What is shadow AI?

• Your organization faces immediate data leakage risks when users paste sensitive intellectual property (IP) or personally identifiable information (PII) into public AI models.
• AI tool bans rarely work. Security leaders succeed when they act as brokers who enable safe AI usage.
• Your organization must establish visibility as your first step to governance, adopting standard security for AI using tools like Tenable AI Exposure.

Shadow AI emerges when your employees use unauthorized artificial intelligence tools to do their jobs, like when staff members use generative AI applications, like large language models (LLMs) to draft content, generate images, write code, or analyze data without your IT team's approval.

While the shadow AI concept mirrors traditional shadow IT (the use of unapproved software or hardware), shadow AI accelerates your cyber risk. Think of shadow AI as shadow IT on steroids. 

Traditional shadow IT often requires installation or procurement, which creates friction. In contrast, your users can instantly access browser-based, free AI tools. Quick and easy access allows AI adoption to spread virally across your workforce before your security teams can even detect it.

Data flow is a critical differentiator between shadow AI and shadow IT. Where traditional risks usually center on the unapproved software as the vulnerability, shadow AI introduces risk through the data you feed into it.

When employees paste proprietary code, sensitive customer information, or internal strategy documents into public AI models, they surrender control of your data. Submitting that data trains the model to create better outcomes, but it also exposes your organization to data leakage. The AI may learn and later reveal your exact intellectual property in response to outside queries.

As best practice, categorize your AI tools by functionality and risk:

• Sanctioned AI is generally low risk. Think enterprise-managed instances of ChatGPT Enterprise, where you control data retention and privacy settings.
• Shadow AI carries variable to high risk. These unmanaged AI tools let data leave your perimeter. Some are reputable, like the free version of ChatGPT. Others, like DeepSeek, are more obscure and may lack transparency or solid security controls.
• AI agents and autonomous AI tools are a critical risk. Unlike basic chatbots that usually just answer questions, agents and autonomous tools can do tasks without human oversight.

See how to integrate shadow AI security into your exposure management strategy: Security for AI vs. AI for security.

Shadow AI introduces immediate risks to your intellectual property and compliance. While traditional software vulnerabilities usually take time for attackers to exploit, generative AI security failures can happen as soon as an employee hits “enter” on a keyboard.

Your primary concern should be data leakage. 

When your teams paste proprietary code, financial forecasts, or sensitive customer details into a public AI chatbot, they're essentially handing that information over to the model provider. 

Unfortunately, most public terms of service allow these AI vendors to use inputs for model training. That means your trade secrets could end up in a competitor's answer down the road.

If your organization relies on unmanaged models, shadow AI also introduces the risk of harmful or unethical AI outcomes, which can lead to reputational damage or operational failures.

Beyond data loss, the reliance on unmonitored consumer tools risks the quality and reliability of AI outcomes, potentially impacting core business operations.

You also face a new breed of hidden risk: autonomous AI agents. 

As noted by CIO.com, "hidden agents" are more than simple chatbots. They can perform complex tasks without oversight. These AI tools often entirely bypass traditional cybersecurity governance. 

When employees bypass sanctioned AI, they operate outside your visibility. The "Access-Trust Gap" report from 1Password found that 33% of employees admit they do not always follow AI policies. 

These unmonitored data flows also paralyze incident response. Your team cannot mitigate a data leak or satisfy regulatory compliance for a breach they cannot see. 

To effectively govern shadow AI, you must recognize where it hides. Without your knowledge, it can appear across every function of your business. Employee desire to work faster is a common driver.

• Software development engineers often paste proprietary code blocks into LLMs to debug errors or generate documentation. In a widely publicized incident, Samsung employees accidentally leaked sensitive proprietary code by uploading it to a public AI assistant. Developers also often spin up unauthorized cloud instances to host their own open-source AI models, which creates unmanaged infrastructure vulnerabilities.
• Marketing and sales teams often use AI to draft emails or analyze prospects. They might upload spreadsheets containing customer lists or sales revenue figures into public tools to generate summaries and inadvertently expose PII and financial data to the AI model provider.

Even risk-aware departments like legal and HR can make these mistakes. A legal associate might upload a confidential contract to summarize complex terms, or a human resources manager might paste performance reviews to draft feedback. In both cases, highly sensitive internal data enters the public domain.

Leadership within your organization might feel tempted to ban generative AI entirely. It seems like the easier option, but don’t do it. 

Strict AI bans rarely work. They drive AI use underground. Employees who believe AI makes them more productive will find workarounds. They’ll use their personal devices or off-network VPNs, which leave you with no visibility.

