Both teams need shared ownership because the problem is simultaneously about abuse detection and identity governance. Fraud teams understand adversarial traffic patterns, while IAM teams control identity, entitlement, and authorisation policy. The operational model should join those disciplines so one team is not approving traffic the other team is trying to stop.
Why This Matters for Security Teams
AI agent traffic is not just another application control problem. Fraud teams look for abuse patterns, impossible behaviour, and anomalous session paths. IAM teams own identity, entitlement, policy, and credential governance. When agents can chain tools, call APIs, and act at machine speed, ownership cannot sit in only one discipline without creating blind spots. The practical risk is that one team approves a path the other team would flag as hostile.
That tension is already visible in current research. NHIMG notes in AI Agents: The New Attack Surface that 80% of organisations report AI agents have already performed actions beyond intended scope, including unauthorized systems access and credential exposure. That is why the control question is broader than traffic monitoring. It is about who can interpret intent, who can authorize it, and who can stop it when behaviour shifts. NIST frames this as shared governance across risk, identity, and operational controls in the NIST AI Risk Management Framework.
In practice, many security teams discover the ownership gap only after an agent has already chained access across systems, rather than through a deliberate review of control boundaries.
How It Works in Practice
The most workable model is joint ownership with clear control boundaries. Fraud teams should lead detection for abusive traffic patterns, prompt manipulation, abnormal tool use, and behavioural escalation. IAM teams should lead workload identity, authentication, authorization policy, credential lifecycle, and entitlement review. For autonomous agents, static RBAC alone is usually too coarse because the agent’s access pattern is not stable; it changes by task, context, and tool chain.
Current guidance suggests treating the agent as a workload identity first, then applying runtime policy decisions to what it is trying to do. That means short-lived credentials, per-task authorization, and policy-as-code checks at request time. The goal is to issue only the minimum access needed for the current action and revoke it as soon as the task completes. This is consistent with the control direction in OWASP Top 10 for Agentic Applications 2026 and the threat modeling emphasis in the CSA MAESTRO agentic AI threat modeling framework.
- IAM owns the agent’s cryptographic identity, token issuance, and secret hygiene.
- Fraud owns anomaly detection, behavioural scoring, and escalation triggers.
- Both teams agree on policy thresholds for high-risk actions such as data export, privilege changes, and payment or account operations.
- Policy evaluation should happen at runtime, not only during design review.
NHIMG’s The 2024 Non-Human Identity Security Report shows that 59.8% of organisations see value in dynamic ephemeral credentials, which fits agent traffic far better than long-lived secrets. These controls tend to break down in legacy environments where agents share service accounts, outbound traffic is flattened through proxies, and there is no reliable way to tie one action back to one workload identity.
Common Variations and Edge Cases
Tighter agent traffic governance often increases operational overhead, requiring organisations to balance abuse resistance against release speed and support complexity. That tradeoff is real, especially when business teams want agents to move quickly across many systems.
There is no universal standard for this yet. In some environments, fraud teams can own detection and response while IAM owns preventive controls; in others, a platform or security engineering team may sit between them as the policy operator. The important point is that neither team should own the full problem alone. Fraud-only ownership tends to miss entitlement drift and secret exposure. IAM-only ownership tends to miss novel abuse patterns and lateral movement.
Edge cases appear when agents are customer-facing, perform financial actions, or operate across multi-cloud and SaaS integrations. In those settings, the control model should be explicit about who can approve new tool access, who can revoke it mid-session, and who gets alerted when the agent exceeds expected behaviour. NHIMG’s Ultimate Guide to NHIs — Standards is a useful reference point for mapping those responsibilities to identity governance, while the OWASP NHI Top 10 is a practical reminder that agent behaviour and identity controls must be designed together. The right operating model is shared ownership with a single decision path, not split accountability with competing approvals.
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 and CSA MAESTRO address the attack and risk surface, while NIST AI RMF set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | A01 | Agent traffic ownership depends on runtime abuse paths and agent-specific trust failures. |
| CSA MAESTRO | MT-2 | MAESTRO covers agent threat modeling and control ownership across teams. |
| NIST AI RMF | AI RMF supports shared governance for identity, abuse detection, and accountability. |
Assign fraud and IAM responsibilities within MAESTRO and review agent actions as shared risk events.
Related resources from NHI Mgmt Group
- How do IAM teams decide whether an AI security assistant needs its own access controls?
- How should security teams classify AI agent traffic in fraud prevention flows?
- Who should own AI agent compliance across security and IAM teams?
- How do IAM teams know if privileged access controls are actually working?
Deepen Your Knowledge
Reviewed and updated by the NHIMG editorial team on June 11, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org
