PII - Fiddler Documentation

Get your sensitive information detection running in minutes with Fiddler’s Centor PII Guardrails. This guide walks you through detecting PII, PHI, and custom entities to protect sensitive data across your applications.

What You’ll Build

In this quick start, you’ll implement a sensitive information detection system that:

Detects a comprehensive set of personally identifiable information (PII) entity types
Identifies protected health information (PHI) entity types for healthcare compliance
Configures custom entity detection for organization-specific data
Provides real-time detection with sub-second latency

Interactive TutorialFor more advanced examples, including batch processing, performance optimization, and production deployment patterns:Open the Complete Sensitive Information Guardrail Notebook in Google Colab →Or download the notebook from GitHub →

Prerequisites

Fiddler account with access token
Python 3.10+ environment
Basic understanding of data privacy concepts

Overview

Fiddler’s Centor PII and PHI detection provides enterprise-grade protection against data leakage by automatically detecting sensitive information across multiple categories. These guardrails integrate seamlessly with Fiddler’s AI Observability platform, enabling continuous monitoring and automated compliance reporting.

Key Capabilities

PII Detection: a comprehensive set of entity types, including names, addresses, SSN, credit cards, emails, and phone numbers
PHI Detection: healthcare-specific entity types for HIPAA compliance
Custom Entities: Define organization-specific sensitive data patterns
Real-time Processing: Sub-second latency for production applications

Set Up Your Environment

Connect to Fiddler and configure the Sensitive Information Guardrail API:

import json
import pandas as pd
import requests
import time
import fiddler as fdl

# Replace with your actual values
URL = 'https://your_company.fiddler.ai'
TOKEN = 'your_token_here'

# API Configuration
SENSITIVE_INFORMATION_URL = f"{URL}/v3/guardrails/sensitive-information"
FIDDLER_HEADERS = {
    "Authorization": f"Bearer {TOKEN}",
    "Content-Type": "application/json",
}

# Connect to Fiddler
fdl.init(url=URL, token=TOKEN)
print("✅ Connected to Fiddler successfully!")

Define Helper Functions

Create reusable functions for interacting with the API:

def get_sensitive_information_response(
    text: str,
    entity_categories: str | list[str] = 'PII',
    custom_entities: list[str] = None,
):
    """
    Detect sensitive information in text.

    Args:
        text: Input text to analyze
        entity_categories: 'PII', 'PHI', 'Custom Entities', or list
        custom_entities: Custom entity patterns (when using 'Custom Entities')

    Returns:
        Tuple of (API response dict, latency in seconds)
    """
    data = {'input': text}

    # Add entity configuration if specified
    if entity_categories != 'PII' or custom_entities:
        data['entity_categories'] = entity_categories
        if custom_entities:
            data['custom_entities'] = custom_entities

    start_time = time.monotonic()

    try:
        response = requests.post(
            SENSITIVE_INFORMATION_URL,
            headers=FIDDLER_HEADERS,
            json={'data': data},
        )
        response.raise_for_status()
        return response.json(), (time.monotonic() - start_time)

    except requests.exceptions.RequestException as e:
        print(f'❌ API call failed: {e}')
        return {}, (time.monotonic() - start_time)

def print_detection_results(response, latency):
    """Display detection results in a formatted way."""
    entities = response.get('fdl_sensitive_information_scores', [])

    print(f"\n🔍 Detection Results (⏱️ {latency:.3f}s)")
    print(f"📊 Total Entities Found: {len(entities)}\n")

    if not entities:
        print("✅ No sensitive information detected.")
        return

    # Group by entity type
    by_type = {}
    for entity in entities:
        label = entity.get('label', 'unknown')
        if label not in by_type:
            by_type[label] = []
        by_type[label].append(entity)

    # Display grouped results
    for label, group in sorted(by_type.items()):
        print(f"🏷️  {label.upper()} ({len(group)} found):")
        for entity in group:
            print(f"   • '{entity['text']}' (confidence: {entity['score']:.3f})")
        print()

Example 1: PII Detection

Detect common personally identifiable information:

# Sample text with various PII types
sample_text = """
I'm John Doe and I live at 1234 Maple Street, Springfield, IL 62704.
You can reach me at john.doe@email.com or call me at (217) 555-1234.
My social security number is 123-45-6789, and I was born on January 15, 1987.
My credit card number is 4111 1111 1111 1111 with CVV 123.
"""

print("🧪 Testing PII Detection")
print("📄 Input Text:")
print(sample_text)

