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What is Shadow AI and how does Tumeryk help manage it?

Learn how Tumeryk helps organizations discover ungoverned AI usage, build an enterprise AI inventory, and identify associated security and governance risks.

Shadow AI refers to AI tools, models, applications, or services being used within an organization without adequate visibility, approval, or governance from security, IT, risk, or compliance teams.

As generative AI adoption expands, employees and teams can begin using public AI tools, embedded AI capabilities, copilots, models, and AI-powered applications faster than traditional enterprise discovery and governance processes can identify them.

This can create visibility and risk-management gaps. Organizations may not have a complete understanding of which AI systems are being used, who is using them, what data they can access, or what risks they introduce.

Tumeryk helps organizations:

  • Discover AI usage across enterprise environments.
  • Identify AI models, tools, services, applications, and agents being used within the organization.
  • Build a centralized AI inventory to provide security and governance teams with greater visibility into the enterprise AI footprint.
  • Map ownership, usage, and access patterns associated with discovered AI assets.
  • Assess vulnerabilities and risk through automated testing and validation.
  • Prioritize AI risk so teams can determine which systems require further assessment, remediation, security controls, or governance.
  • Continuously monitor AI posture as new AI technologies, users, and configurations are introduced.

Discovery is only the first stage. Once an AI asset is identified, Tumeryk’s broader Discover → Assess → Secure → Govern lifecycle enables organizations to assess its risk, apply appropriate security controls, and establish ongoing governance.

This approach helps organizations move away from relying solely on static inventories or employee self-reporting toward continuous visibility into enterprise AI usage and risk.

The objective is not simply to restrict AI adoption. By understanding where AI is being used and the risks associated with it, organizations can make more informed decisions about which AI systems can be approved, restricted, remediated, or monitored.