Enforcing per-team AI policies with Kong Consumer Groups

What per-team policy means on a shared gateway

Many enterprises put one Kong deployment in front of several LLM providers and AI services. Different internal teams then need different treatment: which models each team may call, how many requests and tokens it may consume, and how prompts and responses are handled. If those differences are encoded route by route, the configuration multiplies with every new team and every new model, and inconsistencies appear quickly.

Consumer groups address this by letting you express policy once per team or per policy tier and attach it to a set of consumers. The pattern is not unique to Kong. NIST guidance on attribute-based access control for microservices describes applying one type of policy to all members of a logical group, with exceptions specified, for example an allow policy for everything in a namespace and a deny policy for one named service. Group default plus named exception is a sound way to think about team policy.

The gateway is acting as a policy enforcement point in this model. NIST describes proxies in that role, enforcing allow or deny decisions. That framing helps set expectations: the gateway enforces what it can see, which is the identity of the caller, the route, the model requested, and the volume of traffic.

Verifying each team's effective policy

A group configuration that looks correct is not evidence that it behaves correctly. Verify effective policy per team by sending test traffic with each team's credential and checking the outcome against the written policy intent. At minimum, test an allowed model, a model the team should not reach, a request that exceeds the team's rate limit, and a request that violates a request control. Run the same tests with a consumer that has no group, so you know what the default does.

Test the boundaries as well as the typical case. Add a consumer to a second group and observe which settings apply. Remove a consumer from a group and confirm that it falls back to the default rather than keeping stale permissions. Where the same control is configured at more than one scope, such as globally, on a service, on a route, and on a group, establish by test which setting wins for a given request, and record the result so reviewers do not rely on assumptions.

If your design includes model fallback or rollover, include it in the test plan. NIST's generative AI profile treats testing and managing fallback and rollover behavior as a policy concern, and a fallback that sends a restricted team to a model outside its allowed list defeats the group policy.

Operational pitfalls: overlap, drift, and ownership

Most problems with per-team policy appear after the first deployment, as teams multiply and configuration changes hands. NIST's AI overlays concept paper is explicit that AI-specific controls assume certain controls are already in place, including access control, account management, identification and authentication, and configuration management. Gateway group policy sits on top of those, and weaknesses in them show up as inconsistent team policy.

• Overlapping scopes: Document which scope is intended to define each control, and avoid setting the same control at several levels without a reason.
• Configuration drift: Compare the running gateway state with the repository on a schedule, and treat manual changes as incidents to reconcile.
• Stale membership: Reconcile consumer and group membership with the directory so departed or reassigned workloads lose access.
• Unowned groups: Assign a named owner to every group. NIST's draft guidance on misuse risk stresses clear responsibilities for responding to incidents.
• Untested exceptions: Every per-consumer exception to a group default needs a test and a review date.

Where gateway group policy ends

Consumer groups answer a bounded question well: which team is calling, which model or service it is addressing, and how much it may use. That is valuable, and for many internal use cases it is sufficient.

Regulated enterprises often have to go further. A team may be permitted to call a model, yet individual interactions within that permission still need a policy decision: whether a prompt contains data that must be redacted, whether a response may be returned, whether a tool call should proceed or wait for a human. Those decisions are made on the content and context of each interaction and tool call, not on route and rate configuration. Equally, a regulator or auditor may ask what each team's AI was allowed to do and what it did. Gateway configuration shows what was intended. It does not by itself produce durable evidence of what was enforced on each interaction.

This boundary is a design input. Use consumer groups for team scoping, access, and quotas, and decide early which interaction-level and tool-call decisions, and which evidence, need enforcement beyond the gateway.

Runtime enforcement and evidence with Trussed

Trussed enforces policy in the execution path across models, agents, tools, MCP servers, AI sites, and coding assistants. Policy is enforced at runtime, not reviewed after the fact, on individual interactions and tool calls. The available actions are allow, redact, block, rate limit, and escalate for human approval.

That enforcement generates continuous, audit-ready evidence, mapped to frameworks including NIST and the EU AI Act, so a regulated enterprise can show what its AI was allowed to do and what it did. Trussed supports self-managed deployment in the customer cloud, with configurable retention and data residency. A team-scoped gateway and runtime enforcement address different layers, and per-team intent defined at the gateway can sit alongside enforcement in the path.