> ## Documentation Index
> Fetch the complete documentation index at: https://docs.fiddler.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Guardrails

> Fiddler Guardrails is a powerful solution designed to serve as the first-line of defense to protect enterprises from costly GenAI and LLM risks in real-time environments.

## Overview

Fiddler Guardrails provide real-time protection for GenAI applications—including LLM-powered systems and agentic AI workflows—by detecting and preventing harmful content, PII leaks, and hallucinations before they reach your users. Built on Fiddler Centor Models—Fiddler's proprietary small language models (SLMs)—Guardrails deliver enterprise-grade security with low-latency, high-throughput performance optimized for production environments.

**Use Fiddler Guardrails to:**

* Detect and block harmful or inappropriate content across 11 safety dimensions
* Prevent personally identifiable information (PII) leaks in user inputs and model outputs
* Identify hallucinations in retrieval-augmented generation (RAG) applications
* Protect against prompt injection and jailbreaking attempts

## What Fiddler Guardrails Can Moderate

Fiddler Guardrails are powered by Fiddler Centor Models, and you can apply them to moderate or block three categories of risk:

* **Safety** - Detect harmful, toxic, or jailbreaking content
* **Hallucination (faithfulness)** - Identify hallucinations in RAG applications
* **PII/PHI** - Detect and redact sensitive information

<Info>
  Guardrails are designed for **real-time content blocking** with more sensitive thresholds than enrichments used for monitoring and analytics. See the [Enrichments guide](/observability/llm/enrichments) for batch processing and monitoring use cases.
</Info>

## Getting Started with Fiddler Guardrails

### Prerequisites

* **Fiddler Environment** - Access to a Fiddler environment with Guardrails enabled
* **API Key** - Generate your API key from Settings → [Credentials](/reference/administration/settings#credentials)
* **HTTP Client** - Python 3.8+ with `requests` library, cURL, or any HTTP client

Guardrails can be invoked directly via REST API from any programming language. The examples below demonstrate usage with cURL and Python.

***

## Safety

For safety moderation, Fiddler Guardrails use the Centor Model for Safety, which evaluates the safety of text along eleven different dimensions: `illegal, hateful, harassing, racist, sexist, violent, sexual, harmful, unethical, jailbreaking, roleplaying`.

This model requires a single string input for evaluation and outputs 11 distinct scores (floats between 0 and 1). **Starting in release 26.13, scores are calibrated so that `0.5` is the default decision threshold across all 11 dimensions — a score of 0.5 or above indicates unsafe content.** Lower thresholds increase sensitivity but may over-block; tune the threshold for your data and risk tolerance.

<Info>
  **Threshold Guidance:** Starting in release 26.13, the Centor Model for Safety is calibrated so a single decision threshold of **0.5** applies across all 11 dimensions — no per-dimension tuning required. Lower the threshold to increase sensitivity (at the cost of more false positives), or raise it to reduce false positives. For monitoring use cases with enrichments, see [Safety Enrichment](/observability/llm/enrichments#safety) for monitoring thresholds.
</Info>

<Warning>
  **Migrating from earlier releases:** If you previously used a threshold of `0.1` (calibrated for the older, uncalibrated model), adopt `0.5` for the 26.13 calibrated model — keeping `0.1` will over-block benign content. Choosing a stricter (lower) threshold remains a deliberate option for high-sensitivity use cases.
</Warning>

### Safety Example Code

<Tabs>
  <Tab title="cURL">
    ```bash theme={null}
    curl --location 'https://{fiddler_endpoint}/v3/guardrails/ftl-safety' 
    --header 'Content-Type: application/json' 
    --header 'Authorization: Bearer {token}' 
    --data '{
        "data": {
            "input": "I am a dangerous person who will be wreaking havoc upon the world!!!"
        }
    }'
    ```
  </Tab>

  <Tab title="Python">
    ```python theme={null}
    import requests
    import json

    token = "YOUR_FIDDLER_TOKEN_HERE"
    url = "FIDDLER_ENDPOINT_HERE"

    payload = json.dumps(
        {
            "data": {
                "input": "I am a dangerous person who will be wreaking havoc upon the world!!!"
            }
        }
    )
    headers = {"Content-Type": "application/json", "Authorization": f"Bearer {token}"}

    response = requests.request(
        "POST", f"{url}/v3/guardrails/ftl-safety", headers=headers, data=payload
    )

    print(response.text)
    ```
  </Tab>
</Tabs>

**Sample Response**

```json theme={null}
{
  "fdl_harmful": 0.119,
  "fdl_violent": 0.073,
  "fdl_unethical": 0.043,
  "fdl_illegal": 0.016,
  "fdl_sexual": 0.005,
  "fdl_racist": 0.003,
  "fdl_jailbreaking": 0.002,
  "fdl_harassing": 0.001,
  "fdl_hateful": 0.001,
  "fdl_sexist": 0.001,
  "fdl_roleplaying": 0.051
}
```

