The Cost of Shadow AI: How to Manage AI Risk in a Changing Landscape

What Is Shadow AI?

Shadow AI is the use of artificial intelligence tools, including generative AI, by employees without the knowledge, approval, or oversight of IT or security teams.

How is shadow AI different than shadow IT?

Shadow AI is a more specific evolution of shadow IT, the longstanding practice of using unapproved technology at work. The key difference is the nature of the risk. Shadow IT can lead to employees leaking sensitive data or intellectual property (IP) via a public chatbot, or relying on an AI-powered tool for a mission-critical task with no oversight, no audit trail, and no way to reconstruct what happened.

The business cost of unmanaged AI

When an employee pastes a confidential client contract into a public ChatGPT session, your client's data and your reputation are immediately at risk.

IBM’s Cost of a Data Breach Report 2025 reports that shadow AI was a factor in one in five breaches studied, adding an average of $670,000 in additional breach costs compared to organizations with little or no shadow AI. That makes ungoverned AI one of the top three costliest breach factors of the year. Meanwhile, 97% of organizations that experienced an AI-related security incident lacked proper AI access controls, and 63% had no AI governance policy at all.

Why Shadow AI Is Spreading Across Your Organization

Shadow AI is not, for the most part, the result of malicious intent. Employees are using these tools because they want to improve the quality of their work or increase productivity.

GenAI is embedded in tools your employees already use

Many generative AI tools are freely available, browser-based, or added to SaaS products your employees use every day. Microsoft 365, Google Workspace, Notion, Slack, and hundreds of other platforms have embedded AI features into existing subscriptions. Because these capabilities arrive inside already-sanctioned applications, they rarely trigger the scrutiny of a new tool adoption. IT and security teams need policies that govern not just new software, but new AI capabilities embedded within existing tools.

Most organizations still have no AI governance policy

63% of organizations either lack AI governance policies entirely or are still developing them. When no policy around AI use exists, employees make their own decisions.

The result is predictable. By the time a security team implements an AI usage policy, dozens of GenAI tools are already being used in live workflows.

The Cybersecurity Risks of Shadow AI

Shadow AI doesn’t introduce entirely new categories of risk. It accelerates familiar cybersecurity failures, just through a new interface. At its core, AI risk is still about protecting data, systems, and decision integrity.

Data exposure and compliance failures

• Unauthorized data processing: Employees paste sensitive data into public AI tools, where it may be retained, logged, or used for training.
• Regulatory non-compliance: GDPR, the EU AI Act, DORA, and NIS2 impose strict requirements on how data is processed. Unsanctioned AI use creates gaps that are difficult to audit or defend.
• No audit trail: AI-assisted decisions often leave no record of inputs, outputs, or user actions, making incident investigation and accountability difficult.

Attack vectors and model integrity risks

Shadow AI expands the attack surface in ways security teams don’t monitor:

• Expanded attack surface: Unapproved tools, APIs, and plug-ins create unmanaged entry points into corporate data
• Prompt injection: Malicious inputs manipulate models to leak data or perform unintended actions
• Data leakage via outputs: Sensitive information can be exposed through generated responses
• Unvetted models: Unknown training data and behavior introduce risks of bias, hallucination, or hidden data exposure
• AI supply chain risk: Compromised third-party tools or integrations can become a pathway into internal systems

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