside your inventory is also outside your controls, vendor reviews, and audit evidence.

Here are the main risks:

• Sensitive data exposure. Employees paste customer records, source code, and contracts into tools with unknown data handling terms. Once that data leaves your environment, you may not be able to retrieve or delete it.

• Unassessed third parties. Every AI tool is a vendor. If it never went through third-party risk review, you do not know where your data is stored, who can access it, or whether it trains a public model.

• Regulatory exposure. US state AI laws, such as those in effect in California, Texas, and Illinois since January 2026, add obligations for certain AI uses. Unknown AI in hiring, lending, or healthcare decisions can create compliance gaps you cannot defend.

• Audit and contract gaps. Customer contracts and frameworks like SOC 2 and ISO 27001 assume you know which systems handle customer data. Shadow AI quietly breaks that assumption.

Gartner's advice to CIOs is direct: set clear enterprise-wide rules for AI tool use, audit regularly for shadow AI, and add GenAI risk checks to SaaS assessments. That makes shadow AI a GRC program issue, not just a network-blocking problem.

How can GRC teams find shadow AI? A 6-step approach

No single tool shows every AI tool in use. Combine technical discovery with business signals and simple conversations, since each step catches what the others miss.

1. Review network and SaaS discovery data. Use your secure web gateway, CASB, DNS logs, or SaaS management platform to list traffic to known AI domains. Look for new sign-ups using corporate email addresses and single sign-on connections to AI apps.

2. Check expenses and procurement records. Many AI tools start as small monthly subscriptions on a corporate card. Search expense reports and purchase requests for AI vendors, and flag any that never went through security review.

3. Audit browser extensions and integrations. AI browser extensions and OAuth app connections can read emails, documents, and web pages. Review which extensions are installed and which third-party apps have access to your email, file storage, and code repositories.

4. Re-check your existing vendors. Ask your critical SaaS vendors whether they have added AI features, which model providers they use, and whether your data is used for training. Update vendor records where the answers have changed.

5. Ask employees directly. Run a short, non-punitive survey asking which AI tools people use and why. A week of honest answers often beats a month of log analysis.

6. Build and maintain an AI inventory. Record every tool you find, who owns it, what data it touches, and its approval status. Treat the inventory as a living record, not a one-time project.

For framework guidance, the NIST AI Risk Management Framework is a useful, voluntary reference for mapping AI use and its risks.

How can GRC teams find shadow AI? A 6-step approach

Finding shadow AI is only half the job. Blanket bans rarely work, because employees switch to personal devices where you see even less. Instead, make the approved path faster than the unapproved one.

A practical control program includes:

• A clear AI acceptable use policy. Explain which tools are approved, what data can never be entered into AI tools, and how to request a new one.

• A fast intake process. If an AI tool review takes three months, people will skip it. Aim for a risk-tiered process where low-risk tools get approved in days.

• Risk-based reviews. Assess each tool for data handling, model training terms, access controls, and the decisions it influences. Tools that touch customer data or regulated decisions get deeper review.

• Approved alternatives. Offer enterprise versions of popular AI tools with proper data protections, so employees do not need to look elsewhere.

• Ongoing monitoring. Re-run discovery regularly and re-check vendors for new AI features. Shadow AI is not a one-time clean-up.

For most GRC teams, the bottleneck is capacity: every new AI tool means another vendor review and more controls to test.

How can GRC teams find shadow AI? A 6-step approach

Discovery tools can tell you that an AI tool exists. The harder part is everything that follows, and that is where Zania helps. Its AI agents run third-party risk reviews on newly found AI tools, map AI use to frameworks such as ISO 42001 through compliance automation, and continuously test whether your AI policies work in practice.

Every output shows its sources and reasoning, so your team and your auditors can verify it, and Zania does not train on customer data. The result is an AI review backlog that shrinks instead of grows, while your team stays in control of every decision.

Conclusion: You cannot govern what you cannot see

Shadow AI is not a sign of bad employees. It is a sign that AI adoption is moving faster than governance. The answer is not to block AI, but to see it clearly, assess it quickly, and give people a safe, approved path.

Start with the six discovery steps, build a living AI inventory, and set up a review process that keeps pace with new tools. That reduces breach risk and keeps you ahead of new AI rules, while your teams still benefit from AI.

Ready to clear the review backlog? Book a demo with Zania to see how AI agents help you assess and govern AI tools at enterprise scale.