# Call the API with default PII configuration
response, latency = get_sensitive_information_response(sample_text)

# Display results
print_detection_results(response, latency)

Expected Output:

🔍 Detection Results (⏱️ 0.125s)
📊 Total Entities Found: 8

🏷️  PERSON (1 found):
   • 'John Doe' (confidence: 0.987)

🏷️  ADDRESS (1 found):
   • '1234 Maple Street, Springfield, IL 62704' (confidence: 0.945)

🏷️  EMAIL (1 found):
   • 'john.doe@email.com' (confidence: 0.998)

🏷️  PHONE NUMBER (1 found):
   • '(217) 555-1234' (confidence: 0.976)

🏷️  SOCIAL SECURITY NUMBER (1 found):
   • '123-45-6789' (confidence: 0.991)

🏷️  CREDIT CARD NUMBER (1 found):
   • '4111 1111 1111 1111' (confidence: 0.989)

🏷️  CVV (1 found):
   • '123' (confidence: 0.892)

🏷️  DATE OF BIRTH (1 found):
   • 'January 15, 1987' (confidence: 0.923)

Example 2: PHI Detection for Healthcare

Detect protected health information in medical contexts:

# Sample text with PHI information
healthcare_text = """
Patient report: John Smith was prescribed metformin for his diabetes condition.
His health insurance number is HI-987654321, and medical record shows
serial number MED-2024-001 for his glucose monitor device.
Birth certificate number is BC-IL-1987-001234.
Current medication includes aspirin and lisinopril for blood pressure management.
"""

print("🏥 Testing PHI Detection for Healthcare Data")
print("📄 Input Text:")
print(healthcare_text)

# Call the API with PHI configuration
response, latency = get_sensitive_information_response(
    healthcare_text,
    entity_categories="PHI"
)

# Display results
print_detection_results(response, latency)

Expected Output:

🔍 Detection Results (⏱️ 0.098s)
📊 Total Entities Found: 5

🏷️  PERSON (1 found):
   • 'John Smith' (confidence: 0.976)

🏷️  MEDICATION (3 found):
   • 'metformin' (confidence: 0.945)
   • 'aspirin' (confidence: 0.932)
   • 'lisinopril' (confidence: 0.928)

🏷️  HEALTH INSURANCE NUMBER (1 found):
   • 'HI-987654321' (confidence: 0.887)

Example 4: Custom Entity Detection

Define and detect organization-specific sensitive data:

# Sample text with custom entities
custom_text = """
Employee ID: EMP-2024-001, Badge Number: BD-789456
Project code: PROJ-AI-2024, Server hostname: srv-prod-01
API key: sk-abc123xyz789
Internal ticket: TICK-2024-5678
"""

# Define custom entities for your organization
custom_entities = [
    'employee id',
    'badge number',
    'project code',
    'api key',
    'server hostname',
    'ticket number'
]

print("🎯 Testing Custom Entity Detection")
print(f"🏷️ Custom entities: {custom_entities}")

# Call the API with custom entity configuration
response, latency = get_sensitive_information_response(
    custom_text,
    entity_categories='Custom Entities',
    custom_entities=custom_entities
)

# Display results
print_detection_results(response, latency)

Expected Output:

🔍 Detection Results (⏱️ 0.112s)
📊 Total Entities Found: 6

🏷️  EMPLOYEE ID (1 found):
   • 'EMP-2024-001' (confidence: 0.923)

🏷️  BADGE NUMBER (1 found):
   • 'BD-789456' (confidence: 0.911)

🏷️  PROJECT CODE (1 found):
   • 'PROJ-AI-2024' (confidence: 0.897)

🏷️  API KEY (1 found):
   • 'sk-abc123xyz789' (confidence: 0.945)

🏷️  SERVER HOSTNAME (1 found):
   • 'srv-prod-01' (confidence: 0.878)

🏷️  TICKET NUMBER (1 found):
   • 'TICK-2024-5678' (confidence: 0.902)

API Reference

Endpoint

POST /v3/guardrails/sensitive-information

Request Format

{
  "data": {
    "input": "Text to analyze for sensitive information",
    "entity_categories": "PII" | "PHI" | "Custom Entities" | ["PII", "PHI"],
    "custom_entities": ["api key", "employee id", "custom pattern"]
  }
}

