Topic starter
10/06/2026 10:57 pm
TL;DR: AI gateways can authenticate users and route traffic, but they often cannot decide what those identities may do across models, tools, MCP methods, and delegated agent calls, according to Cerbos. Fine-grained authorization shifts that decision to policy, where contextual access can be enforced at every hop instead of drifting into agent logic.
Editorial analysis by NHI Mgmt Group, based on content published by Cerbos: “Fine-grained authorization for AI gateways”.
Key questions
Q: What breaks when AI gateways do not tie requests to identity?
A: When requests are not tied to identity, teams lose the ability to distinguish between approved use, delegated agent activity, and accidental overreach.
Q: Why do AI gateway access decisions need contextual policy instead of static allowlists?
A: Because static allowlists can answer who may reach a model, but not whether that request is allowed for this resource, team, environment, or workflow state.
Q: What are the signs that agent-to-agent delegation is creating privilege drift?
A: Look for sub-agents acting with broader authority than the initiating user intended, especially when tokens pass downstream unchanged and audit logs show only the user.
Practitioner guidance
- Implement request-level policy enforcement Evaluate every model call, tool call, and MCP method before the gateway forwards it, using principal, resource, and context attributes together.
- Separate authentication from authorisation decisions Keep identity verification at the gateway, but move allowed-action logic into a policy layer that can test team ownership, environment, and data classification.
- Attenuate delegated agent scopes Require each downstream agent or sub-agent to stay within the delegating principal's permissions and deny re-expansion unless the original grant explicitly allowed it.
Bottom line: AI gateways can validate callers and still leave the actual permission decision unresolved, which is why request-level authorization matters.
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This topic was modified 7 days ago by NHI Mgmt Group
12/06/2026 4:43 am
AI gateways have become enforcement chokepoints, but not governance points by default. They authenticate traffic, yet the real decision about allowed action often remains buried in agent code or downstream services. That split creates an authorization gap across model calls, tool calls, and MCP methods. The implication is that identity teams cannot stop at the front door if the policy engine is not at the same boundary.
A few things that frame the scale:
- 24,008 unique secrets were exposed in MCP configuration files in 2025 alone, the protocol's first year of widespread adoption, according to the State of Secrets Sprawl 2026.
A question worth separating out:
Q: How should security teams govern managed MCP access for AI clients?
A: Security teams should treat managed MCP as a federated resource server and issue identity-bound tokens for each delegated task. That means no shared service-account secrets, narrow role scopes, and audit logs that can tie the human requester to the agent session and the resulting data access. Use the token as the control boundary, not the client config.
👉 Read our full editorial: AI gateways need fine-grained authorization, not just identity checks
This post was modified 7 days ago by NHI Mgmt Group