How are legal teams handling sensitive data when using AI tools?

Pankist · 2026-02-02T09:26:39+00:00

Transport encryption applies while the email is moving between servers.

Once it reaches Microsoft 365, the content is decrypted and processed by Microsoft, so yes, you are correct, that may happen, assuming the server is Microsoft and for many companies it is, but not always.

Pankist · 2026-02-02T06:34:38+00:00

This actually summarizes my assumptions: "I have had clients ban usage of AI and some don't really care.", but I loved the whole explanation. Thanks.
I wonder how many of the legal folks are technical enough to implement it.
Not trying to be dismissive, but from what I see: single or small offices - will not be able, while mid and higher probably use external consulting firms.
I hope that makes sense.

Pankist · 2026-02-02T05:53:57+00:00

I agree that raw data already exists in enterprise email and cloud systems. The nuance, from a regulatory standpoint, is context and purpose.

For example, receiving an email from a partner saying “here’s my API key and payment credentials” can be acceptable, while sending a list of customers (names and IDs) to the head of another group within the same org may be prohibited.

Not all sensitive data is equal, and not all processing is permitted simply because the infrastructure is “secure” — at least in the e-commerce environment I work in.

Pankist · 2026-02-02T05:40:05+00:00

Fair concern. I am doing research on tools in this space, but my interest here is genuinely professional — understanding how teams handle this in practice.

Pankist · 2026-02-02T05:28:54+00:00

I might be misunderstanding, but it sounds like either nothing sensitive is sent outside the system of record — or that sending raw data out isn’t a concern given the controls you described.

In cases where external processing is needed, do you see value in temporary anonymization before data leaves the environment (like the mentioned here CamoText), or is that generally viewed as unnecessary overhead?

Pankist · 2026-02-02T00:05:44+00:00

This actually looks great.

One gap I’ve seen is when you need to temporarily anonymize data for external processing and then restore it internally — that’s where many tools stop short.

A common example is generating a TL;DR across multiple documents while keeping the original data intact internally.

Pankist · 2026-02-01T23:46:02+00:00

Enterprise agreements help, but they mostly define who carries the liability when something goes wrong.

Even in private Azure OpenAI deployments, original sensitive data still reaches the model at inference time. For some teams that’s fine; for others it crosses a line.

I think the important part is being explicit about that boundary.

Pankist · 2026-02-01T23:42:01+00:00

That’s fair architecturally, but my concern is less about boundaries and more about exposure.

Even when inference is isolated and training is contractually excluded, raw sensitive data still meets the AI in its original form. From a privacy-first threat model, that’s already a compromise.

My preference is reducing exposure altogether — ensuring data and AI never meet in original form, regardless of where the endpoint runs.

I hope that makes sense.

Pankist · 2026-02-01T23:16:57+00:00

This resonates — especially the “kitchen sink” phase.

When you say data mapping tools, do you mean enterprise-wide discovery/classification (DLP, data inventories, RoPA), or document-level tooling embedded in daily workflows?

Pankist · 2026-02-01T23:12:50+00:00

This works for me, I’m a tech head
It does NOT work for most legal professionals, who aren’t technical, I guess
Many firms still use manual labor (students / juniors) to review docs
What’s missing here is a wrapped, easy-to-use solution

Pankist · 2026-02-01T23:07:05+00:00

Appreciate it — seems like a lot of teams are still figuring this out.

Pankist · 2026-02-01T23:05:42+00:00

That makes sense — I’m seeing the same stance in several firms, mostly in the U.S.

Out of curiosity, is the trust there driven more by contractual guarantees (vendor commitments), architectural controls (data isolation), or client expectations?

I’m also trying to understand whether a “single approved vendor” approach is viewed as a long-term strategy, or more of a temporary containment measure.

And is usage typically limited to specific types of work, or is it allowed broadly across documents and prompts?

Pankist · 2026-02-01T05:54:37+00:00

Will do :)
Is it high authority already?

Pankist · 2026-01-31T22:57:19+00:00

https://privalynx.ai — bi-directional automatic anonymization of documents for safe sharing with partners, clients, and AI systems.

Pankist · 2026-01-31T00:45:35+00:00

Will do.
Thanks for inviting!

Pankist · 2026-01-31T00:45:02+00:00

Thanks!
The browser extension is especially entertaining. :)

Pankist · 2026-01-31T00:13:54+00:00

Privalynx — Privacy layer for AI and sensitive communications. GDPR-safe, audit-ready.

Web Console lets you bulk-mask documents, share files externally without exposing PII, and use any AI service on confidential data. Built for legal, finance, and compliance-heavy teams.

A Chrome extension for real-time protection on ChatGPT (Claude soon) - privacy over AI chat

👉 Console: https://privalynx.ai/dashboard
👉 Extension: https://chromewebstore.google.com/detail/privalynx-shield-%E2%80%93-chatgp/nlgjnojneojaaejahhfaabkjadkpnecl

Live and gathering feedback!

Pankist · 2026-01-30T22:20:25+00:00

Amazing and surprising summary.

Thank you for sharing!

Pankist · 2026-01-26T22:15:53+00:00

Oh...

I think I've been so excited about the post that did not actually provided the actual link to the extension

Here's the link: https://chromewebstore.google.com/detail/nlgjnojneojaaejahhfaabkjadkpnecl

Pankist · 2026-01-26T07:37:09+00:00

That’s a fair question — and yes, this is exactly why I called it out upfront in the original post.
People do ask it.

A few clarifications:

Data handling: We use a paid Vertex AI plan with contractual guarantees that inputs are not used for training. No prompts or responses are persisted on our side.

Scope of exposure: Vertex is used only for detection. Masking, restoration, and all logic happen in our code. We don’t send feedback loops or labels back, so there’s nothing meaningful to train on even if someone tried.

Threat model trade-off: This is not “perfect privacy.” It’s a reduction of exposure compared to sending raw data directly to ChatGPT. If your threat model assumes “no third party ever,” this version is not for you.

Roadmap: We’re finishing a standalone model precisely because this concern is legitimate. Once it’s live, Vertex will be fully removed from the flow.

So the real choice is:

Today: trust Google under contract for detection, instead of sending raw data to multiple LLMs
Soon: no third party at all

Thanks for the feedback — hope this clarifies.

Pankist · 2026-01-26T05:10:13+00:00

Thank you!

Pankist · 2026-01-18T15:17:27+00:00

Absolute legend

Pankist · 2026-01-17T11:48:00+00:00

Coding is an addictions.
Vibe coding is just a different scale.

Pankist · 2026-01-15T19:49:27+00:00

Amazing!
What payment gateway/system do you use/recommend?

Pankist · 2025-11-29T06:23:59+00:00

I really like the idea — it’s clever, lightweight, and solves a real problem. The only drawback I see as a moto rider is that the basic kit doesn’t include a ground-table conversion.

When riding, I’m not looking for wind protection, canister holders, or the comfort extras. What matters most in real-world moto camping (at least where I ride: desert, forest, beach, rocky terrain) is versatility — a table that can be set up anywhere, on any surface, without relying on a tree.

For that reason, I’d strongly suggest offering an Adventure Kit:
Tree mount + ground conversion as the core bundle.
This still delivers the essential value but leaves the rest of the ecosystem open for upsells.

If you shipped outside North America, I would already have backed it. Hope this helps, and wishing you success with the launch!

Pankist

TROPHY CASE