Request Parameters

Parameter	Type	Description	Default
`input`	string	Text to analyze for sensitive information	Required
`entity_categories`	string or array	Detection mode(s) to use	”PII”
`custom_entities`	array	Custom entity patterns (required when using “Custom Entities”)	None

Response Format

{
  "fdl_sensitive_information_scores": [
    {
      "score": 0.987,
      "label": "email",
      "start": 78,
      "end": 100,
      "text": "jane.smith@company.com"
    }
  ]
}

Response Fields

Field	Type	Description
`score`	float	Confidence score (0.0 to 1.0)
`label`	string	Entity type identifier
`start`	integer	Character position where entity starts
`end`	integer	Character position where entity ends
`text`	string	The detected entity text

Supported Entity Types

PII Entities

The Centor PII model returns labels as lowercase strings — use these exact values when filtering responses.

Personal: person, date of birth
Contact: email, email address, phone number, mobile phone number, landline phone number, fax number, address, postal code
Financial: credit card number, credit card expiration date, cvv, cvc, bank account number, account number, iban
Government IDs: social security number, passport number, driver’s license number, tax identification number, license plate number, cpf, cnpj
Digital: ip address, digital signature, website

PHI Entities

Medical: medication, medical condition
Insurance: health insurance number, health insurance id number, national health insurance number
Identifiers: birth certificate number, serial number

Code Examples

Python - Requests
Python - Error Handling
cURL

import requests

url = "https://your_company.fiddler.ai/v3/guardrails/sensitive-information"
headers = {
    "Authorization": "Bearer YOUR_TOKEN",
    "Content-Type": "application/json"
}

payload = {
    "data": {
        "input": "Contact John at john@email.com",
        "entity_categories": "PII"
    }
}

response = requests.post(url, json=payload, headers=headers)
entities = response.json().get("fdl_sensitive_information_scores", [])

for entity in entities:
    print(f"Found {entity['label']}: {entity['text']} (confidence: {entity['score']})")

def safe_detect_pii(text):
    """Detect PII with proper error handling."""
    try:
        response = requests.post(
            SENSITIVE_INFORMATION_URL,
            headers=FIDDLER_HEADERS,
            json={'data': {'input': text}},
            timeout=10
        )
        response.raise_for_status()
        return response.json()

    except requests.exceptions.Timeout:
        print("Request timed out. Try again or check your connection.")
        return None

    except requests.exceptions.HTTPError as e:
        if e.response.status_code == 401:
            print("Authentication failed. Check your API token.")
        elif e.response.status_code == 429:
            print("Rate limit exceeded. Wait before retrying.")
        else:
            print(f"HTTP error {e.response.status_code}: {e.response.text}")
        return None

    except Exception as e:
        print(f"Unexpected error: {e}")
        return None

curl -X POST 'https://your_company.fiddler.ai/v3/guardrails/sensitive-information' 
  -H 'Content-Type: application/json' 
  -H 'Authorization: Bearer YOUR_TOKEN' 
  -d '{
    "data": {
      "input": "My SSN is 123-45-6789",
      "entity_categories": "PII"
    }
  }'

Next Steps

After completing this quick start:

Explore other Fiddler guardrails for comprehensive AI safety
Review the complete guardrails documentation for all available guardrail types
Integrate guardrails into your applications for production use

Summary

You’ve learned how to:

✅ Detect a comprehensive set of PII entity types in text data
✅ Identify PHI for healthcare compliance
✅ Configure custom entities for your organization
✅ Integrate the Centor PII Guardrails API into your applications.

The Centor PII Guardrails offer enterprise-grade protection for sensitive information with sub-second latency, making them ideal for real-time applications while ensuring compliance with privacy regulations such as GDPR, HIPAA, and CCPA.

​What You’ll Build

​Prerequisites

​Overview

​Key Capabilities

​API Reference

​Endpoint

​Request Format

​Request Parameters

​Response Format

​Response Fields

​Supported Entity Types

​PII Entities

​PHI Entities

​Code Examples

​Next Steps

​Summary

What You’ll Build

Prerequisites

Overview

Key Capabilities

API Reference

Endpoint

Request Format

Request Parameters

Response Format

Response Fields

Supported Entity Types

PII Entities

PHI Entities

Code Examples

Next Steps

Summary