PII Detection Guardrail

This example demonstrates how to use Agno’s built-in PII detection guardrail to protect sensitive data like SSNs, credit cards, emails, and phone numbers.

This example demonstrates how to use Agno’s built-in PII detection guardrail with an Agent.

This example shows how to:

1. Detect and block personally identifiable information (PII) in input
2. Protect sensitive data like SSNs, credit cards, emails, and phone numbers
3. Handle different types of PII violations with appropriate error messages

1. Create a Python file

   pii_detection.py

   import asyncio
   
   from agno.agent import Agent
   from agno.exceptions import InputCheckError
   from agno.guardrails import PIIDetectionGuardrail
   from agno.models.openai import OpenAIResponses
   
   
   async def main():
       """Demonstrate PII detection guardrails functionality."""
       print("🛡️ PII Detection Guardrails Demo")
       print("=" * 50)
   
       # Create an agent with PII detection protection
       agent = Agent(
           name="Privacy-Protected Agent",
           model=OpenAIResponses(id="gpt-5.2"),
           pre_hooks=[PIIDetectionGuardrail()],
           description="An agent that helps with customer service while protecting privacy.",
           instructions="You are a helpful customer service assistant. Always protect user privacy and handle sensitive information appropriately.",
       )
   
       # Test 1: Normal request without PII (should work)
       print("\n✅ Test 1: Normal request without PII")
       print("-" * 30)
       try:
           agent.print_response(
               input="Can you help me understand your return policy?",
           )
           print("✅ Normal request processed successfully")
       except InputCheckError as e:
           print(f"❌ Unexpected error: {e}")
   
       # Test 2: Request with SSN (should be blocked)
       print("\n🔴 Test 2: Input containing SSN")
       print("-" * 30)
       try:
           agent.print_response(
               input="Hi, my Social Security Number is 123-45-6789. Can you help me with my account?",
           )
           print("⚠️ This should have been blocked!")
       except InputCheckError as e:
           print(f"✅ PII blocked: {e.message}")
           print(f"   Trigger: {e.check_trigger}")
   
       # Test 3: Request with credit card (should be blocked)
       print("\n🔴 Test 3: Input containing credit card")
       print("-" * 30)
       try:
           agent.print_response(
               input="I'd like to update my payment method. My new card number is 4532 1234 5678 9012.",
           )
           print("⚠️ This should have been blocked!")
       except InputCheckError as e:
           print(f"✅ PII blocked: {e.message}")
           print(f"   Trigger: {e.check_trigger}")
   
       # Test 4: Request with email address (should be blocked)
       print("\n🔴 Test 4: Input containing email address")
       print("-" * 30)
       try:
           agent.print_response(
               input="Please send the receipt to [e-mail] for my recent purchase.",
           )
           print("⚠️ This should have been blocked!")
       except InputCheckError as e:
           print(f"✅ PII blocked: {e.message}")
           print(f"   Trigger: {e.check_trigger}")
   
       # Test 5: Request with phone number (should be blocked)
       print("\n🔴 Test 5: Input containing phone number")
       print("-" * 30)
       try:
           agent.print_response(
               input="My phone number is 555-123-4567. Please call me about my order status.",
           )
           print("⚠️ This should have been blocked!")
       except InputCheckError as e:
           print(f"✅ PII blocked: {e.message}")
           print(f"   Trigger: {e.check_trigger}")
   
       # Test 6: Mixed PII in context (should be blocked)
       print("\n🔴 Test 6: Multiple PII types in one request")
       print("-" * 30)
       try:
           agent.print_response(
               input="Hi, I'm John Smith. My email is [e-mail] and phone is 555.987.6543. I need help with my account.",
           )
           print("⚠️ This should have been blocked!")
       except InputCheckError as e:
           print(f"✅ PII blocked: {e.message}")
           print(f"   Trigger: {e.check_trigger}")
   
       # Test 7: Edge case - formatted differently (should still be blocked)
       print("\n🔴 Test 7: PII with different formatting")
       print("-" * 30)
       try:
           agent.print_response(
               input="Can you verify my credit card ending in 4532123456789012?",
           )
           print("⚠️ This should have been blocked!")
       except InputCheckError as e:
           print(f"✅ PII blocked: {e.message}")
           print(f"   Trigger: {e.check_trigger}")
   
       print("\n" + "=" * 50)
       print("🎯 PII Detection Demo Complete")
       print("All sensitive information was successfully blocked!")
   
       # Create an agent with PII detection which masks the PII in the input
       agent = Agent(
           name="Privacy-Protected Agent (Masked)",
           model=OpenAIResponses(id="gpt-5.2"),
           pre_hooks=[PIIDetectionGuardrail(mask_pii=True)],
           description="An agent that helps with customer service while protecting privacy.",
           instructions="You are a helpful customer service assistant. Always protect user privacy and handle sensitive information appropriately.",
       )
   
       # Test 8: Request with SSN (should be masked)
       print("\n🔴 Test 8: Input containing SSN")
       print("-" * 30)
       agent.print_response(
           input="Hi, my Social Security Number is 123-45-6789. Can you help me with my account?",
       )
   
   
   if __name__ == "__main__":
       asyncio.run(main())
   
2. Set up your virtual environment

   Mac

   uv venv --python 3.12
   source .venv/bin/activate
   

   uv venv --python 3.12
   .venv\Scripts\activate
   
3. Install dependencies

   uv pip install -U agno openai
   
4. Export your OpenAI API key

   Mac/Linux

     export OPENAI_API_KEY="your_openai_api_key_here"
   

     $Env:OPENAI_API_KEY="your_openai_api_key_here"
   
5. Run Agent

   python pii_detection.py