Shadow AI Is Turning Into a Billion-Dollar Security Problem

The AI revolution is creating a new cybersecurity headache: companies increasingly do not know exactly which AI tools are being used inside their organizations, what data they can access, or what autonomous agents are doing on their behalf.

Shadow AI refers to AI tools and applications used by employees without the knowledge, approval or oversight of their organization’s IT or security teams. It can be as simple as an employee pasting sensitive company information into an unapproved chatbot, or as complex as connecting an AI application to corporate systems without a formal security review.

Now, the market for controlling that risk is expanding rapidly.

According to a new report from MarketsandMarkets, the global shadow AI risk and governance market is projected to grow from $1.39 billion in 2026 to $8.64 billion by 2032, representing a compound annual growth rate of 35.6%.

The bigger shift may be happening inside those numbers. While AI data protection and security controls are expected to account for the largest share of the market, AI access and agent governance is forecast to be the fastest-growing segment, with a 47.2% CAGR.

That reflects a change in the problem enterprises are facing. Shadow AI once largely meant employees quietly using unauthorized chatbots or AI applications. As AI agents gain access to corporate systems, databases and business processes, organizations increasingly need to control not only which tools employees can use, but what AI agents are authorized to access and do.

The emerging market is already drawing companies from both the cybersecurity and AI governance sectors. MarketsandMarkets lists Israeli companies Check Point and Noma Security among the market participants, alongside Microsoft, Palo Alto Networks, Cisco, Zscaler, IBM and ServiceNow.

The report says organizations are increasingly looking for technologies that can discover AI applications, protect sensitive data, enforce access policies, assess risk and maintain records showing how AI systems are being monitored and controlled.

The result is a shift from simply asking “Who is using AI?” to much more consequential questions: What can that AI access? What can it do? And can the organization prove that it is operating within approved boundaries?

As AI moves deeper into enterprise systems, those questions are creating an entirely new layer of security and governance — and a rapidly expanding market around it.