AI agent governance

Why AI agents need a governance process

AI agents change the security equation because they do more than generate an answer. They can plan a sequence of steps, use tools, interact with enterprise systems, work with data, and carry out actions with limited human involvement. As organizations give agents more responsibility, security teams need a way to understand which agents exist, what they are allowed to access, which actions fall within their intended scope, and where human intervention is required.

AI agent governance is the discipline of defining and maintaining those boundaries. It is the set of processes and controls an organization uses to decide which AI agents may operate in its environment, what each one can access, and who is accountable for it. In practice, it extends identity governance and administration, the discipline enterprises apply to employees and service accounts, to a kind of non-human identity that can select among the tools permitted for its tasks.

ManageEngine SecureAI is an agentic AI security and governance platform that brings these agents under the controls identity teams already apply to people. It finds agents across each platform, assigns every agent an owner, maps the entitlements it holds, limits what it can do through policy at the MCP gateway, and certifies its access on a recurring schedule.

Build an AI agent governance strategy

Maintain an inventory of agents  

SecureAI connects to Microsoft Copilot Studio, Microsoft Foundry, Zia Agents, and Gemini Enterprise Agent Platform (formerly Vertex AI) and builds a single AI inventory of the agents, MCP servers, and models registered on each platform. Each agent record shows the application and instance it belongs to, the model it uses, its owner, and whether it is active, so security teams can work from one list instead of several admin consoles.

Assign an owner to every agent  

An agent should not be trusted simply because it is acting on behalf of a legitimate user or application. Give each agent an identity, scope its access to the work it is expected to perform, and review delegated permissions as its role changes. SecureAI automatically applies ownership policies to assign an accountable owner to each agent when it is discovered. Establishing ownership and accountability for AI agents this way ensures the later stages of governance, including access reviews, always have a named person behind them.

Map what each agent can access

The Access Graph maps each agent to the resources it can reach, grouped into agent identity, LLMs, knowledge, tools, MCP servers, memory, guardrails, and IAM roles. Reviewers can see in a single view how an agent's permissions connect, which is easier to reason about than a flat list of grants spread across directories.

Enforce policy at the MCP gateway  

An agent's entitlements show what it could do, but the tools it actually calls depend on the task in front of it. Through SecureAI's MCP gateway, each agent is checked against the control policies that apply to it. Access policies decide which identities can use which MCP servers and tools, while DLP policies look inside tool input and tool output for sensitive content, then either block the request or redact the matching text and let the rest through.

Review and certify agent access

Access certification closes the governance loop by producing a record that a person reviewed each agent's access and decided whether it should stay. Access certification campaigns run periodic reviews of agent entitlements, and reviewers either certify the access as still needed or revoke it, which keeps agents close to least privilege as their work changes. Because ownership is assigned at discovery, the question of who should review an agent's access already has an answer before a campaign begins.

Governing AI agents with SecureAI

1. Discover and document AI agents: Establish ownership, purpose, identity, connections, and the business process each agent supports.
2. Define agents' scope: Specify what they may access, which tools they may invoke, and which actions remain outside their role.
3. Apply least-privilege access: Limit credentials and delegated permissions to what the agent needs, and review them as the workflow evolves.
4. Set policy and oversight points: Decide which actions can proceed autonomously and which should be blocked, reviewed, or redacted.
5. Monitor activity and retain evidence: Keep sufficient records to investigate behavior, support accountability, and identify where governance needs to change.
6. Reassess throughout the life cycle: Regularly revisit permissions, tool connections, policies, and ownership over what the agent can access.