How do I handle PII when using HuggingChat? Should we be scrubbing data first?

Yes, you absolutely need a scrubbing step before it touches an external API. The regex filter idea is a decent start for simple detection, but it's the bare minimum and will miss things.

A better approach is to treat it like any other data egress point. Use a dedicated PII detection library (Microsoft Presidio, for example) or a managed service from your cloud provider. These are trained on more patterns than you'll catch with regex, like partial addresses or project-specific ID formats.

And don't forget, you also need to log what you removed for audit trails. It adds complexity, but sending raw support tickets to a third party is a compliance disaster waiting to happen.

Oh yeah, that's a smart worry. I'm also trying to figure this stuff out.

I'd start with the regex filter in a script, just to get something working and safe quickly. It's better than nothing and you learn from doing it. But like, I'd only use it for a quick prototype or testing. Real data needs a proper tool.

For a project, I'd probably look at that Microsoft Presidio library user216 mentioned. It feels more "real". Have you tried setting up any PII tools yet? I'm still reading docs 😅

Totally agree that starting with a regex filter for a prototype is a sensible path to get something safe running fast. I think there's a hidden benefit there - you get to actually see what kinds of data your support tickets contain, which informs the rules you'll need later.

I'm curious, though, about the jump from regex to a full library like Presidio. I've been reading up too, and it seems like the setup and tuning overhead is pretty real. My hesitation is that I might spend all my time configuring a PII tool instead of actually getting to test HuggingChat.

Has anyone found a good middle ground, maybe a simpler open-source script that's a step up from basic regex but isn't a whole enterprise project? Or is that just a recipe for missing something important?

Yep, you definitely need that scrubbing step before the API call. Starting with a regex script is actually perfect for getting a feel for the data flow.

I'd say run that script on a batch of old tickets first. You'll be surprised how many weird edge cases pop up, like internal ticket IDs that look like social security numbers. That's how you build your real rules later.

For a next step, maybe check out a hosted PII redaction API if you're in a cloud. AWS and Azure have them, and it's less config work than rolling your own library. Lets you move faster while staying safe.

That's a really practical idea to test the regex on old tickets first. I did exactly that once and found our devs were putting fake test data like "John Doe - 123-45-6789" in tickets, which my regex happily matched as real SSNs.

The hosted API suggestion is spot-on for a middle ground. AWS Comprehend has a PII detection feature that's like five API calls to set up in a Lambda. Way less overhead than maintaining a whole library, and you still get the benefit of their updated models.

Just remember to check the cost per document if you're processing high volumes. Sometimes those APIs get pricey compared to a self-hosted open source tool at scale.

Absolutely start with that scrubbing step before the API. It's the right instinct.

The regex script is a solid first move because you learn your data's shape. I'd run it, but then also sample some results manually. You'll spot patterns a regex can't catch, like how people phrase things - "my account ending in 4567" - that's PII but might not match a simple card number pattern.

For teams, it's about layering. Regex first pass, then maybe a basic dictionary of internal project code names, then review. Lets you build up security without getting stuck in tool config hell early on.

Your instinct is spot on - you *must* scrub data before it leaves your systems. Starting with a regex script is exactly how I'd kick it off in a real pipeline.

But treat it like a smoke test, not a final solution. Run it on a few hundred old tickets and pipe the output to a file for manual review. You'll quickly see where it fails - people write "call me at 555-1234" or use internal IDs that match your patterns.

That exercise defines what you actually need from a proper tool. Then you can decide if a cloud API or a library like Presidio is worth the setup time.

Totally agree that starting with a regex filter is the way to go. It's what I did when I first tested summarizing team feedback.

One thing I'd add: after you build that script, try it on tickets from different teams (support vs sales vs billing). You'll see patterns you never expected, like sales using a "customer code" format that looks exactly like a phone number. That's the quickest way to learn what your real data looks like before picking a heavier tool.

Have you looked at your actual ticket samples yet?

Oh, running the script on old tickets first is such a smart, practical step. It turns a theoretical problem into a hands-on data audit, which is exactly what you need.

That process of finding "internal ticket IDs that look like social security numbers" often uncovers a secondary issue - your own internal data hygiene. Sometimes the biggest PII leak in a support ticket is a screenshot someone pasted in, or a poorly formatted "test" entry from years ago that your regex will now flag.

I've seen teams get stuck because they tried to build a perfect filter from day one. Your approach of letting the old tickets teach you the rules is way more effective. It builds a real-world data catalog for when you do graduate to that hosted API or a library like Presidio. You'll know exactly what entity types you need to detect.

You're assuming the old tickets are a good proxy. They can be a trap. Ticket formats change, and new teams generate data in ways you've never seen. I've had a regex filter pass an audit because it worked on the last year of data, then fail immediately when a new support team started pasting full credit card auth logs into a custom field.

That "real-world data catalog" is only valid until someone changes a business process.

You absolutely must scrub first. Don't think of it as a feature, think of it as a mandatory security control. That text leaves your perimeter.

The problem with a regex script isn't that it's a starting point, it's that people stop there. They get one approval and then it becomes the permanent solution, rusting into place for years. Regex can't catch a name in an image a user pasted into the ticket, and it never will.

Find your nastiest 100 support tickets, the ones with screenshots of errors showing full customer records, and run your regex on them. The output is your failure report. That's your business case for a real tool. If you're serious, look at dedicated redaction software, not just a library.

That manual sampling step is a great call. It's exactly how we caught edge cases where a user would write "my customer ID is 123-456-789" and our pattern flagged it as an SSN, when it was actually just a made-up internal format. The human review is what teaches the system.

Your layered approach is key. We ended up with a similar three-tier setup:
1. Regex for the obvious, structured patterns (SSN, credit card).
2. A small, constantly updated allow-list for known internal patterns (project codes, fake test data formats).
3. A final, random-sample human audit for a percentage of processed items. That last layer keeps you honest as new phrasing emerges.

It keeps the complexity manageable while you prove the need for a more automated detection service.

You're relying on humans to catch what the rules miss, which is fine until you scale. That manual audit becomes a compliance checkbox, not a review.

And "prove the need for a more automated service"? That's backwards. The need was proven the second you decided to send data to an external AI.

Regex is cheap and feels like progress, but it's a false economy. You'll spend more time maintaining exception lists than you saved.

Build the cost of a real tool into your project's ROI from day one. Every hour your team spends tweaking patterns is billable time that could've bought a managed service.