**Interpreting Safety Scores:**

Each dimension returns a score between 0 and 1:

* **Closer to 0** - Safe content
* **Closer to 1** - Unsafe content
* **0.5 or above** - Meets or exceeds the calibrated default decision threshold (tunable for your use case)

***

## Hallucination (faithfulness)

For hallucination moderation, Fiddler Guardrails use the Centor Model for Faithfulness, which evaluates the accuracy and reliability of facts presented in AI-generated text responses by comparing them to provided context documents. This model uses `response` and `context` inputs.

<Info>
  **Not to be confused with RAG Faithfulness.** For real-time blocking, Fiddler Guardrails use the Centor Model for Faithfulness (`ftl_response_faithfulness`). RAG Faithfulness is a separate LLM-as-a-Judge evaluator available in Agentic Monitoring and Experiments for diagnostic evaluation. See [RAG Health Diagnostics](/concepts/rag-health-diagnostics) for details.
</Info>

This model requires a response string and contextual documents as input. The model outputs a single faithfulness score (float between 0 and 1). **Set a threshold of \< 0.5 for detection (any value less than 0.5 indicates unfaithful content).**

<Info>
  **Threshold Guidance:** A score closer to **0** means unfaithful (the LLM hallucinated relative to the provided context), while a score closer to **1** means faithful (the LLM output did not hallucinate and is well-grounded in the provided context). For real-time guardrails, a threshold of **0.5** strikes a balance between sensitivity and accuracy.
</Info>

### Faithfulness Example Code

<Tabs>
  <Tab title="cURL">
    ```bash theme={null}
    curl --location 'https://{fiddler_endpoint}/v3/guardrails/ftl-response-faithfulness' 
    --header 'Content-Type: application/json' 
    --header 'Authorization: Bearer {token}' 
    --data '{
        "data": {
          "response": "The Yorkshire Terrier and the Cavalier King Charles Spaniel are both small breeds of companion dogs.",
          "context": "The Yorkshire Terrier is a small dog breed of terrier type, developed during the 19th century in Yorkshire, England, to catch rats in clothing mills.The Cavalier King Charles Spaniel is a small spaniel classed as a toy dog by The Kennel Club and the American Kennel Club"
      }
    }'
    ```
  </Tab>

  <Tab title="Python">
    ```python theme={null}
    import requests
    import json

    token = "YOUR_FIDDLER_TOKEN_HERE"
    url = "FIDDLER_ENDPOINT_HERE"

    payload = json.dumps(
        {
            "data": {
                "response": "The Yorkshire Terrier and the Cavalier King Charles Spaniel are both small breeds of companion dogs.",
                "context": "The Yorkshire Terrier is a small dog breed of terrier type, developed during the 19th century in Yorkshire, England, to catch rats in clothing mills.The Cavalier King Charles Spaniel is a small spaniel classed as a toy dog by The Kennel Club and the American Kennel Club",
            }
        }
    )
    headers = {"Content-Type": "application/json", "Authorization": f"Bearer {token}"}

    response = requests.request(
        "POST",
        f"{url}/v3/guardrails/ftl-response-faithfulness",
        headers=headers,
        data=payload,
    )

    print(response.text)
    ```
  </Tab>
</Tabs>

**Sample Response**

```json theme={null}
{
  "fdl_faithful_score": 0.194
}
```

**Interpreting Faithfulness Scores:**

* **0.0 - 0.49** - Unfaithful (likely hallucination - block or flag for review)
* **0.5 - 1.0** - Faithful (response is well-supported by the provided context)

The example above shows a score of **0.194**, which is **below the 0.5 threshold**, indicating the response may contain hallucinated information not supported by the context.

***

## PII/PHI

For PII/PHI moderation, Fiddler Guardrails use the Centor Model for PII/PHI, which detects, flags, and redacts PII leakage in both user inputs and model responses.

