- Validate: Validate the input and reject the input if it contains PII. (raise a 400 error)
- Mutate/Mask: Mask the PII data in the input. (replace the PII data with a placeholder)
Example of PII Data
Types of PII Data
- Direct identifiers: Names, Social Security numbers, driver’s license numbers, passport numbers
- Contact information: Email addresses, phone numbers, home addresses
- Financial data: Credit card numbers, bank account details, routing numbers
- Biometric data: Fingerprints, facial recognition data, voice prints
- Digital identifiers: IP addresses, device IDs, online usernames
Available Integrations on Truefoundry
You can handle PII data by using any of the following integrations:AWS Bedrock Guardrails
- Offers sensitive information filters to detect and handle PII in input prompts or model responses.
- Supports blocking or masking PII.
- Allows custom regex patterns for specific use cases.
- Read how to configure AWS Bedrock Guardrails on TrueFoundry here.
Azure PII Detection Services
- Utilizes machine learning and AI algorithms to identify and redact sensitive information using Named Entity Recognition (NER).
- Supports text, conversation, and document PII detection.
- Offers options for masking detected entities.
Guardrails AI Integration
- Provides a PII filter that validates text to ensure it does not contain PII.
- Uses Microsoft’s Presidio for detection.
- Supports various PII entities.
- Offers programmatic fixes for anonymization.
Enkrypt AI Protection
- Focuses on removing vulnerabilities in AI applications with customizable guardrails for PII detection and redaction.
- Supports domain-specific requirements.
- Ensures compliance with regulatory frameworks.
Custom PII Detection and Masking
- Build your own PII detection logic with custom webhook endpoints.
- Use presidio library or run a custom model internal to your enterprise.
- Read how to configure Custom PII Detection and Masking on TrueFoundry here.