Connect
The era of “move fast and break things” has a new contender: Shadow AI.
For years, IT admins battled shadow IT—employees using Dropbox instead of the file server, or Trello instead of Jira. But in 2026, the stakes have evolved. We aren’t just dealing with unauthorised data storage; we are dealing with unauthorised data training.
Recent data suggests that 81% of the global workforce has used an unapproved AI tool to complete a work task. While the intent is usually benign—employees just want to automate drudgery—the outcome can be catastrophic.
Here is why shadow AI is the defining security challenge of the year, and how you can implement a “Discover, Govern, Enable” framework to manage it.
Why Shadow AI Is Different from Shadow IT
In the old days of shadow IT, if an employee uploaded a sensitive document to an unapproved cloud storage app, the data sat there. It was risky, but it was contained. You could find it, delete it, and close the account.
Shadow AI is fundamentally different. When an employee pastes proprietary code, meeting transcripts, or customer PII into a public large language model (LLM) to “summarize this” or “debug this,” that data doesn’t just sit in a vault. In many cases, it is ingested into the model’s training set.
The data becomes part of the probabilistic math that powers the AI. It creates a scenario where your intellectual property could potentially be regurgitated to a competitor prompting the same model next week.
The “Unlearning” Problem
This leads to what security researchers call the “Unlearning Problem.” You cannot simply hit “delete” on a neural network. Once a model has learned from your data, removing that influence is technically difficult, if not impossible, without retraining the model from scratch.
This permanence turns a simple policy violation into a lasting data leak.
A Framework for Governance: Discover, Govern, Enable
The knee-jerk reaction for many IT leaders is to block everything. However, history tells us that the “Department of No” always loses. If you block ChatGPT, employees will find a browser extension or a mobile app that bypasses your firewall.
Instead, successful IT organizations are shifting to a strategy of enablement.
1. Discover (Turn On the Lights)
You cannot manage what you cannot see. Shadow AI often lives in the browser. Conduct an audit of browser extensions across your fleet. Are users installing “AI Writers” or “Meeting Summarizers” that have read/write access to every webpage they visit? Scan for OAuth tokens to see which third-party apps have been granted access to your corporate Google or Microsoft environments.
2. Govern (Identity First)
Governance doesn’t mean bureaucracy; it means visibility. The most effective way to govern AI is to treat it as an identity problem.
We are moving toward an autonomous workforce where AI agents perform tasks on behalf of humans. These agents are Non-Human Identities (NHIs). They require the same rigor you apply to human employees:
- Least privilege: Does this AI agent really need access to the entire marketing drive, or just one folder?
- Lifecycle management: When the project ends, does the AI’s access play revoked?
3. Enable (The “Yes” Environment)
Shadow AI is a demand signal. Your users are telling you they need help. To stop them from using risky tools, you must provide sanctioned alternatives. Create a curated “AI Toolkit” of vetted, enterprise-grade tools. When you provide a safe lane for innovation, users will stop driving off-road.
Conclusion: The Future Is Autonomous
The goal isn’t to stop AI; it’s to ensure that when your organization hits the gas, you have a steering wheel. By shifting your focus from blocking URLs to managing Identities, you can turn shadow AI from a security liability into a competitive advantage. Download the eBook today!
