What types of sensitive data can Privatiser detect and anonymize?

Updated

Privatiser is a browser-based tool that strips sensitive values from text before you paste it into an AI chat, then lets you restore the real values from a local mapping afterward. It detects 30+ patterns across 13 languages, covering secrets and keys, network and cloud identifiers, and personal identity data. Everything runs locally in your browser — the site states "nothing leaves your machine" and "0 bytes to any server," so the redaction happens on your device rather than on a remote service.

Secrets and keys

These are the credentials that most often leak by accident when developers paste config or logs into an AI assistant:

  • API keys and secrets — including provider-prefixed keys such as sk-proj-... (OpenAI-style) and generic assignments like DB_PASSWORD = "..." or webhook_secret = "...".
  • JWTs and bearer tokens — the eyJ... header-payload-signature strings, plus Bearer tokens.
  • SSH / PEM keys — private key blocks.
  • Slack tokens — e.g. xoxb-....
  • Generic identifiers — passwords written in English, French, or Spanish (mot_de_passe, contraseña), which the sample input shows being caught.

Network and cloud identifiers

Useful when you're debugging infrastructure and want to share a config without exposing your topology:

  • IP addresses — both public and internal ranges (e.g. 10.0.4.221, 192.168.1.100).
  • Internal domains — hostnames like monitoring.local:9090.
  • MAC addresses.
  • Cloud resource IDs — AWS ARNs (arn:aws:iam::...), plus AWS, GCP, and Azure identifiers, and Azure-style resource paths such as /subscriptions/0000-4000-a000-0001/prod.

Personal identity data (PII)

For text that contains customer or personal records:

  • Emails and phone numbers (including international formats like +44 ...).
  • Credit cards — validated with the Luhn check, so random 16-digit strings aren't flagged by mistake.
  • SSNs, passports, IBANs — e.g. 078-05-1120, A1234567, GB29 NWBK ....
  • Vehicle plates and VINs.
  • Names and dates of birth.
  • UK-specific identifiers — NHS numbers, SWIFT codes, sort codes.

Optional pattern packs and custom rules

Beyond the built-in categories, you can enable extra pattern packs for domain-specific work:

Pack What it targets
Crypto BTC, ETH, and key material
Kubernetes K8s resource identifiers
Terraform IaC-specific values
Comms Slack, Teams, Twilio tokens
Location Coordinates

You can also add custom regex patterns (with a case-insensitive option) and a custom words to redact list for anything not covered by a pack. A separate allowlist lets you mark values that should never be redacted, so you don't accidentally strip something you need to keep.

How to check it fits your case

  1. Open the live tool on the site and paste a representative sample of the text you'd normally share with an AI.
  2. Confirm the values you care about appear in the redacted output. If a pattern is missing, add it under Custom regex patterns or Custom words to redact.
  3. Note that detection is pattern-based, not semantic — it catches structured formats (keys, IPs, card numbers) reliably, but free-form names or unusual formats may need a custom rule.
  4. If you want the same behavior without copy-pasting, the browser extension auto-anonymizes on paste into ChatGPT, Claude, Gemini, and others; there's also a VS Code extension and a Python package (pip install privatiser).

The practical takeaway: if your sensitive data is structured — credentials, network addresses, cloud IDs, or standard PII formats — Privatiser's built-in patterns will likely cover it. If it's unstructured or domain-specific, plan on adding custom regex or a word list.