PII Redactor

Strip emails, phone numbers, credit cards and other personal data out of your text before it reaches an AI model — then restore it afterwards with a reversible token map. GDPR-friendly redaction for AI, automation and analytics pipelines.

What it does

• Detects and masks PII — email addresses, phone numbers, credit card numbers, US SSNs, IP addresses, IBANs and URLs.
• Reversible tokens — each value becomes a typed token like [EMAIL_1] or [CREDITCARD_1], so the text stays readable and the structure survives.
• One combined token map — a single reversible map (token → original value) is written to the run's key-value store, so a later step can put the real values back.
• Two modes — mask (sequential typed tokens) or hash (typed tokens with a stable hash).
• Privacy by default — the original text is never stored or logged unless you explicitly opt in.

Use cases

• Mask before the LLM — redact prompts before an AI node (OpenAI, Anthropic, etc.), then restore names, emails and numbers in the model's output. Keep raw PII out of third-party models.
• n8n / Make / Zapier flows — drop it in as a step: redact before the AI node, restore after. Great for support-ticket, email and document automations.
• GDPR-friendly logging & analytics — scrub personal data out of text before it lands in logs, datasets or a warehouse.
• Safe sharing — clean transcripts, tickets and messages before handing them to a vendor or teammate.

Input

{
  "text": "Contact John at john@acme.com or +1 415 555 0132, card 4111 1111 1111 1111",
  "types": ["email", "phone", "creditcard", "ssn", "ip", "iban", "url"],
  "mode": "mask",
  "includeOriginal": false
}

Provide text, items, or both.

Output

Each row is one redacted input:

{
  "ok": true,
  "redacted": "Contact John at [EMAIL_1] or [PHONE_1], card [CREDITCARD_1]",
  "replacements": [
    { "type": "email", "token": "[EMAIL_1]", "value": "john@acme.com" },
    { "type": "phone", "token": "[PHONE_1]", "value": "+1 415 555 0132" },
    { "type": "creditcard", "token": "[CREDITCARD_1]", "value": "4111 1111 1111 1111" }
  ],
  "counts": { "email": 1, "phone": 1, "creditcard": 1 },
  "totalReplacements": 3
}

A combined reversible token map is also saved to the run's default key-value store under token-map.json ({ "map": { "[EMAIL_1]": "john@acme.com", ... } }) so a downstream step can restore the originals.

Output schema

FAQ

Does it call an AI model or any external service? No. Detection is local pattern-matching with light validation (for example a Luhn check on card numbers) — nothing leaves the run.

How do I restore the original values? Use the token-map.json map (token → value) from the key-value store, or the per-row replacements, to swap the tokens back after your AI or automation step.

Can I automate it? Yes — via integrations on the Apify platform (n8n, Make, Zapier and more) and the Apify API.

Notes

Original clean-room implementation.

You pay only for what the run returns. Set a maximum cost per run and it stops cleanly at that limit.