PII/PHI moderation supports a comprehensive set of label types, including: `person, address, email, email address, credit card number, credit card expiration date, cvv, cvc, bank account number, iban, social security number, date of birth, ip address, phone number, mobile phone number, landline phone number, passport number, driver's license number, tax identification number, cpf, cnpj, account number, license plate number, fax number, website, digital signature, postal code`. See the [PII & PHI Tutorial](/developers/tutorials/guardrails/guardrails-pii) for the full entity list.

<Info>
  The Centor Model for PII/PHI supports a different entity set than the [PII Enrichment](/observability/llm/enrichments#personally-identifiable-information) (which uses Presidio). For monitoring and batch processing, see the PII Enrichment documentation.
</Info>

<Info>
  **PHI Detection also supported.** Fiddler Guardrails also detect Protected Health Information (PHI) for HIPAA compliance, including: `medication, medical condition, health insurance number, health insurance id number, national health insurance number, birth certificate number, serial number`. Pass `"entity_categories": "PHI"` in your request body. See the [PII & PHI Tutorial](/developers/tutorials/guardrails/guardrails-pii) for full entity lists and example code.
</Info>

This model accepts a single text string and returns all detected PII spans with their labels, confidence scores, and character offsets.

### PII/PHI Example Code

<Tabs>
  <Tab title="cURL">
    ```bash theme={null}
    curl --location 'https://{fiddler_endpoint}/v3/guardrails/sensitive-information' 
    --header 'Content-Type: application/json' 
    --header 'Authorization: Bearer {token}' 
    --data '{
        "data": {
            "input": "Some of my colleagues share their contact info as well. Jane Smith's email is jane.smith@company.com, and her office is located at 432 Oak Avenue, Suite 210, Chicago, IL 60611. You can call her mobile at 312-555-7890."
        }
    }'
    ```
  </Tab>

  <Tab title="Python">
    ```python theme={null}
    import requests
    import json

    token = "YOUR_FIDDLER_TOKEN_HERE"
    url = "FIDDLER_ENDPOINT_HERE"

    payload = json.dumps(
        {
            "data": {
                "input": "Some of my colleagues share their contact info as well. Jane Smith's email is jane.smith@company.com, and her office is located at 432 Oak Avenue, Suite 210, Chicago, IL 60611. You can call her mobile at 312-555-7890."
            }
        }
    )
    headers = {"Content-Type": "application/json", "Authorization": f"Bearer {token}"}

    response = requests.request(
        "POST", f"{url}/v3/guardrails/sensitive-information", headers=headers, data=payload
    )

    print(response.text)
    ```
  </Tab>
</Tabs>

**Sample Response**

```json theme={null}
{
  "fdl_sensitive_information_scores": [
    {
      "score": 0.987,
      "label": "email",
      "start": 78,
      "end": 100,
      "text": "jane.smith@company.com"
    },
    {
      "score": 0.945,
      "label": "address",
      "start": 131,
      "end": 175,
      "text": "432 Oak Avenue, Suite 210, Chicago, IL 60611"
    },
    {
      "score": 0.987,
      "label": "mobile phone number",
      "start": 204,
      "end": 216,
      "text": "312-555-7890"
    }
  ]
}
```

**Response Fields:**

* `score` - Confidence score (0.0 to 1.0)
* `label` - Entity type (e.g., "email", "social security number")
* `text` - The detected sensitive information
* `start` / `end` - Character positions in the input text

***

## Summary

Fiddler Guardrails provide real-time protection for GenAI applications, powered by Fiddler Centor Models, across three categories of risk:

* **Safety** - Detect harmful content across 11 safety dimensions with a calibrated default decision threshold of 0.5 (tunable)
* **Hallucination (faithfulness)** - Identify hallucinations in RAG applications with a recommended threshold of \< 0.5
* **PII/PHI** - Detect and redact PII and PHI across a comprehensive set of entity types

All guardrails use Fiddler Centor Models—Fiddler's proprietary small language models—optimized for sub-second latency in production environments.

## Next Steps

* **Quick Start** - [Get started with Fiddler Guardrails in 15 minutes](/developers/quick-starts/guardrails-quick-start)
* **API Reference** - [Complete Guardrails API documentation](/sdk-api/guardrails-api-reference)
* **Tutorials** - Explore detailed tutorials for [Safety](/developers/tutorials/guardrails/guardrails-safety), [PII](/developers/tutorials/guardrails/guardrails-pii), and [Faithfulness](/developers/tutorials/guardrails/guardrails-faithfulness)
* **Concepts** - [Understand Fiddler Centor Models and enrichments](/observability/llm/enrichments)
* **Monitoring** - [Integrate guardrails with LLM monitoring](/observability/llm/enrichments#safety)