Policy describes intended behaviour, but runtime observability shows what the agent actually did under real conditions. Agent decisions depend on context, tool choice, and session state, so pre-deployment review cannot capture every failure mode. Observability gives security teams the evidence needed to detect misuse, drift, or delegated actions that exceed intended authority.
Why This Matters for Security Teams
agentic ai changes the control problem: policy can define what an agent should do, but only runtime observability reveals whether it actually stayed within bounds once tools, prompts, and session state began to interact. That gap matters because autonomous systems can chain actions, retry failures, and shift tactics mid-task in ways static review cannot anticipate. The AI Agents: The New Attack Surface report shows why the issue is already operational, not theoretical.
Current research suggests visibility is uneven across teams, and that is exactly where governance breaks down. The strongest policies still fail if security cannot reconstruct which tool was called, which secret was used, what data was touched, and whether the final action matched the approved intent. That is why runtime telemetry, audit trails, and identity-linked event records are now part of agent security rather than optional monitoring. Guidance from the OWASP Agentic AI Top 10 and the NIST AI Risk Management Framework both point toward runtime assurance as a necessary complement to design-time controls. In practice, many security teams discover the lack of observability only after an agent has already overreached, not during policy review.
How It Works in Practice
Effective agent governance uses policy and observability together. Policy answers whether an action is allowed; observability answers whether the agent behaved as expected under real conditions. For autonomous systems, that means tying each tool call, API request, and data access event to a workload identity, a task context, and a decision record. The relevant evidence is not just “who authenticated,” but what the agent attempted, which policy evaluation occurred, and what the outcome was.
Teams often implement this by combining real-time policy checks with immutable telemetry. A request can be evaluated at runtime against task scope, risk signals, data classification, and session state, while logs capture the prompt context, tool selection, token usage, and downstream side effects. This is where policy-as-code, workload identity, and short-lived credentials work together. If an agent uses a JIT token for a single task, the audit trail should show issuance, use, and revocation in one chain. For implementation patterns, the CSA MAESTRO agentic AI threat modeling framework and the MITRE ATLAS adversarial AI threat matrix are useful references for mapping telemetry to threat scenarios.
NHIMG research on the OWASP NHI Top 10 and the CoPhish OAuth Token Theft via Copilot Studio shows why runtime records must include delegated actions and token abuse paths, not just successful logins. These controls tend to break down when agents operate across multiple SaaS tools with weak event correlation, because the security team can no longer reconstruct the full action chain.
Common Variations and Edge Cases
Tighter observability often increases logging, storage, and review overhead, so organisations have to balance forensic depth against operational noise. There is no universal standard for how much agent telemetry is “enough” yet, but current guidance suggests preserving enough context to explain every privileged decision without exposing sensitive payloads unnecessarily.
Some environments need stronger runtime coverage than others. Highly regulated workflows, agents with write access, and multi-agent pipelines usually need session replay, action graphs, and tamper-evident logs. Lower-risk read-only copilots may rely on sampled telemetry, but that approach is weaker when agents can self-redirect or invoke external tools. The Ultimate Guide to NHIs — 2025 Outlook and Predictions is a useful reminder that NHI programs are moving toward finer-grained lifecycle control, while the NIST Cybersecurity Framework 2.0 remains a practical way to anchor detection and response expectations.
The hardest edge case is delegated action. If an agent can hand work to another agent, human, or external workflow, observability must preserve the delegation chain or the original policy loses meaning. That is why runtime visibility is not just a monitoring feature, but the evidence layer for accountability in agentic systems.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Agentic AI Top 10, CSA MAESTRO and OWASP Non-Human Identity Top 10 address the attack and risk surface, while NIST AI RMF and NIST CSF 2.0 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | A03 | Agent misuse and tool abuse require runtime evidence, not just design-time policy. |
| CSA MAESTRO | M1 | MAESTRO emphasizes threat modeling and control visibility for agentic workflows. |
| NIST AI RMF | GOVERN | AI RMF governance requires accountability and monitoring for deployed AI systems. |
| NIST CSF 2.0 | DE.CM-8 | Continuous monitoring is needed to detect unexpected agent activity and drift. |
| OWASP Non-Human Identity Top 10 | NHI-05 | NHI visibility is essential when agent identities and secrets are used at runtime. |
Map agent actions, dependencies, and failures into a live threat model with traceable events.
Related resources from NHI Mgmt Group
- When does just-in-time access reduce risk for agentic AI, and when does it fall short?
- How should security teams govern machine identity credentials in agentic AI environments?
- How can organizations mitigate tool misuse in agentic deployments?
- Why do AI gateways need runtime enforcement as well as observability?
