Kong AI Gateway PII Sanitizer vs Custom Redaction: What It Takes to Build

What you are actually choosing between

Regulated enterprises sending prompts, responses and tool-call payloads to LLMs need to keep PII from leaving approved boundaries. The two common options are a gateway-native sanitizer, such as the Kong AI Gateway PII Sanitizer plugin, and a redaction layer the team builds itself.

NIST describes an AI gateway as a centralized control point and unified interface between external applications and AI services, and lists sensitive information filtering among the functions such a gateway can perform. That makes a gateway sanitizer a legitimate category. It also defines its boundary: it governs what passes through the gateway.

A custom build is not bound to one routing layer. It can sit in an application, a sidecar or a proxy. The price is that your team owns every component, from detection to evidence. Neither choice is inherently better. The right one depends on how well each fits the criteria below, tested against your own data and traffic.

Criteria matrix: gateway-native sanitizer vs custom build

Use these criteria to score both options. The gateway column describes the structural characteristics of a plugin that runs in the gateway. Confirm product-specific behavior, such as supported entities, languages and modes, against current vendor documentation and your own test data.

• Detection coverage and tuning: Gateway: detection is packaged, so tuning is limited to what the plugin exposes. Custom: you choose and combine methods such as patterns, validation checks and models, and you own tuning for your domain-specific identifiers.
• Redaction, masking or reversible handling: Gateway: limited to the modes the plugin offers. Custom: any strategy you implement, including pseudonymization with a protected mapping store.
• Request vs response scope: Gateway: depends on whether the plugin inspects both directions. Custom: you decide, but you must build and test both paths.
• Tool-call and agent payloads: Gateway: only payloads that traverse the gateway are in scope. Custom: only the call sites you instrument or proxy are in scope.
• Latency, deployment and residency: Gateway: inline cost is added to every request in the gateway's deployment model. Custom: you control placement and residency, and you own the performance budget.
• Policy management and audit evidence: Gateway: policy is plugin configuration, and evidence depends on what the gateway records. Custom: policy and evidence schemas are yours to design, version and retain.

Redaction is one handling strategy, not the only one

NIST SP 800-188 defines redaction as removing information for legal or security purposes, also called suppression, and states that redaction alone is generally insufficient to provide formal privacy guarantees. For LLM traffic, this means a sanitizer reduces exposure of detected PII. It does not prove that a prompt is non-identifying.

NIST also describes alternatives. Pseudonymization replaces direct identifiers with pseudonyms, which allows linking information about an individual across records, provided all direct identifiers are pseudonymized systematically. A one-way hash is another way to obscure PII. Each option trades utility against re-identification risk.

The practical consequence is that you should decide the handling strategy per entity type before comparing tools. Removing a name outright may be acceptable. A customer identifier that a downstream tool call needs may call for a consistent replacement. If you pick reversible handling, the mapping itself becomes sensitive data and needs protection. NIST notes that selective redaction can reduce accuracy in datasets, so measure the same effect on model output quality for your prompts.

What a custom build requires

The initial implementation is usually the smaller part of the cost. Detection quality, regression testing and evidence design recur for as long as the system runs. Plan for each of these components and assign an owner.

• Detection: Combine pattern matching, checksum validation and context-aware models for entities such as names and addresses. Measure precision and recall on your own prompts, since false negatives leak PII and false positives degrade responses.
• Anonymization strategy: Define per entity type whether to remove, pseudonymize or hash, and protect any mapping store used for reversal.
• Policy configuration: Express rules by entity, destination, user or application, and direction. Version policies so each decision can be tied to the rules in force.
• Performance: The layer sits inline, so set a latency budget and test under realistic prompt sizes and concurrency, including streamed responses.
• Testing: Maintain labeled test sets, run regression checks whenever detection or policy changes, and test the effect of redaction on output utility.
• Evidence generation: Record the decision, entity types detected, policy version and destination for each request, without storing raw PII in the evidence itself.

Gaps that either option can leave open

The first gap is traffic coverage. A gateway control only applies to what routes through it. Applications calling a model provider directly, developer tools and embedded assistants can sit outside it. NIST also names authentication, API inventory and security configuration as primary AI gateway vulnerabilities, so the control point needs its own hardening and inventory. A custom layer has the same problem at a different level: it covers only the call sites you have wired up.

The second gap is scope within the traffic. Prompts are the obvious target, but model responses and tool-call or agent payloads can carry PII too. Test each direction and payload type separately rather than assuming coverage.

The third gap is evidence. NIST SP 800-239 identifies logging users' queries and API calls, with the corresponding model responses, as a key step for detecting suspicious activity. Those logs can themselves contain sensitive data, so they need privacy-aligned handling. An auditor will want more than a transcript: they will want to see what policy applied to each interaction and what the system did about it. Draft NIST guidance for AI cybersecurity also refers to implementing runtime redaction for sensitive data in AI prompts, which reflects the direction of expectations, though it is draft guidance and not a binding requirement as of November 2026.

Where runtime policy enforcement fits

Trussed addresses the gaps above from a different position. It is not a gateway or a redaction library. It enforces policy in the execution path across models, agents, tools, MCP servers, AI sites and coding assistants, with allow, redact, block, rate limit and escalate-for-approval outcomes applied to individual interactions and tool calls. Policy is enforced at runtime, not reviewed after the fact.

Each enforcement decision produces audit-ready evidence, mapped to HIPAA, NAIC, NYDFS, the EU AI Act and NIST. Trussed deploys self-managed in the customer cloud, with configurable retention and data residency. It has run continuously in a regulated enterprise for more than 18 months, with 99.95% uptime on live production traffic and more than 100 models under one policy plane.

This can complement either option. A gateway sanitizer or custom detector can remain a detection component while enforcement and evidence are applied consistently across the paths that matter to a regulated enterprise. The decision criteria stay the same: coverage of your real traffic, handling of responses and tool calls, and evidence an auditor can use.