About guardrails

Protect LLM requests and responses from sensitive data exposure and harmful content using layered content safety and guardrail controls (PII detection, DLP).

Protect LLM requests and responses from sensitive data exposure and harmful content using layered content safety controls.

About

In agentgateway, you can use guardrails to help prevent sensitive information from reaching LLM providers and block harmful content in both requests and responses. Guardrails broadly cover a range of content safety techniques including personally identifiable information (PII) detection, PII sanitization, data loss prevention, prompt guards, and other guardrail features.

You can layer multiple protection mechanisms to create comprehensive guardrail protection:

Regex-based filters: Fast, deterministic matching for known patterns like credit cards, SSNs, emails, and custom patterns
External moderation: Leverage built-in model moderation endpoints and cloud provider-specific guardrails for advanced content filtering
Custom webhooks: Integrate your own guardrail logic for specialized requirements

How guardrails works

Agentgateway checks for content safety in the request and response paths. You can configure multiple prompt guards that run in sequence, allowing you to combine different detection methods.

    sequenceDiagram
    participant Client
    participant Gateway as Agentgateway
    participant Guard as Guardrail
    participant LLM

    Client->>Gateway: Send prompt
    Gateway->>Guard: 1. Regex check (fast)
    Guard-->>Gateway: Pass/Reject/Mask

    alt Passed Regex
        Gateway->>Guard: 2. External moderation (if configured)
        Guard-->>Gateway: Pass/Reject/Mask

        alt Passed Moderation
            Gateway->>Guard: 3. Custom webhook (if configured)
            Guard-->>Gateway: Pass/Reject/Mask

            alt Passed All Guards
                Gateway->>LLM: Forward sanitized request
                LLM-->>Gateway: Generate response
                Gateway->>Guard: Response guards
                Guard-->>Gateway: Pass/Reject/Mask
                Gateway-->>Client: Return sanitized response
            end
        end
    else Rejected
        Gateway-->>Client: Return rejection message
    end

The diagram shows content flowing through multiple guard layers. Each layer can:

Pass: Allow content to proceed to the next layer
Reject: Block the request and return an error message
Mask: Replace sensitive patterns with placeholders and continue
Audit: Record what the guard detected, and let the content continue unchanged

Every action is available on the request path and the response path. A response guard can reject a response as well as mask it.

Possible actions

The values that action takes depend on the guard, because a regex guard can mask content and an external guard cannot.

Guard	`action` values	Default
`regex`	`Mask`, `Reject`, `Audit`	`Mask`
`openAIModeration`	`Reject`, `Audit`	`Reject`
`webhook`	`Reject`, `Audit`	`Reject`
`bedrockGuardrails`	`Reject`, `Audit`	`Reject`
`googleModelArmor`	`Reject`, `Audit`	`Reject`

Audit mode

By default, a guard enforces the verdict that it reaches. A regex guard masks the content that matches, and an external guard rejects the request that its provider flags. Set action: Audit to make a guard observe instead. The guard still runs, and it still records what it detected in metrics and in the structured access log, but the content always passes through unchanged.

Audit mode is how you measure a guard before you enforce it. Start a new guard in audit mode, review what it flags in real traffic, tune the patterns or the provider policy, then change the action to enforce the verdict.

The following policy runs a credit card detector and OpenAI moderation, both in audit mode, so that the gateway logs what each guard finds and then forwards the request.

Note

Audit mode changes only whether the gateway acts on the verdict. The guard still calls its provider, so an external guard in audit mode adds the same latency and the same provider cost as an enforcing guard.

kubectl apply -f - <<EOF
apiVersion: agentgateway.dev/v1alpha1
kind: AgentgatewayPolicy
metadata:
  name: openai-guardrails-audit
  namespace: agentgateway-system
spec:
  targetRefs:
  - group: gateway.networking.k8s.io
    kind: HTTPRoute
    name: openai
  backend:
    ai:
      promptGuard:
        request:
        - regex:
            action: Audit
            builtins:
            - CreditCard
        - openAIModeration:
            action: Audit
EOF

Guard scope

A request guard does not inspect the whole request. By default, a guard reads the system prompt and the text of regular user and assistant messages. Tool call content is left alone, so a Social Security number that a tool returns to the model reaches the provider unmasked.

Set the scope field on a request guard to choose what the guard reads.

spec:
  backend:
    ai:
      promptGuard:
        request:
        - scope:
          - SystemPrompt
          - Messages
          - ToolOutput
          regex:
            action: Mask
            builtins:
            - Ssn

Value	What the guard reads
`SystemPrompt`	The system or developer prompt.
`Messages`	Regular user and assistant message text.
`ToolInput`	Tool call arguments, which the model usually produces.
`ToolOutput`	Tool call results that are fed back to the model.

Warning

A scope replaces the default, it does not add to it. A guard with scope: [ToolOutput] reads tool results and stops reading messages, so content that the guard used to catch passes through. To cover messages and tool results with one guard, list both values.

