PII Redactor
Strip personal information out of any text before you share it. An AI model (bert-base-NER) finds people, organizations and locations, while carefully written patterns catch emails, phone numbers, credit-card numbers (Luhn-checked), Social Security numbers, IP addresses and URLs. Choose exactly which types to remove and how to mask them: solid blocks, readable labels like [EMAIL], or stable pseudonyms such as Person 1. Everything runs entirely in your browser, so the text you paste is never uploaded. The AI model downloads once on first use, then is cached.
How to redact PII from text
- Paste the email, ticket, log, or document containing personal information into the box.
- Click Detect and redact and wait a moment while the model loads on first use.
- Toggle which types to remove and pick a style (block, label, or pseudonym), then copy the clean output.
Examples
Redacting a support message
Hi, this is Jane Doe at Acme Corp. Reach me at jane@acme.com or 415-555-2671.
Hi, this is [PERSON] at [ORG]. Reach me at [EMAIL] or [PHONE]. (Person, Org, Email and Phone each detected.)
Frequently asked questions
Is my text uploaded anywhere?
No. Nothing is uploaded. The AI name detector runs entirely in your browser via WebAssembly, and the email, phone, card, SSN, IP and URL detection is plain pattern matching that also runs locally. Your text never leaves your device. Only the AI model is downloaded, once, then cached.
What kinds of personal information does it find?
Two layers. An AI model (bert-base-NER) detects people, organizations and locations from context. Regular expressions detect emails, phone numbers, credit-card numbers, US Social Security numbers, IP addresses and URLs. You can turn each type on or off before redacting.
How are the redaction styles different?
Block replaces each item with a run of solid blocks so the length is hidden. Label swaps in a readable tag like [EMAIL] or [PERSON] so a reader knows what was removed. Pseudonym assigns stable placeholders such as Person 1 and Email 2, and the same original value always reuses the same placeholder, which keeps anonymized text readable.
Are the credit-card and SSN detectors accurate?
They are deliberately conservative to avoid false positives. Credit-card candidates must pass the Luhn checksum that real card numbers use, and SSNs must use a valid structure (for example, never area 000, 666 or 900 and above). This means a stray number in your text is unlikely to be flagged, but always review the output before sharing.
Can it catch every possible name or piece of PII?
No automated redactor is perfect. The AI model misses unusual names and may mislabel rare words, and the patterns target common formats rather than every regional variant. Treat this as a fast first pass that removes the obvious personal data, then review the redacted text yourself before relying on it.
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