What types of PII does this tool detect?

It detects email addresses, phone numbers, IPv4 addresses, 13-16 digit card numbers, US Social Security numbers, URLs, and capitalised full names. Each category is a separate toggle so you only redact what you need. Detection is pattern-based using regular expressions tuned to reduce obvious false positives.

Is the redaction reversible?

No. Matched values are replaced with fixed placeholders like [EMAIL] or [NAME], and the original values are discarded. This is one-way masking suitable for logging and sharing — it is not tokenization or format-preserving encryption, so you cannot recover the originals from the output.

Will it catch every name or address?

No — name and address detection is the hardest case for any regex. The tool flags capitalised multi-word names, but it will miss lowercase names, single names, and unusual formats, and it may over-match ordinary capitalised phrases. Always review the output before relying on it for compliance.

Does my text leave the browser?

No. All scanning and replacement happen locally in JavaScript. Nothing is uploaded, which is exactly what you want when the whole point is removing sensitive data — the tool never sees a server.

Why redact LLM output specifically?

LLM responses often echo back PII from the prompt or retrieved documents, and that text frequently flows into logs, analytics, prompt-caching stores, and debugging traces. Redacting before those sinks keeps personal data out of systems that were never designed to hold it, reducing breach exposure and compliance scope.

AI Output Anonymizer

LLM responses have a habit of repeating back the personal data you fed them — names, emails, phone numbers — and that text then lands in logs, traces, and analytics that were never meant to store PII. This tool strips it out first. Paste the output, pick the categories to redact, and copy clean text with sensitive values replaced by placeholders. Nothing leaves your browser.

How it works

The anonymizer runs a set of regular-expression matchers over the text, one per entity type:

Emails, URLs, and IPv4 addresses — structured patterns that match cleanly.
Phone numbers — international and grouped formats, with a guard that ignores too-short sequences.
Card numbers (13-16 digits) and US SSNs — common financial and identity formats.
Names — capitalised multi-word sequences, the hardest and least precise category.

Matches are replaced with fixed placeholders ([EMAIL], [PHONE], [NAME], …) and you get a breakdown of how many of each were redacted. Matching order is chosen so that structured patterns like emails are handled before looser ones like phone numbers, avoiding partial mangling.

What it is and isn’t

This is one-way masking for the common case of keeping PII out of downstream systems. It is not format-preserving encryption or reversible tokenization — once redacted, the originals are gone. For regulated, high-stakes anonymization you should layer a dedicated PII detection service and human review on top; regex alone will always miss context-dependent identifiers.

Tips

Redact at the boundary: clean text the moment it leaves the model and before it reaches any log, cache, or analytics sink.
Turn off categories you don’t need — leaving “names” on can over-match ordinary capitalised phrases.
Pair it with the Prompt Injection Detector to both sanitise inputs and clean outputs around your LLM.