Instead of becoming the "Department of No," embrace being an AI "broker,” meaning you enable access to the AI tools your business needs while you establish the necessary guardrails to keep data safe. You validate which tools meet security standards and provide a sanctioned path for usage.

Begin with a clear AI acceptable use policy. Your AI AUP should clarify which data classifications are safe for AI and which are off-limits. 

By clarifying the rules rather than blocking the capability, you build trust and encourage users to stay within a visible, monitored environment.

Need help getting started with AI governance? Read our guide on What is an AI acceptable use policy?

To enable AI innovation while protecting your data, you need a continuous, five-step AI governance framework. Tenable believes that visibility is the foundational layer for this governance. Only by unifying visibility across IT, cloud, identity, OT, AI, and the rest of your attack surface can you effectively reduce risk.

1. Discover your AI exposure

Visibility is your foundational protection against shadow AI risks. You should identify every AI tool in use across your environment, from authorized enterprise instances to unauthorized public apps running in employee browsers. 

While many organizations rely on traditional tools like data loss prevention (DLP), cloud access security broker (CASB), endpoint detection and response (EDR), or cloud-native security controls, these are often insufficient. They don’t have specific context to understand AI models and their unique AI data flows. 

To find the shadow usage you miss, you need automated discovery purpose-built for AI:

AI Aware helps you inventory AI usage by revealing apps on endpoints and networks.

AI-SPM capabilities allow you to inventory AI in build environments to discover shadow AI development before it goes live. To secure this workflow, use Tenable AI Exposure. to validate risk before deployment and continuously monitor for exposures at runtime.

2. Evaluate AI risk

Once you see the AI tools, assess them. Review the terms of service for each application. 

• Does the AI vendor claim ownership of your inputs?
• Do they use your data to train public AI models?
• In which region does the system house your data (for example, the EU or China)?
• Which types of industry compliance does the AI vendor adhere to?

Then, classify each tool as "safe," "restricted," or "prohibited" based on these findings.

Tenable AI Exposure continuously monitors these shadow AI agents to show you exactly how they operate and which AI risks they introduce.

3. Govern with policy

Formalize your decisions into a clear AI governance policy. You should define exactly who can use AI, which AI tools they can use, and what data they can input. 

Your policy should also establish clear accountability metrics for the board and CEO to validate that the AI is safe and delivers expected business value. For a starting point, align your AI governance strategy with the NIST AI Risk Management Framework.

Further, to prove ROI, expand your metrics beyond risk reduction to include business impact, such as tracking the adoption rates of sanctioned AI tools versus unsanctioned ones and estimating the operational efficiency your organization gains by moving users to secure, enterprise-grade AI models.

4. Educate your workforce

Policy means nothing without training. Teach your employees why these AI use guardrails exist. Explain the specific risks of data leakage AI tools pose, so they understand you are protecting the company's secrets, not just enforcing arbitrary rules.

5. Continuously monitor and audit AI use

The AI landscape changes daily. New tools emerge, and safe tools change their terms of service. You must maintain a continuous audit loop to detect unauthorized tools. As ISACA highlights, auditing these unauthorized AI tools is critical to maintaining a compliant enterprise in the face of rapid AI adoption.

Ultimately, generative AI offers immense value, but your organization must securely and thoughtfully adopt it. Don't let shadow AI usage drive your hidden exposures. By establishing clear visibility and governance, you can empower your workforce to innovate without surrendering your data to the public AI domain.

See, secure, and manage your AI ecosystem with Tenable AI Exposure.

• Reach out to Tenable for a security for AI consultation or a demo.
• Watch or join Tenable webinars on AI topics.
• Review the Tenable plug-ins page for AI detection.

As AI emerges and adoption increases, just the amount of questions also grow at an equal pace. We've compiled some of the most frequently asked questions below, aiming to answer those that are most pressing.

What is the difference between shadow AI and shadow IT? 

Shadow IT refers to any unauthorized AI software or hardware. Shadow AI is a specific subset involving artificial intelligence tools. The key difference is in the risk profile. Shadow IT risks your infrastructure, while shadow AI primarily risks the data you feed into it.

Is ChatGPT considered shadow AI? 

If ChatGPT is on your approved AI list, then it is sanctioned; otherwise, it is shadow AI.

How can I detect shadow AI usage? 

You cannot rely on manual surveys to detect shadow AI use. Tenable One Exposure Management unifies data from AI Aware, AI-SPM and Tenable AI Exposure to surface AI exposures from endpoints, network, cloud, and identity.

Get complete visibility into the AI applications and close your AI exposure gap with Tenable One Exposure Management Platform.