Shadow AI: the risk isn’t the tool, it’s the data

Somewhere in your organization right now, someone is pasting a customer email into an AI assistant to draft a faster reply. Someone else is uploading a spreadsheet to get a summary before a meeting. Neither of them is trying to create risk. They’re trying to get their work done, and AI helps them do it.

That’s the reality of unapproved AI. It’s also why the-most common response, finding and blocking every unsanctioned AI tool, rarely solves the problem it sets out to solve. The tool is only the channel. The risk is the data that flows through it.

What is shadow AI?

Shadow AI is the use of AI tools, models, or features inside an organization without the knowledge, approval, or oversight of IT and security teams. It includes generative AI chatbots accessed through personal accounts, AI features switched on inside SaaS applications, browser extensions with built-in AI assistants, and AI services that developers connect to through APIs outside standard review.

Like shadow IT before it, shadow AI grows when demand for new capabilities outpaces an organization’s ability to provide sanctioned options. Unlike shadow IT, it doesn’t just store data. It can process, retain, and generate new content from whatever it’s given.

Why chasing unsanctioned AI tools falls short

Blocking unsanctioned AI tools is a reasonable first instinct. As a standalone strategy, though, it has limits:

• The list never ends. New AI tools and features launch constantly, and many arrive as updates to software you’ve already approved.
• Blocking can redirect usage rather than stop it. When a tool is blocked on the corporate network, some people may turn to personal devices, where you have even less visibility.
• It treats every AI interaction the same. Asking an assistant to polish a public blog post and asking it to analyze a customer database carry very different risk, but a tool-level block can’t tell them apart.

Tool controls still have a place. But they answer the question “Which apps are people using?” when the question that determines your exposure is “What data are they putting into them?”

Shadow AI risks start with the data

When you look at shadow AI through a data lens, the real risks come into focus:

• Sensitive data leaves your control. Customer personal information, source code, contracts, financial forecasts, or credentials entered into an unsanctioned tool may be stored, logged, or, depending on the provider’s terms, used to improve a model.
• Compliance obligations follow the data. Regulations such as GDPR and HIPAA apply to personal and health information regardless of which tool processes it. If regulated data enters an unvetted AI service, your obligations still apply.
• Questions become harder to answer after the fact. Without a clear view of what sensitive data you have and where it lives, it’s difficult to assess what may have been shared, with which service, and what that means for your organization.
• Sanctioned AI can surface data too. AI assistants built into approved platforms can draw on any content a user has permission to reach. If access permissions are broader than they should be, sensitive files can appear in AI-generated answers for people who were never meant to see them.

The common thread: each of these risks shrinks when you know where your sensitive data is, how well it’s protected, and who has access to it.

A data-first approach to shadow AI governance

Effective shadow AI governance shifts the focus from the tool to the data. Rather than trying to anticipate every AI service your people might use, you make sure the data behind them is visible, governed, and protected, so it’s safer wherever it ends up.

Here’s what that looks like in practice:

• Know what sensitive data you have and where it lives. Automated discovery and classification across cloud, on-premises, and SaaS repositories gives you the foundation for every other decision. Solutions such as OpenText™ Voltage™ Data Security Platform help you find and classify sensitive data across hybrid environments, so you can see what’s at stake before it reaches an AI tool.
• Prioritize by risk. Not all exposure is equal. Scoring data by sensitivity, location, and access helps you focus on what would cause the most harm if it were shared, and put a business value on that exposure so you can make the case for action.
• Right-size access. Reviewing who can reach sensitive data reduces what any AI assistant, sanctioned or not, can surface on someone’s behalf.
• Give people a secure path forward. Approved AI tools with clear guidance on which data categories can be used make the sanctioned option the easiest one. People turn to shadow AI to be productive. Meeting that need reduces the pull toward workarounds.
• Protect the data itself. Controls that travel with the data, such as de-identification, masking, and tokenization, mean that even if sensitive information ends up somewhere it shouldn’t, it’s far less useful to anyone who shouldn’t have it.

From shadow AI to trusted AI adoption

Shadow AI isn’t going away. New tools will keep reaching your people faster than any approval process can evaluate them. That isn’t a failure of your security program. It’s a signal that AI is delivering value your organization wants.

The organizations that handle shadow AI well will be the ones that stop treating it as a tool problem. When you know your data, control who can reach it, and protect it wherever it goes, you can say yes to AI with more confidence and spend less time catching up with the next unsanctioned app.

Explore how the OpenText data security portfolio helps you discover, govern, and protect sensitive data across your environment, so AI adoption can move forward without leaving your data exposed.

Coming up next in this series: the forgotten files lurking in your repositories, and why shadow AI may be about to bring them back to life.

Mutaharra Mian

Mutaharra Mian is a Product Marketing Manager at OpenText focused on data security, privacy, and risk insights. She currently manages product marketing for OpenText Voltage DSP, helping organizations discover sensitive data, assess risk, and strengthen compliance initiatives. With the growing importance of data governance and protection, Mutaharra is committed to helping customers gain the visibility and intelligence they need to make informed security decisions and reduce organizational risk.