# DLP at an AI gateway: reducing sensitive data exposure

Design AI gateway DLP around outgoing fields, a narrow policy, inspected upstream payloads and explicit behavior when the check fails.

Author: Retrivium team
Updated: 2026-11-01
Canonical URL: https://retrivium.ai/journal/ai-gateway-dlp/
Language: en

DLP at an AI gateway helps control particular information sent to models. Start by defining which data must not leave and where it must be blocked or transformed. There is no universal switch that recognizes every secret and repairs every application path. Test a narrow policy against the actual outgoing request.

## Map outgoing data fields

Include system messages, conversation history, retrieved documents, tool results and metadata. The visible user question may be only part of what the application transmits. Identify every external participant and the point before which a restriction must apply.

A pre-call check can affect input before the relevant model request. Reviewing logs afterwards can support detection but does not undo the transmission. Confirm the integration's supported execution mode instead of assuming that any enabled guardrail is preventive. Preserve safe decision metadata without creating another unnecessary copy of private input.

## Define one synthetic test class

Choose an artificial secret format or internal identifier, then specify the expected action: block, replace with a marker or allow. Use the documented filter patterns and actions for the selected mode. A known test string makes behavior verifiable without exposing production information.

Include an ordinary allowed message and a similar-looking value that should not match. False positives matter because they can make useful tasks fail or encourage operators to disable a control. Keep the policy version, expected decisions and test dataset together for later changes.

## Compare blocking, masking and observation

| Mode | Useful outcome | Remaining check |
| --- | --- | --- |
| Blocking | The request is not executed under the rule | Clear refusal and no unnecessary retries |
| Masking | Upstream input contains substituted fields | The task remains useful |
| Observation | An event exists for analysis | Data may already have left |

Inspect the upstream payload in an authorized neutral test. A masked interface display does not prove the original value was removed from the transmitted body. Likewise, a log event confirms observation only at the point where it was generated.

## Test unavailable checks and operational impact

Define what happens if the DLP dependency times out, errors or returns an unclear result. A mandatory boundary should not silently disappear. Test streaming and tool-result paths separately when they are part of the application, because a basic text request covers only one route.

Record latency, false matches and the effect on task quality. For equipment owners and operations funders, these are pipeline requirements alongside compute and storage. A local GPU or gateway filter alone does not establish universal confidentiality or compliance.

Official references: [docs.litellm.ai — quick start](https://docs.litellm.ai/docs/proxy/guardrails/quick_start), [docs.litellm.ai — litellm content filter](https://docs.litellm.ai/docs/proxy/guardrails/litellm_content_filter).

Related guides: [LiteLLM guardrails: choosing where to inspect requests and responses](/journal/litellm-guardrails-boundary/), [Preparing LiteLLM UI for model and key management](/journal/litellm-ui-setup/).