What Are Shadow AI Tools and How Can You Detect Them?

You’ve probably noticed more shadow AI tools appearing in your environment, even if no one admits to using them. I know I have. Here are some of the best practices and tools you can use to manage the risks of these shadow apps.

What are shadow AI tools?

Shadow AI tools are any AI apps that employees use without security approval across the organization. Think ChatGPT or other AI chatbots, Chrome extensions that summarize web pages, AI note-taking apps, AI PDF analyzers, and code-generation assistants.

It’s similar to shadow IT, except the impact is bigger. AI tools often handle internal data (via prompts or file uploads), store those prompts on external servers, or call out to external APIs you know nothing about. One copy-paste into the wrong AI text box and you might leak sensitive data outside your environment.

Why do employees use shadow AI tools?

Most employees use shadow AI tools because the tools help them work faster.

The most common motivators are:

• Productivity: They want to write code faster, summarize docs, or automate grunt work.
• Slow approval cycles: If your app review process takes weeks, people will just install the tool and beg forgiveness later.
• Poor internal alternatives: If IT doesn’t provide an approved AI stack, employees look for their own.
• Curiosity: New AI tools pop up daily. It’s hard to resist at least experimenting. A one-time use can quickly turn into regular use.

What are the risks?

Shadow AI introduces several risks that traditional discovery tools do not catch.

They include:

• Data leakage: Employees paste customer data, code, contracts, SQL queries, or internal docs into an AI tool that stores prompts on external servers. That data is now in someone else’s environment, often with unclear retention.
• Unknown third-party dependencies: Many AI tools, especially browser extensions, are not self-contained. A random AI Chrome extension might secretly be calling ten different domains or APIs you’ve never vetted. Some might even silently log everything.
• Model training exposure: Some AI tools use user inputs to train their models. If employees paste internal data into those tools, your IP may end up in a system you don’t control.
• Inconsistent decision-making: When different teams rely on different AI tools, you lose consistency. Two groups can make different decisions from the same data because their tools behave differently.
• Attack surface expansion: AI plugins, extensions, and agents increase the number of entry points attackers can target. A compromised AI tool can read everything in the user’s browser or IDE.

How to detect shadow AI tools

To get a real handle on shadow AI, you need visibility across networks, devices, and user accounts.

Here are the main detection approaches that many teams have found useful:

Network traffic monitoring

Watch for outbound calls to known AI endpoints. Many generative AI tools will have telltale domains (e.g., calls to api.openai.com, anthropic.com, huggingface.com, midjourney.com, etc.).

Often, you can’t identify the AI tool from the domain alone because some tools make calls to cloud providers or obscure endpoints. But if suddenly a client machine is making lots of POST requests to an IP or domain you’ve never seen until that day, it’s a red flag.

Browser extension audits

Extensions like AI writer, AI summarizer, and AI assistant for Gmail often request full read and write access to any webpage or to your inbox.

We decided to get proactive on this. Using our device management (MDM) and browser enterprise policies, we now inventory the extensions installed across company browsers. If we find an unapproved extension, we block it or talk with the user about why they installed it.

It’s very similar to how you’d police other extension risks, but with an eye on AI keywords like “GPT,” “AI summarizer,” etc.

Endpoint monitoring

Your endpoint detection and response (EDR) or other device monitoring solutions can help spot unauthorized apps.

Watch for unusual processes or binaries, especially ones that access the internet or interact with browsers and IDEs.

Identity and SSO audits

Audit your SSO logs or OAuth consents. Cloud Identity platforms often let you see all third-party app connections. If an app name looks unfamiliar and nobody requested approval for it, you found shadow AI.

Similarly, check browser login artifacts. If users log into AI sites with corporate credentials, that might show up in your identity provider logs.

Surveys and culture checks

I’ve learned that if IT provides approved AI options, employees are more willing to discuss their needs rather than sneaking around. So, as a detection method, consider doing periodic AI tool check-ins.

Ask teams what tools they’ve found useful. Position it as collaborative. For example, “We want to learn what AI is helping you, so maybe we can officially support it!”

