A common warning sign is that teams can list which agents exist, but cannot explain what those agents actually do moment to moment. Other indicators include shadow agents, unclear secret usage, and limited correlation across human, NHI, and agent activity. When visibility is static instead of behavioral, policy may look complete while real actions remain unobserved.
What runtime visibility has to show, not just name
Runtime visibility is only useful when it reveals current behavior, not just registration data. For agentic identity governance, that means seeing which agent invoked which tool, under what authority, against which target, and with what side effects. If your inventory is accurate but the execution trail is opaque, governance is probably static while risk is changing in real time. For the governance side of that gap, see Ultimate Guide to NHIs — Key Challenges and Risks.
Practitioners should separate lifecycle visibility from runtime visibility. Lifecycle tells you an agent exists and who approved it; runtime tells you whether it is acting within its intended boundary, whether it is reusing secrets unexpectedly, and whether its tool calls match the business process it was supposed to automate. When those signals are missing, policy attestations can look clean while the actual control surface is drifting.
The strongest internal benchmark is whether you can reconstruct a sequence of agent decisions from logs alone. If you can only answer “was it provisioned?” and not “what did it do in the last hour?”, you do not have operational visibility. That is especially important when agent activity overlaps with human and NHI activity, because a shared execution path can hide delegated abuse or misattributed action. The broader governance problem is covered in NHI Lifecycle Management Guide and 2026 Identity Security Trends & Predictions.
Signals that the visibility model is too static
Shadow agents are a strong warning sign, but the deeper pattern is behavioral blind spots. If an agent can appear in inventory while its tool usage, secret access, and downstream actions remain uncorrelated, the governance model is only tracking state, not behavior. Another red flag is when teams can explain policy rules yet cannot identify which runtime events would prove a policy was actually enforced.
A practical way to test for this gap is to ask whether your telemetry can answer three questions together: what the agent touched, what credential or token was used, and whether the action was normal for that agent at that time. If any one of those is missing, you may still have monitoring, but you do not yet have usable governance visibility. For a maturity baseline on the problem space, compare against The State of Non-Human Identity Security and the evidence in The 2024 ESG Report: Managing Non-Human Identities.
Where organisations also struggle is in correlating agent activity with the surrounding identity fabric. If human approvals, machine actions, and agent tool calls are logged in separate systems with no shared correlation key, anomalous behavior can remain invisible until an incident forces reconstruction. That is why runtime visibility should be judged by correlation quality, not log volume.
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 and NIST AI RMF set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Non-Human Identity Top 10 | Visibility and Discovery — Visibility and Discovery | Runtime visibility gaps are central to NHI governance and agent activity tracing. |
| Secrets and Credential Management — Secrets and Credential Management | Unclear secret usage is a direct sign of missing runtime governance over agent actions. | |
| Excessive Permissions — Excessive Permissions | Overbroad agent authority becomes hidden when runtime behavior is not visible. | |
| Recommendation — Instrument discovery and runtime telemetry so every agent action can be correlated to its authority path. Track secret use at runtime and alert when agent actions depend on unexpected credentials. Continuously verify that agent privileges match observed behavior and intended scope. | ||
| OWASP Agentic AI Top 10 | Agent Tool Use and Authority — Agent Tool Use and Authority | The question is about whether agent runtime actions are observable and attributable. |
| Agent Identity and Lifecycle — Agent Identity and Lifecycle | Shadow agents and weak runtime visibility indicate gaps in agent governance across the lifecycle. | |
| Recommendation — Log and review every tool call against the agent's delegated authority before granting broader access. Maintain a live agent inventory linked to execution telemetry and ownership metadata. | ||
| NIST CSF 2.0 | DE.AE-1 — Anomalous Activity Is Detected | Behavioral visibility depends on detecting deviations from expected agent activity. |
| ID.AM-1 — Physical Devices and Systems Are Inventoried | Agent visibility begins with knowing what exists before assessing what it is doing. | |
| PR.AC-4 — Access Permissions and Authorizations Are Managed | Runtime visibility is needed to confirm permissions match actual agent behavior. | |
| Recommendation — Detect and triage deviations in agent behavior, tool use, and access patterns. Keep the agent inventory current and tie each entry to its runtime telemetry source. Review agent permissions against observed actions and remove authority that is never justified in use. | ||
| NIST AI RMF | GOV — Govern | Agentic governance is fundamentally a governance and accountability problem for AI-enabled action. |
| MAP — Map | Mapping the agent's operating context is necessary to know what behavior should be visible. | |
| Recommendation — Establish accountability for agent actions and require runtime evidence of control effectiveness. Map agent capabilities, dependencies, and expected behaviors before trusting runtime reports. | ||
Practitioner Guidance
What to prioritise: Start with the events that change risk the fastest, which are tool invocations, secret use, privilege changes, and cross-boundary actions. Inventory completeness matters, but it is not the first proof of runtime governance; the first proof is whether you can trace a live action back to an accountable agent and an intended authority path.
What to verify: Confirm that telemetry can answer three operational questions without manual stitching: which agent acted, which secret or delegated permission enabled the action, and what object or system was affected. If those answers require multiple teams to reconstruct after the fact, the visibility model is too weak for governance to be trusted.
What practitioners underestimate: The most dangerous failure mode is not total absence of logs, but misleading completeness. A dashboard can show provisioned agents, approved policies, and healthy counts while missing the behavioral evidence that would expose shadow activity or authority drift.
Practitioner takeaway: Good agentic identity governance is proven by behavioral traceability, not by a tidy registry, so treat any inability to correlate agent, secret, and action as a runtime visibility defect rather than an observability nuisance.
Related resources from NHI Mgmt Group
- Why is it important to integrate identity and data governance?
- Why is visibility important in AI governance?
- What are the signs that an identity governance approach is still fragmented rather than truly converged?
- Why does closing on-premises data centers make identity governance more important for security and compliance?
Deepen Your Knowledge
Reviewed and updated by the NHIMG editorial team on September 20, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org
