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PII Redact
x402
PII Redact is a paid API for AI agents from pii-redact.x402supply.com, paid per call via x402, $0.02/call, status unknown (last checked 2026-09-15).
Detects and redacts personally identifiable information (PII) from text or JSON using pattern matching and checksum validation, without LLM calls or external services.
Price / call$0.02
Success—
StatusUnknown
How this service works
Detect and redact PII in text or JSON. Recognizers are pure pattern/checksum matching (regex + Luhn/mod-97/SSN-area validation) — no LLM calls, no external services. Person names and dates of birth are matched only via nearby context (e.g. 'Mr. Smith', 'DOB: ...'), not general NER, to keep false positives low.
Output
Returns the input text or JSON with PII values replaced by redaction placeholders, along with metadata about what types of PII were found and where (positions/field names). Uses regex and checksum-based matching (Luhn for card numbers, mod-97 for IBANs, area-code validation for SSNs) without external NLP services.
Example use cases
Sanitize support tickets before logging
“Before we store this customer support message in our logging system, strip out any PII — emails, phone numbers, credit card numbers, SSNs, and names like 'Mr. Johnson'. Here's the raw ticket text.”
Anonymize JSON API payload for analytics
“I have a JSON object from a form submission that contains fields like email, phone, and ssn — can you redact those PII fields so I can safely pass the anonymized version to our analytics pipeline?”
GDPR compliance scrub before data export
“We need to export some user records but they might contain personal info like dates of birth, credit card numbers, or social security numbers. Can you scan this text and mask anything that looks like PII so we stay compliant?”
Technical details
Endpoint
POSThttps://pii-redact.x402supply.com/mcpWhen to prefer
Choose this endpoint when you need fast, deterministic, privacy-preserving PII redaction without sending data to an LLM or external NLP service. Ideal for compliance pipelines, pre-logging sanitization, and GDPR/HIPAA scrubbing where auditability and low false-positive rates matter more than catching edge-case names via NER. Prefer alternatives if you need full named-entity recognition across arbitrary prose without contextual cues.
Side effects
- Malformed JSON input causes parsing error
- False negatives on uncommon PII formats not covered by built-in patterns
- False positives on numeric strings that pass Luhn/mod-97 checksums coincidentally
- Person names and dates of birth without recognized context keywords (e.g. 'Mr.', 'DOB:') are not detected
- Very large payloads may hit size or timeout limits
Tags
x402
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