Privacy and Text, free, by Pivotal Labs
Take the personal data out before you share it
Replace emails, phone numbers, postcodes, card numbers and links with consistent labels, so a log, a ticket or a transcript can be shared without the people in it coming too. It runs in your browser.
Loading PII Scrubber...
What PII Scrubber does
Replace emails, phone numbers, postcodes, card numbers, and links with consistent [placeholder] labels. Predictable rules, honest scope.
Doing this job well
The moment this matters is ordinary and frequent: you need to show somebody a real example. A support ticket that demonstrates the bug, a log with the failing request in it, a transcript that proves the point. The example is only useful because it is real, and it is only shareable if the real parts about people are taken out first.
Doing that by hand fails in a specific way. You catch the obvious ones, the email address and the phone number, and you miss the postcode in line four hundred, because the eye stops looking once the page feels clean. Scale makes it worse rather than better, and the miss is the only part anyone will remember.
This applet replaces what it finds with consistent placeholders, and the consistency is the useful part rather than the removal. If the same address becomes the same label everywhere it appears, the shape of the conversation survives: you can still see that the person who complained on Tuesday is the person who was charged twice on Monday. Redaction that destroys that relationship also destroys the reason you wanted the example.
It is honest about its scope, which matters more than coverage. Pattern matching finds things with a shape: addresses, numbers, postcodes, card numbers. It does not find a person's name, because a name has no shape, and it will not know that a reference number is identifying in your particular system. Anything you share still needs a human to read it. This gets you from unreviewable to reviewable, which is the useful step, not from unsafe to safe.
That distinction is worth being firm about. Pseudonymised data is still personal data under UK GDPR when the original can be recovered, and a scrubbed transcript sitting next to the ticket it came from is exactly that. What the applet reduces is the risk of casual exposure when something is pasted into a chat, a ticket or an email, which is where most of the accidental disclosures actually happen.
Running locally is not a nice extra here, it is the entire point. A tool that removes personal data by uploading the personal data has solved nothing. Everything happens inside this page, which is the only arrangement that makes sense for the job.
Common questions
Is my text uploaded to be scrubbed?
No, and it could not sensibly be. Scrubbing by uploading would defeat the purpose. Everything runs in your browser.
Does it remove names?
No. Names have no reliable pattern, so it does not pretend to find them. Anything you are about to share still needs reading by a person.
Why does the same email become the same label every time?
So the structure survives. If every address became a generic label you would lose the ability to see that two events involved the same person, which is usually why the example was worth sharing.
Is scrubbed text safe to share publicly?
Treat it as reviewable rather than safe. Under UK GDPR, data you can still re-identify is still personal data, so this reduces casual exposure rather than removing your obligations.
What does it actually detect?
Things with a recognisable shape: email addresses, phone numbers, UK postcodes, card numbers and links. The rules are predictable on purpose, so you can check them.
Related applets
Built by Pivotal Labs
We build software, and this is a small piece of it.
Pivotal Labs is a software development and product management team. The applets on this site are the offcuts, the small things we build for ourselves and give away. The work we are paid for looks rather different.
See what Labs buildsWhen the browser tab is not enough
A one-off clean-up is a job for an applet. Doing it the same way every month, across systems that disagree with each other, is a job for data management & crm integration.
Further reading

The CS Tech Stack: What to Buy, and in What Order

The Sales-to-CS Handover Is Broken, and Both Teams Know It

How Customer Experience Transformation Boosts Business Success

What Your CS Health Score Is Actually Measuring, and What It's Probably Missing

The QBR Format That Fits Nobody

