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Create Guardrail Config

A guardrail configuration determines which guardrails are enforced for each request by specifying matching criteria in the when block. The when block allows you to target specific models, users, or request metadata, ensuring that the appropriate guardrails are applied only to relevant requests. You can create a guardrail config by navigating to the AI Gateway -> Config Tab -> Guardrails Section -> Create/Edit Guardrail Config.
TrueFoundry interface showing YAML editor for creating guardrail configuration

Create/Edit Guardrail Config

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Config

The guardrails configuration contains an array of rules that are evaluated for each request. Only the first matching guardrail rule is applied to that request. Each rule can specify input and output guardrails that will be applied. Let’s take a look at a sample configuration first.

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Example Configuration

name: guardrails-config
type: gateway-guardrails-config
rules:
  - id: demo-guardrail
    when:
      subjects:
        - user:john@example.com
    input_guardrails: 
      - truefoundry:guardrail-config-group:demo-guardrail-group:guardrail-config:open-ai-guardrail
    output_guardrails: []

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Explanation of the Example Configuration

Let’s break down the sample guardrail configuration shown above:
  • name:
    This is the identifier for your guardrail configuration. You can set it to any string that helps you recognize the config in logs or the UI. In the example, it’s set to guardrails-config.
  • type:
    This must always be gateway-guardrails-config. It tells the TrueFoundry platform that this YAML file defines a guardrail configuration.
  • rules:
    This is a list of rule objects. Each rule determines exactly which requests it will match and what guardrails will be enforced:
    • id:
      A unique string to identify the rule. This helps you track which rule matched a request in logs or for future updates.
    • when:
      This block specifies the precise criteria a request must meet for the rule to apply. The rule will match a request if all of the following (if present) are satisfied:
      • subjects:
        An array of user, team, or virtual account identifiers. For example, user:john@example.com matches requests made by that user.
        You can also use team:team-name or virtualaccount:account-name to match requests from a specific team or virtual account.
      • models (optional):
        An array of model names. If provided, the rule only matches requests targeting these models.
      • metadata (optional):
        Key-value pairs to match against request metadata. For example, metadata: { environment: "production" } matches only if the request includes the header X-TFY-METADATA with environment=production.
      • If when is {} (empty), the rule matches all requests. In summary:
        In the sample config, this rule will match requests made by the user john@example.com because the when block specifies subjects: [user:john@example.com]. If you add models or metadata fields, the rule will only match requests that satisfy all specified conditions. If a field is omitted, it is not used for filtering.
    • input_guardrails:
      An array of guardrail integration selectors to apply to the request input (prompt) before it is sent to the LLM. Each selector references a specific guardrail integration you have configured.
    • output_guardrails:
      An array of guardrail integration selectors to apply to the LLM’s response after it is received.
How it works:
  • The rules are evaluated in order. Only the first rule that matches a request is applied; subsequent rules are ignored for that request.
  • Each rule can target specific users, teams, models, or metadata, and can enforce different input and output guardrails as needed.
Order your rules with the most specific at the top and the most generic at the bottom to ensure that specialized guardrails are prioritized and general rules serve as a fallback.

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How to get the selector of guardrail integrations

You can get the selector of guardrail integrations by navigating to the Guardrail tab on AI Gateway and clicking on the “Copy FQN” button next to the guardrail integration.
Guardrail integration interface showing Copy FQN button to obtain guardrail selector

Copy Guardrail Selector

Once you submit the config, you can make LLM requests either from the Playground or your code. If the requests match the rule, the guardrails will be applied to the request.