Shadow AI

What is Shadow AI?

Shadow AI is the use of artificial intelligence tools and services, such as generative AI assistants, by employees without the knowledge or approval of IT and security. A newer form of shadow IT, it carries specific risks: staff may paste confidential data into unvetted AI tools, training data and outputs are hard to govern, and AI spend grows invisibly across the organization.

Why shadow AI is a distinct risk

• Employees may submit sensitive or regulated data to public AI tools.
• Data handling, retention, and training policies of those tools are often unknown.
• AI subscriptions proliferate quickly and quietly, inflating spend.
• Outputs can introduce accuracy, IP, and compliance exposure.
• Governance frameworks rarely cover AI yet, leaving a policy gap.

Examples and use cases

An analyst pastes a confidential customer list into a public AI chatbot to summarize it. The data has now left the company's control, and IT has no record it happened. Shadow AI combines the visibility gap of shadow IT with a sharper data-leakage edge. Managing it starts with discovering which AI tools are in use, then setting policy and monitoring usage, the same pattern as shadow IT plus AI-specific data controls.

Related concepts

• The rise of shadow AI: understanding the risks and mitigating the threats
• AI spend management in the age of Claude and Copilot
• Corma for security teams

FAQ

What is the difference between shadow AI and shadow IT?

Shadow AI is a subset of shadow IT focused on unsanctioned AI tools. It adds a sharper data-leakage risk, since employees may feed sensitive data into public models.

Why is shadow AI dangerous?

Because confidential data can leave the company through unvetted AI tools, with unknown retention and training practices, and no IT record of it.

How do you manage shadow AI?

Discover which AI tools are in use, set clear usage and data policies, monitor adoption and spend, and bring approved tools under governance.

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