5 tools to manage shadow AI risks

There isn’t one single tool that solves shadow AI completely. But there are a bunch of tools that each tackle a slice of the problem, and together they can drastically reduce the risk.

Below are five that I have tried and found useful:

Superblocks

Superblocks provides a governed environment where teams can build internal AI-powered applications without relying on external tools. It’s not that every employee will have to build an app, but one team can create tailor-made AI tools for others, so they don’t feel the need to grab random ones outside.

How it manages shadow AI risks:

• It centralizes permissions, audit logs, user access, and data handling. Every internal AI workflow follows the same policies.
• Instead of using random AI tools, teams can build their own apps, like a chatbot trained on company policies, and keep everything inside your environment.

Nightfall

Nightfall monitors data flowing out of your environment and blocks risky content before it reaches external AI tools.
How it manages shadow AI risks:

• It scans text, documents, code, and other sensitive information across SaaS apps and endpoints. If an employee tries to paste internal data into a public AI model, Nightfall detects it and intervenes.
• It does not rely on a list of approved or unapproved services, which makes it effective even when employees use new or unknown AI tools.

Cyberhaven

Cyberhaven tracks how data moves inside your organization and how users interact with applications, across apps, endpoints, and networks.

How it manages shadow AI risks:

• Instead of just blocking domains or monitoring API calls, Cyberhaven observes user behavior. For example, if someone copies content from an internal doc, then switches to an AI site, Cyberhaven will flag that pattern.
• It catches unknown or newly spinning-up AI tools that your team hasn’t yet cataloged.

Zylo

Zylo uncovers shadow SaaS usage, including AI tools, by integrating with your organization’s single sign-on (SSO), financial, and expense management systems. This data-driven approach helps you compile an inventory of SaaS applications employees are using or purchasing, without relying on direct browser data integration.

How it manages shadow AI risks:

• It reveals the shadow SaaS footprint people sign up for, often on free tiers or bypassing procurement. That way, you discover what tools are being used long before they become a problem.
• Once you know what’s out there, you can decide what stays, what gets reviewed, and what needs to be shut down.

Best for: Organizations dealing with SaaS sprawl, wanting to know exactly how many different tools (AI or not) are in use and which ones need oversight.

Microsoft Purview

Purview plugs into the Microsoft ecosystem and enforces data rules across the apps people already live in, including Outlook, Teams, OneDrive, SharePoint, and now AI tools like Copilot.

How it manages shadow AI risks:

• You can classify and label sensitive information, then set up policies that trigger when someone tries to share that data with an external service.
• If a user tries to export or upload from a sensitive SharePoint or OneDrive folder into an external AI service, Purview can block or flag it depending on your rules.

Netskope

Netskope recognizes AI services at the traffic level and can stop people from pushing files or large text blobs into external models.

Why it matters for shadow AI:

• It can detect unknown AI tools based on traffic behavior, block risky uploads, and allow low-risk prompts.
• Netskope also provides detailed logs that show who used which AI service and when.

How these tools work together

In isolation, each tool solves a different part of the problem. But together they form a practical, layered approach, which is what mature security teams actually end up doing.

For example:

• Zylo gives you visibility into the full landscape of SaaS (including AI) tools in use.
• Superblocks offers a safe, governed alternative for building internal AI apps so people don’t resort to random public tools.
• Nightfall watches for sensitive data leaving the network or being pasted into unknown AI tools.
• Cyberhaven tracks data movement and user behavior across the network, catching suspicious patterns even if the AI tool is unknown.
• Microsoft Purview brings data governance and policy enforcement for companies using Microsoft 365.
• Netskope blocks traffic at the firewall so employees can’t access unapproved extensions or apps.

One more thing. Don’t forget the policy and training. All the tech in the world won’t help if your company’s leadership doesn’t back you up with clear policies. Establish a formal AI usage policy that states what’s allowed and what isn’t, and more importantly, why.

Then communicate to everyone that unapproved AI tools can pose security and compliance risks, and offer the approved alternatives.

Wrapping up

In short, shadow AI tools aren’t going away. The smart move is to get ahead with strong detection and better communication. Start by mapping out your risks, involving your teams, and putting clear guardrails in place. Your proactive approach will help everyone use AI safely.