The real risks of rushing AI adoption
In enterprises across all industries, AI adoption is accelerating fast. “Let’s do AI” has become a board-level rallying cry, motivated by the promise of a competitive edge and the fear of being left behind. Meanwhile, the departments in these large organizations aren’t complaining. Tools like Copilot are already automating workflows, simplifying tasks and boosting productivity.
But between top-down pressure and bottom-up excitement, some organizations are integrating AI before confirming their data is ready—without clear labeling, enforcement or visibility into how tools interact with sensitive information. What risks are we talking about, exactly? Let’s get into specifics.
Data exposure via prompts
Once data is uploaded to a public model like ChatGPT or Notebook LM, an organization loses control over where it goes or how it might be used. Just because a tool lives within a trusted platform like Office 365 doesn’t mean it should have access to every file.
AI responses to user prompts aren’t automatically deleted, either. That means that the AI might later surface sensitive documents from executives, HR or legal and deliver them to the wrong users, internally or externally. Furthermore, even if a document was deleted, the tool’s memory may still retain its content.
Misleading permissions
If Copilot offers content, it must be safe to use, right? Not always. AI tools can retrieve data that users technically have access to—but may be unaware of otherwise. Because Copilot returns that information so easily, it gives users the impression that the data is fair game to use however they want, even if their access might have been due to a mistake or permissions oversight.
Unlabeled or misclassified content
Much of the data in platforms like Office 365 has never been properly labeled or classified. That’s a problem when AI tools are introduced—anything unlabeled might be considered accessible by default. Unfortunately, manual labeling is often done incorrectly or forgotten altogether.
Even when a document starts out harmless, new sensitive content can be added over time without a manual label update to reflect the change. AI-generated content like meeting summaries and collaborative documents often lack labels altogether unless a DLP system intervenes.
Shadow AI and third-party exposure
Employees are now exploring tools on their own, often without approval or oversight. A recent study found that 50% of workers use unapproved AI tools, and most wouldn’t stop even if banned. These apps may store data in unknown regions or fail to support corporate data deletion requests. Nearly 21% of ChatGPT’s prompt traffic goes to the free tier, where inputs can be retained and used for training. A single prompt from a personal email account could send sensitive company data to a model with no way to get it back.