Generative AI and AI Agents are rapidly becoming part of enterprise environments. Employees may use AI services such as ChatGPT, Claude, and Gemini, while development or IT teams may deploy various AI Agents across the organization.

As enterprise AI adoption becomes increasingly diverse, the first question organizations need to address is no longer whether they should use AI, but rather: Do we actually know which AI Agents are being used across our organization?

Establishing this visibility is a fundamental step in putting AI Governance into practice.


What Is Essential to AI Governance?

AI Governance refers to the policies, responsibilities, and management mechanisms organizations establish to govern AI adoption, usage, and associated risks. It encompasses areas such as data governance, cybersecurity, privacy, regulatory compliance, model risk, and continuous monitoring.

AI Governance therefore cannot be achieved simply by deploying a single tool. It requires a cross-functional management framework. Regardless of the governance approach an organization adopts, however, it must first answer a fundamental question: What AI is actually being used within the organization?

Without a clear understanding of AI assets and usage, organizations may face blind spots when conducting risk assessments, establishing policies, and implementing security controls.



Why Should Enterprises Inventory AI Agent Usage First?

AI adoption often moves faster than internal governance processes can keep up.

In addition to officially approved AI services, employees may independently sign up for generative AI platforms, install AI applications, or authorize AI Agents to access files, browsers, and other corporate resources. This can create a Shadow AI problem similar to Shadow IT.

Unmanaged AI applications may introduce risks such as sensitive data leakage, inappropriate access permissions, malicious instructions, and exposure to third-party services.

Building an AI Inventory is therefore an important foundation for effective AI Governance.


What Should Enterprises Include in an AI Agent Inventory?

The first step is to establish a basic AI Inventory. Organizations can document information such as the AI tool or service name, department using it, purpose of use, types of data involved, deployment method, and responsible personnel. This helps answer a basic question: Who is using which AI, and for what purpose?

The second step is to gain visibility into the AI Agents actually running on endpoints. Policy reviews and employee surveys can only reveal AI usage that the organization already knows about. If employees independently install AI applications, browser extensions, or AI Agents, IT and security teams may still lack complete visibility.

The third step is to classify and prioritize risks. For example:

  • Does the AI have access to confidential documents?

  • Can it read or write files, execute programs, or connect to external services?

  • Do its permissions exceed actual business requirements?

Organizations can prioritize subsequent governance and security measures based on data sensitivity, access privileges, and usage scenarios.



Build the Foundation for AI Governance by Making AI Visible

AI Governance covers policies, processes, compliance, and cybersecurity. While an AI inventory is only one part of governance, visibility into actual AI usage is essential for assessing and managing risks.

TeamT5 ThreatSonar Plus helps organizations identify AI Agents and related risks from an endpoint security assessment perspective, providing greater visibility to support risk assessment and governance decisions.

As AI adoption grows, organizations can start by identifying which AI tools and Agents are being used, where they operate, and which corporate resources they can access. This visibility provides a practical foundation for effective AI risk management and governance.