A transitional operating state in which actions are observed and recorded but not blocked. It is useful for discovering real agent behaviour, but it is not a production control because it does not prevent the action or limit the side effect.
Expanded Definition
Monitor mode is a visibility-first operating state used to observe how an AI agent, workflow, or security policy behaves before enforcement is turned on. In practice, it records decisions, tool calls, approvals, denials, and downstream effects without interrupting execution. That makes it valuable for testing policy logic, surfacing edge cases, and understanding how often a rule would trigger in live conditions.
Usage in the industry is still evolving, and definitions vary across vendors. In some platforms, monitor mode is treated as a safe rollout phase for policy changes. In others, it is a dedicated telemetry mode for agent governance or access control tuning. The important distinction is that monitor mode is not a control outcome by itself; it is an observation state that informs later enforcement. For security teams, that difference matters because the system may appear governed while still allowing the underlying action to proceed. NIST’s NIST Cybersecurity Framework 2.0 is useful here because it frames governance, monitoring, and continuous improvement as separate but connected security activities.
The most common misapplication is treating monitor mode as a protective control, which occurs when teams assume logged visibility means risky actions are already prevented.
Examples and Use Cases
Implementing monitor mode rigorously often introduces a temporary trust gap, requiring organisations to weigh better detection and policy tuning against the risk of allowing undesirable actions to continue during observation.
- An AI agent is allowed to suggest file deletions, but monitor mode logs each request and its context before any enforcement rule is activated.
- A new policy for agentic AI is run in monitor mode to measure how often the agent attempts tool use outside approved boundaries.
- A security team tests a secrets-handling rule by watching whether an agent tries to access tokens or certificates that should never leave the approved vault boundary.
- A privileged workflow is observed in monitor mode to validate whether escalation requests are legitimate before moving the system into a blocking phase.
- An organisation uses monitor mode during a rollout of Non-Human Identity governance so it can see which service accounts, workloads, or agents would be affected without interrupting production traffic.
In these examples, monitor mode is used to build confidence from real execution data rather than synthetic test cases alone. It is especially useful when the team needs to understand how often exceptions occur, whether policy definitions are too broad, and where false positives would create operational friction.
Why It Matters for Security Teams
Monitor mode matters because it is often the first step in turning a policy idea into a reliable security control. Without it, teams may enforce too early and break business workflows, or they may defer enforcement indefinitely and leave exposure unchanged. For identity and agentic systems, that risk is amplified because the subject being observed may be an NHI, an AI agent, or an automated process acting with execution authority. In those environments, monitor mode helps teams learn which identities are acting, which tools they reach for, and where policy boundaries are too permissive.
This is also where governance discipline becomes critical. A system can generate logs, alerts, and dashboards while still permitting the very action the rule was intended to stop. NIST’s Cybersecurity Framework 2.0 supports the broader practice of detecting, responding, and improving based on observed behaviour, while OWASP NHI Top 10 is especially relevant when monitor mode is used to study service accounts, workload identities, and other machine actors.
Organisations typically encounter the real cost of monitor mode only after a blocked action, privilege abuse, or agent misfire makes clear that observation alone was never enough, at which point enforcement becomes operationally unavoidable.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Non-Human Identity Top 10 and OWASP Agentic AI Top 10 address the attack and risk surface, while NIST CSF 2.0, NIST AI RMF and NIST Zero Trust (SP 800-207) set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| NIST CSF 2.0 | DE.CM-01 | Monitor mode supports continuous observation of security-relevant activity. |
| OWASP Non-Human Identity Top 10 | NHI guidance covers governance of machine identities and their observed behaviour. | |
| OWASP Agentic AI Top 10 | Agentic AI guidance emphasises observing tool use and unsafe behaviour patterns. | |
| NIST AI RMF | AI RMF supports measuring and managing observed AI behaviour during deployment. | |
| NIST Zero Trust (SP 800-207) | Zero Trust design requires continuous verification of actions and access decisions. |
Log and review observed agent actions before moving from visibility to enforcement.
Related resources from NHI Mgmt Group
- How should security teams monitor AI agent activity without disrupting developers?
- What is the difference between sandbox mode and true network isolation for AI workloads?
- What should IAM teams monitor to detect OAuth token compromise?
- What should security teams monitor to detect SaaS supply chain abuse?
Deepen Your Knowledge
Reviewed and updated by the NHIMG editorial team on August 16, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org
