They should extend IAM controls into the gateway, where token brokering, policy enforcement, and audit logging can be applied consistently. The key is to govern the session and the tool set together, because agentic systems can change what they touch faster than legacy access review cycles can follow.
Why This Matters for Security Teams
MCP and agentic ai change the identity problem from static user access to delegated, tool-using execution. Traditional IAM was built around people, apps, and predeclared permissions, but agents can chain tools, change scope mid-session, and act faster than manual review cycles. That means the risk is not just overpermissioning, but uncontrolled delegation at runtime. Guidance from the OWASP Top 10 for Agentic Applications 2026 and NHI research on OWASP Agentic Applications Top 10 both point to the same issue: the control point has to move closer to the action.
This is also why token exposure and session sprawl matter so much. When agents inherit broad credentials, one compromised tool path can become a lateral-movement path. NHIMG’s LLMjacking: How Attackers Hijack AI Using Compromised NHIs research shows attackers move quickly once credentials are exposed, which is exactly the speed mismatch that legacy IAM struggles to contain. In practice, many security teams encounter this only after an agent has already accessed data or invoked tools outside its intended scope, rather than through intentional access review.
How It Works in Practice
The practical answer is to treat the gateway as the policy enforcement layer for MCP and agent sessions. Instead of issuing durable credentials directly to the model or agent runtime, teams broker access through short-lived tokens, scoped per task and per tool. That enables runtime checks against the request, the caller, the target tool, the data classification, and the session context. This pattern aligns with the direction of NIST AI Risk Management Framework and CSA MAESTRO agentic AI threat modeling framework, both of which emphasise governance, monitoring, and context-aware control.
A working implementation usually includes:
- Workload identity for the agent runtime, not just a user session, so the system knows what is calling the gateway.
- Token brokering at the gateway with tight TTLs, per-task scope, and automatic revocation on completion or anomaly.
- Policy-as-code decisions at request time, using context such as tool risk, dataset sensitivity, and action type.
- Full audit logging of issued tokens, tool calls, outputs, and denied actions for forensics and compliance.
- Segregation between interactive prompt handling and privileged tool execution, so prompt injection does not become direct authority.
That approach fits the lessons in NHIMG’s CoPhish OAuth Token Theft via Copilot Studio and Analysis of Claude Code Security, where the boundary between agent intent and privileged action becomes the real attack surface. These controls tend to break down when teams let the agent carry long-lived credentials into legacy systems that cannot evaluate context at request time.
Common Variations and Edge Cases
Tighter gateway control often increases operational overhead, requiring organisations to balance speed against precision. That tradeoff is real, especially when multiple agents, plugins, and MCP servers need different trust levels. Current guidance suggests that not every tool deserves the same policy path, but there is no universal standard for that yet. Some teams use tiered trust zones for read-only tools, write-capable tools, and destructive tools, while others enforce step-up approval only for high-impact actions.
The hardest edge case is partial legacy coverage. If an MCP server talks to systems that only understand static IAM groups or broad service accounts, the gateway can reduce exposure but cannot fully eliminate it. In those environments, teams should narrow the blast radius with short-lived secrets, separate service principals, and explicit allowlists for high-risk actions. NHI governance research from Ultimate Guide to NHIs is useful here because the core problem is still identity lifecycle, even when the workload is autonomous. Best practice is evolving, but the direction is clear: govern the session, not just the account.
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 | Agent tool abuse and runtime misauthorisation are central to this gateway control question. |
| CSA MAESTRO | MAESTRO models the trust and threat boundaries needed for agent and MCP governance. | |
| NIST AI RMF | AI RMF supports governance, monitoring, and accountability for autonomous agent behaviour. | |
| OWASP Non-Human Identity Top 10 | NHI-03 | Short-lived, scoped credentials are essential when agents use privileged non-human identities. |
| NIST CSF 2.0 | PR.AC-4 | Least-privilege and access governance apply directly to agent sessions and tool permissions. |
Assign owners, define risk tolerances, and monitor agent actions with repeatable governance controls.
Related resources from NHI Mgmt Group
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Reviewed and updated by the NHIMG editorial team on August 18, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org