Four rules govern the field:

Omit scope to keep the default. The default is SystemPrompt and Messages. The field takes 1 to 4 values, so an empty list is rejected.
Only the regex and bedrockGuardrails guards accept a scope other than the default. Any other guard type is rejected with only regex and bedrockGuardrails guards support a non-default scope. Other guard types always read the default.
The field applies to request guards only. A response guard has no scope.
Masking ToolInput can produce invalid JSON. In APIs that carry tool arguments as opaque JSON, such as chat completions, the whole argument string is treated as one piece of text. A rule that matches across the JSON punctuation rewrites the arguments into something the provider cannot parse. Prefer ToolOutput, or write a ToolInput pattern that matches only a value.

For a worked example, see Regex filters.

Streaming guardrails

By default, guardrails run only on buffered LLM traffic. When a client sets "stream": true, the LLM response is streamed to the client, and response guards do not run at all.

To run guardrails on streamed content, set the promptGuard.streaming field to Enabled, as shown in the following example.

apiVersion: agentgateway.dev/v1alpha1
kind: AgentgatewayPolicy
metadata:
  name: openai-prompt-guard
  namespace: agentgateway-system
spec:
  targetRefs:
  - group: gateway.networking.k8s.io
    kind: HTTPRoute
    name: openai
  backend:
    ai:
      promptGuard:
        streaming: Enabled
        response:
        - regex:
            builtins:
            - Email
            action: Reject

Value	Description
`Disabled`	The default. Guardrails run only on buffered LLM traffic.
`Enabled`	Guardrails also run on streamed content, including server-sent events (SSE) responses and OpenAI Realtime WebSocket messages.

Warning

The Mask action does not apply to a streamed response. Agentgateway can block a streamed response, but it cannot rewrite content that is already on its way to the client. A response guard that matches content and uses action: Mask passes that content through unmodified. The client receives no error, and the proxy records no guardrail event. The same limit applies to every response guard that modifies content, such as a webhook guard that returns a mask action, or AWS Bedrock Guardrails anonymization.

To protect a streamed response, use action: Reject. When the guard matches, agentgateway ends the stream and sends a guardrail_blocked error event to the client.

Request guards do not have this limit. A request is buffered before it reaches the LLM provider, so both Mask and Reject apply to requests whether or not the client asks for a streamed response.

Choosing the right approach

Use this table to decide which guardrail layer to use for your requirements:

Requirement	Recommended Approach	Reason
Detect known PII formats (SSN, credit cards, emails)	Regex with builtins	Fast, deterministic, no external dependencies
Block hate speech, violence, harmful content	External moderation (OpenAI, Bedrock)	ML-based detection trained for content safety
Organization-specific restricted terms	Regex with custom patterns	Simple pattern matching for known strings
Named entity recognition (people, orgs, places)	Custom webhook	Requires NER models not available in built-in options
HIPAA, PCI-DSS, or other compliance requirements	Layered approach	Combine regex + external moderation + custom validation
Jailbreak - DAN & Role Hijacking	Regex with custom patterns	Pattern-match known jailbreak phrases and role-injection strings before they reach the LLM
Credentials & Secrets (API keys, tokens, passwords)	Regex with custom patterns	Deterministic pattern matching for structured credential formats with no external dependencies
System prompt extraction	Regex with custom patterns	Detect phrases that attempt to reveal or override system instructions before the request is forwarded
Encoding Evasion & Delimiter Injection	Regex with custom patterns	Match encoded or delimiter-based bypass patterns to block evasion attempts early in the pipeline
Integration with existing DLP tools	Custom webhook	Allows reuse of existing security infrastructure
Fastest performance with minimal latency	Regex only	No external API calls
Most comprehensive protection	All three layers	Defense-in-depth with multiple detection methods

Performance considerations

Each content safety layer adds latency to requests. Plan your configuration accordingly:

Regex guards: < 1ms per check, negligible latency impact
External moderation: 50-200ms depending on provider and network latency
Custom webhooks: Varies based on webhook implementation and location

To optimize performance:

Use regex for fast, deterministic checks before slower external checks
Deploy webhook servers in the same region as agentgateway
Configure appropriate timeouts for external moderation endpoints
Consider request size limits to avoid processing very large prompts

Next steps

Check out the following guides to build your guardrail system.

Regex filtersUse custom regex patterns and built-in PII detectors to filter LLM requests and responses. OpenAI moderationUse the OpenAI Moderation API to detect harmful content across categories including hate, harassment, and violence. AWS Bedrock GuardrailsApply AWS Bedrock Guardrails to filter LLM requests and responses for policy-violating content. Google Model ArmorApply Google Cloud Model Armor templates to sanitize LLM requests and responses. Custom webhooksIntegrate your own content safety logic by forwarding requests and responses to a custom webhook. Multi-layered guardrailsRun prompt guards in sequence, creating defense-in-depth protection.

To track guardrails and content safety, see the following guide.

Observe LLM trafficTrack content safety metrics and blocked requests.

Regex filters

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