What Kong AI Gateway Plugins Enforce Out of the Box for Regulated LLM Traffic

What "out of the box" should mean in an evaluation

Security teams evaluating an AI gateway often collapse four different things into one claim that a control is supported. A control may be enforced by default when the plugin is enabled. It may be enforceable only after your team writes rules, sets thresholds or connects an external service. It may require a custom plugin. Or it may belong in a different layer entirely.

The distinction matters for regulated LLM traffic because an auditor does not ask whether a feature exists. The auditor asks whether a specific control was active on a specific interaction, what it did, and what record shows that. A plugin that is installed but unconfigured, or configured to log rather than block, satisfies none of those questions.

For each plugin family under review, including AI Proxy, AI Prompt Guard, AI Prompt Decorator, AI Semantic Prompt Guard, AI Rate Limiting Advanced and AI Sanitizer, record four facts: the default behavior, the required configuration, the action taken on a violation, and the output left behind for evidence. Record the version and license tier you tested against, since both affect what you can rely on.

Control categories and what to test for each

The table lists the control categories most relevant to regulated LLM traffic and the questions that decide whether a gateway plugin covers the requirement. Each row should be answered from your own test environment, not from a feature list.

• Prompt filtering: Test what a match triggers (block, transform or log), how rules are defined, and how the filter behaves on rephrased or novel prompts.
• PII sanitization: Test which data types are detected, whether detection is applied to prompts, responses or both, and whether the original value is retained anywhere.
• Authentication and access control: Test whether identity is established per consumer, per application or per user, and whether access can be scoped by model or route.
• Rate limiting: Test the unit of limiting (requests or tokens), the scope (consumer, route or model), and the response when a limit is exceeded.
• Request transformation: Test whether injected or modified prompt content is recorded, so the record shows what the model actually received.
• Content moderation integrations: Test which external service is called, what happens if it is unavailable, and whether its verdict is retained as evidence.

Why filter-style controls need adversarial testing

Prompt guards, semantic matching and output restrictions are filter-style controls. NIST's Generative AI Profile (NIST AI 600-1) notes that many current systems restrict model outputs to limit certain content or in response to certain prompts, and that this approach may still produce harmful recommendations in response to other, less-explicit, novel prompts. The profile ties this to data privacy and information security risks.

The practical consequence is that a passing test against known-bad patterns says little about coverage. Build a test set that includes paraphrased requests, indirect phrasing, multi-turn setups and prompts that embed sensitive data in unusual formats. Measure the miss rate and decide whether it is acceptable for the data classes involved.

The same reasoning applies to PII sanitization. Detection quality depends on the data types you handle and the formats they appear in. Test with your own representative records, including identifiers specific to your business, rather than only common patterns such as standard-format numbers.

Where gateway-level controls stop

A gateway sits on the request path between applications and model endpoints. That position suits authentication, rate limiting, request shaping and first-pass filtering. It is a weaker fit for decisions that depend on what an agent is about to do with a model's output.

Three requirements commonly fall outside request-level policy. The first is tool-call enforcement: when a model or agent invokes a tool, an MCP server or an internal system, the decision to allow it should be made per call, with the tool, arguments and caller identity in view. The second is human approval: some actions need a person to approve before execution, which requires an escalation path and a held state, not a log entry. The third is evidence: audit-ready records need to show the policy that applied, the decision taken and the interaction it applied to, in a form that can be retained and mapped to a framework.

Trussed addresses this layer. It enforces policy in the execution path across models, agents, tools, MCP servers, AI sites and coding assistants, with actions of allow, redact, block, rate limit and escalate for human approval, applied to individual interactions and tool calls. Enforcement generates continuous audit-ready evidence mapped to HIPAA, NAIC, NYDFS, the EU AI Act and NIST, and it can be deployed self-managed in the customer cloud with configurable retention and data residency. It is a runtime control layer, not a replacement for gateway functions such as routing and authentication, and the two can be evaluated separately.

Turning the results into an architecture decision

Once each control is classified as default, configured, custom or out of scope, the gaps become concrete. Controls that are configured need an owner and a change process, because a rule edit alters your compliance posture. Controls that need custom plugins carry ongoing maintenance, and their evidence output must be designed rather than inherited. Controls that sit outside the gateway need a defined layer in the execution path.

Two tradeoffs deserve explicit decisions. Blocking on a filter match reduces exposure but increases false positives, so set the action by data class rather than globally. Failing closed when a moderation service is unavailable protects regulated data but affects availability, so decide the behavior before it is tested in production.

Finally, decide what an auditor will be shown. If the answer is a set of raw gateway logs, check whether those logs identify the policy and decision for each interaction. If they do not, the evidence requirement is unmet regardless of how well the plugins perform.