AI agent monitoring

AI agent monitoring gives security teams continuous visibility into what AI agents are doing, what they can access, who owns them, and whether the controls around them are being applied as expected. Unlike human users who usually act one step at a time, AI agents can call tools, retrieve data, and perform operations in rapid succession. This speed and autonomy make traditional identity and access controls insufficient on their own, creating a need for continuous visibility and governance built specifically around agent activity.

ManageEngine SecureAI is an AI agent security and governance solution that treats AI agents as a new identity type alongside your human workforce. It brings agent ownership, MCP server activity, policy decisions, and AI platform risk posture into one place.

Why AI agent monitoring can no longer wait

AI agents are moving into enterprise environments faster than the controls used to oversee them. According to Deloitte's 2026 State of AI in the Enterprise research, only 21% of surveyed enterprises have mature governance in place for agentic AI. Deloitte identifies real-time monitoring of agent behavior and audit trails that capture the full chain of agent actions among the capabilities many organizations still lack.

The consequences of weak oversight are already measurable. The IBM Cost of a Data Breach Report 2026 found that 92% of organizations that reported an AI-related security incident lacked proper AI access controls and that 63% lacked AI governance policies to manage AI.

Monitoring also has to continue after an AI system is deployed. In its 2026 report Challenges to the monitoring of deployed AI systems, NIST identifies post-deployment AI agent monitoring as necessary for tracking unexpected behavior and consequences that emerge when AI systems operate in real-world environments.

For security teams, AI agent monitoring means maintaining a continuous record of which AI agents are operating, who owns them, what they access, which actions they take, and whether the controls governing those actions are working as expected.

How SecureAI monitors AI agents

SecureAI approaches AI agent monitoring through three layers: discover, observe, and assess. Together, security teams can monitor what AI agents are operating, what they are doing, and where risks or AI governance gaps exist in your environment.

Discover: Know which agents exist and who is accountable

SecureAI discovers AI agents across Microsoft Foundry, Microsoft Copilot Studio, Google Gemini Enterprise Agent Platform, and Zoho Zia. For each agent, it surfaces information such as its provider, privilege level, creation date, modification date, and associated MCP servers.

An agent ownership policy is triggered as soon as an agent is discovered and a human owner is assigned. SecureAI uses Microsoft Entra, Google Directory, or Okta to associate the agent with a named human owner. Agents without confirmed ownership are flagged as unmanaged.

Security teams get a current view of how many agents, models, and MCP servers exist across the environment, along with unmanaged and high-privilege agents that require closer attention. Ownership gaps therefore remain visible as your environment changes instead of surfacing only during periodic audits.

Observe: Track what agents and MCP servers are doing

SecureAI uses MCP gateways as the observation point for agent activity. Agents connect to the gateway instead of directly to registered MCP servers, allowing their activity to be recorded as requests pass through.

For every request, SecureAI records the agent identity, the human principal behind it, the MCP server contacted, the tool invoked, the policy outcome, and the timestamp. Tools are classified as read or write operations when an MCP server is registered, so security teams can distinguish between agents retrieving information and agents attempting to change it.

This makes it possible to see which agents are using individual MCP servers, what tools they invoke, and whether their requests are allowed, blocked, or redacted.

SecureAI also keeps policy enforcement visible alongside live activity. Access policies determine whether an agent can reach a server or tool, while DLP policies inspect content flowing through the gateway and can redact or block sensitive data. Security teams can track blocked requests, DLP events, policy enforcement rates, tool call outcomes, MCP server latency, and geographic access origins from the same monitoring interface.

When deeper investigation is needed, the Gateway Activity log provides the request-level audit trail behind these metrics.

Assess: Monitor AI risk and safety control coverage

SecureAI helps security teams assess whether the expected governance and safety controls are present across their AI environment.

Guardrail coverage is tracked across supported agent platforms, giving teams an environment-wide view of how broadly safety controls are applied. Microsoft agents can be reviewed individually for their guardrails, while Google Gemini Enterprise Agent Platform agents can be checked against their Model Armor Templates.

SecureAI also performs AI risk assessment at the platform configuration level for Anthropic Claude, GitHub Copilot, and Gemini. It checks these enterprise AI platforms for misconfigurations and maps findings to the EU AI Act, ISO/IEC 42001, and NIST AI RMF.

Security teams can therefore monitor runtime agent behavior alongside the configuration posture and safety-control coverage of the wider AI environment.

How organizations use SecureAI for AI agent monitoring

Discover AI agents and assign accountable owners

SecureAI continuously discovers agents across connected platforms and assigns human owners when they appear. Agents without confirmed human owners are flagged as unmanaged, giving security teams a live view of ownership gaps as new agents are deployed.

Monitor MCP servers and the agents using them

SecureAI tracks registered MCP servers and the activity flowing through them. Security teams can see which agents use each server, which tools they call, how frequently those tools are used, and whether each operation reads or writes data.

They can also monitor MCP server latency, errors, and inactivity, making it easier to identify both unusual agent behavior and operational issues affecting agent-to-tool communication.

Monitor AI agent ownership, access, and policies

SecureAI keeps policy activity visible alongside the agents and requests those policies govern. An agent ownership policy establishes accountability when an agent is discovered, access policies control which servers and tools it can reach, and DLP policies inspect data moving through MCP gateways.

Allowed calls, blocked calls, and data redactions are retained as an auditable record, while policy enforcement and DLP activity can be monitored over time. Security teams can therefore verify whether controls are actually being applied to live agent activity instead of relying only on policy configuration.

Track and manage AI security posture

SecureAI's risk assessment checks the configuration of Anthropic Claude, GitHub Copilot, and Gemini and maps findings to relevant requirements and guidance from the EU AI Act, ISO/IEC 42001, and NIST AI RMF, helping security teams identify configuration risks alongside their AI agent monitoring data.