A gateway can enforce traffic rules, but it does not solve inventory, policy drift, or cost opacity. Teams should first establish a complete API and AI service catalog, then automate policy enforcement across the lifecycle, and finally add metering so every interaction is visible. That combination gives agents a governed entry point and supports safer scale.
Why This Matters for Security Teams
A gateway can filter traffic, but AI agents and API consumers fail at governance long before a request reaches the edge. Platform teams need inventory, ownership, policy, and usage telemetry because agents tend to chain calls, discover new paths, and expose cost or data risk outside traditional perimeter thinking. That is why current guidance in the OWASP Agentic AI Top 10 and the NIST AI Risk Management Framework emphasizes lifecycle controls, not just entry controls.
For NHIs, the real problem is that unmanaged service accounts, tokens, and AI tool permissions accumulate faster than teams can review them. NHIMG research on The 2024 ESG Report: Managing Non-Human Identities shows how compromised non-human identities remain common, which is exactly what happens when APIs and agent endpoints are treated as one-off integrations instead of governed workload identities. In practice, many security teams discover this only after an agent has already called a sensitive API, not through intentional cataloging and policy design.
How It Works in Practice
Effective governance starts with a complete service catalog for both APIs and AI capabilities. Every endpoint, model, tool, and agent should have an owner, purpose, data classification, and approved calling context. A gateway still matters, but it becomes one enforcement point in a broader control plane rather than the control plane itself.
From there, platform teams should automate policy across the lifecycle:
- Register services and agents before they are exposed to production traffic.
- Bind each workload to a distinct identity, ideally using workload identity and short-lived credentials instead of shared secrets.
- Evaluate access at request time with policy-as-code so decisions reflect the agent’s intent, target resource, and risk context.
- Meter every call for cost, volume, and anomaly detection so teams can spot runaway agents and hidden API sprawl.
This is where standards and research converge. The CSA MAESTRO agentic AI threat modeling framework and the MITRE ATLAS adversarial AI threat matrix both reinforce that agent behaviour must be modeled dynamically, not assumed stable. NHIMG’s analysis of OWASP NHI Top 10 shows the same pattern: unmanaged identity sprawl and missing runtime controls turn ordinary integration work into an abuse path. These controls tend to break down when teams run many ephemeral agents across multiple clouds because ownership, telemetry, and policy distribution fragment faster than the gateway can see.
Common Variations and Edge Cases
Tighter lifecycle governance often increases engineering overhead, so organisations must balance stronger control against delivery speed and developer self-service. There is no universal standard for this yet, especially for agentic systems that can create tools on the fly or route tasks across multiple models.
In lower-risk environments, a lighter pattern may be enough: central catalog, mandatory ownership, baseline rate limits, and simple allowlists. In higher-risk environments, current guidance suggests adding context-aware policy, task-scoped approvals, and JIT access for sensitive tools. The important distinction is that a gateway enforces traffic rules, but it does not answer whether the agent should have that capability at all.
NHIMG’s reporting on secrets and NHI exposure also shows why static controls are not enough. The State of Secrets in AppSec highlights the cost of fragmented secret management, while the Analysis of Claude Code Security underscores how quickly AI-assisted workflows expand the attack surface. Best practice is evolving toward governed service catalogs, runtime policy, and metering because agents can inherit risk from both APIs and the secrets that authorize them.
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 | A2 | Agentic systems need runtime policy and tool governance, not just gateway filtering. |
| CSA MAESTRO | M1 | MAESTRO addresses lifecycle threat modeling for agents and their tool chains. |
| NIST AI RMF | GOVERN | AI governance requires ownership, accountability, and risk controls across the lifecycle. |
| OWASP Non-Human Identity Top 10 | NHI-01 | API and agent access depends on non-human identities and their lifecycle control. |
| NIST CSF 2.0 | PR.AC-4 | Least privilege and access control apply directly to service and agent entitlements. |
Define per-agent tool rules and evaluate each request with context-aware policy before execution.
Related resources from NHI Mgmt Group
- Why do AI governance programmes need multidisciplinary oversight instead of leaving decisions to technical teams alone?
- What breaks when AI governance is limited to one platform instead of the systems where models and agents actually operate?
- How should security teams build an AI governance community that keeps improving throughout the year?
- Why is single-provider AI agent governance not enough for enterprise security?
