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Guardrails in the AI Gateway provide a mechanism to ensure safety, quality, and compliance by validating and transforming both the requests sent to and the responses received from Large Language Models (LLMs). This document outlines the configuration and application of guardrails within the TrueFoundry AI Gateway. Guardrails can be applied at two key stages in the AI Gateway workflow:
  1. Before Sending the Request to the LLM: These guardrails are called Input Guardrails and are applied to the request before it is sent to the LLM. Common usecases include:
    • Masking or redacting PII from the request,
    • Filtering or blocking requests containing prohibited topics or sensitive content
    • Validating input format and structure to prevent prompt injection or malformed data.
  2. After Receiving the Response from the LLM: These guardrails are called Output Guardrails and are applied to the response after it is received from the LLM. Common usecases include:
    • Filtering or blocking responses containing prohibited topics or sensitive content
    • Validating output format and structure to prevent hallucination or non-compliant data.
Guardrails can operate in two modes:
  1. Block / Validate request or response: In this mode, the guardrail checks either the request sent to the AI Gateway or the response received from the LLM against defined rules and if the rule is violated, the data is blocked and an error is returned to the client. This mode is called validate and is used to strictly enforce compliance and prevent unsafe or non-compliant data from being processed or returned at any stage.
  2. Modify / Mutate request or response: In this mode, the guardrail modifies the request before it is sent to the model, or alters the response after it is received from the LLM. For example, mutation can redact sensitive information, reformat data, or adjust content to meet policy requirements. This mode is called mutate in the Truefoundry platform.
Diagram showing the flow of requests and responses through input and output guardrails in the AI Gateway

Flow chart of how guardrails work in the AI Gateway

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Controlling Guardrails Scope

By default, guardrails evaluate all messages in a conversation. You can control this behavior using the X-TFY-GUARDRAILS-SCOPE header:
  • all (default): Evaluates all messages in the conversation history
  • last: Evaluates only the most recent message
Example: 
curl -X POST "https://{controlPlaneURL}/chat/completions"
  -H "Authorization: Bearer YOUR_API_KEY"
  -H "Content-Type: application/json"
  -H 'X-TFY-GUARDRAILS: {"input_guardrails":["azure-pii/redaction"],"output_guardrails":["fiddler/safety"]}'
  -H "X-TFY-GUARDRAILS-SCOPE: last"
  -d '{
    "model": "openai/gpt-4o",
    "messages": [
      {"role": "system", "content": "You are a helpful assistant."},
      {"role": "user", "content": "Hello, how can you help me today?"}
    ]
  }'
Use last for better performance when you only need to validate the latest message. Use all when you need to check the entire conversation context.

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Error Handling

If a guardrail service experiences an error (API timeout, 5xx errors, network issues), the gateway continues processing your request by default. This ensures your application remains available even if a guardrail provider has issues.
Guardrail API errors do not block requests. Your LLM calls will complete successfully even if guardrail checks fail to execute.

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Guardrail Integrations

Truefoundry Gateway doesn’t provide any guardrails of its own. It instead integrates with the popular guardrail providers mentioned below to provide a unified interface for guardrail management and configuration.
In case you don’t see the provider you are looking for, please reach out to us and we will be happy to add the integration.

OpenAI Moderations

Integrate with OpenAI’s moderation API to detect and handle content that may violate usage policies, like violence, hate speech, or harassment.

AWS Bedrock Guardrail

Integrate with AWS Bedrock’s capabilities to apply guardrails on AI models.

Azure PII

Integrate with Azure’s PII detection service to identify and redact PII data in both requests and responses.

Azure Content Safety

Leverage Azure Content Safety to detect and mitigate harmful, unsafe, or inappropriate content in model inputs and outputs.

Enkrypt AI

Integrate with Enkrypt AI for advanced moderation and compliance, detecting risks like toxicity, bias, and sensitive data exposure.

Palo Alto Prisma AIRS

Integrate with Palo Alto AI Risk to detect and mitigate harmful, unsafe, or inappropriate content in model inputs and outputs.

PromptFoo

Integrate with Promptfoo to apply guardrails like content moderation on the models.

Fiddler

Integrate with Fiddler to apply guardrails, such as Fiddler-Safety and Fiddler-Response-Faithfulness on the models.

Bring Your Own Guardrail

Integrate your own custom guardrail using frameworks like Guardrails.AI or a python function.

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Configure Guardrails

Guardrails cane be configured both at a per request level and at the central gateway layer. To configure at the per request level, you can use the X-TFY-GUARDRAILS header. You can copy the code from the Playground Section. To configure at the gateway layer, you will need to create a YAML file that which guardrails need to be applied to which subset of the requests. You can decide the subset of requests based on the user making the request, model or any other metadata in the request. You can read about it more in the Configure Guardrails section.
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