Synthetic PII data generator
You need realistic personal data to test an AI prompt, a parsing pipeline, or a validation rule — but using real customer records exposes them to your model provider and breaches data-minimization principles. The synthetic PII data generator produces datasets that look exactly like real personal data — names, emails, phone numbers, addresses, dates of birth, and financial identifiers — while being completely fake. Pick your fields, row count, and locale, then export as CSV or JSON. It all runs in your browser, so nothing leaves your machine.
How it works
Each field is generated from curated value pools and format rules. Names combine
locale-appropriate first and last names; emails are derived from those names on
the reserved example.com domain; phone numbers and postal codes follow the chosen locale’s
pattern; card numbers are built to pass the Luhn checksum so they exercise real
validation logic without being live cards. An optional seed drives a small
deterministic random number generator, so the same seed reproduces the same
dataset exactly — handy for repeatable test runs. Output is assembled into rows
you can copy or download in CSV or JSON format.
Tips and notes
- Match the locale to your users. A UK postcode and a US ZIP exercise different validators — generate the format you actually parse.
- Use a seed for regression tests. Reproducible rows make failures easier to diagnose than fresh random data every run.
- Generate a large batch. A bigger run surfaces a wider spread of name, address, and number combinations to exercise your parser against.
- Never reverse-fill with real data. The whole point is that no real person is involved; keep it that way.