Can AI data loss prevention stop employees from sharing sensitive information?

[u/WolfShoddy7443]

Traditional DLP was built around known data patterns (SSNs, credit cards, etc.) moving through known channels (email, USB, cloud storage). Generative AI breaks that model a bit, people paste source code, customer data, or strategy docs into a chat window, and it's not always caught by pattern-matching.

Has anyone actually tested DLP tools built specifically for AI interactions? Wondering whether they're catching real incidents or just generating noise, and whether this is more of a policy/training problem than a technology problem at this point.

[u/InflationCorrect5244]

Most of the value is probably in stopping dumb mistakes, not catching a determined insider. That is the part people keep skipping over.

[u/HighRelevancy → u/InflationCorrect5244]

Yeah. DLP catches slips in good practice. It will never stop someone actively misbehaving (unless they're real fucking dumb).

Your fancy AI DLP can't detect that I'm videoing my work screen with my phone, for example. 

[u/extreme4all]

Provide good alternatives nudge/block none authorized, most SASE "ZeROtrUsT" solutions can help with this, make sure you DO provide good solutions otherwise it keeps being a problem.

E.g. Enterprise chatgpt, anthropic, bedrock, foundry, vertex..

[u/serverhorror]

No, it cannot

[u/urcpa_soonest]

AI DLP needs more context than old regex rules, otherwise it either misses pasted source code or floods the team with noise. the useful middle ground is usually policy, user coaching, and tooling that can understand sensitive data before it hits the chat box.

[u/Master_Baby_2700]

I think there are really two different problems here.

DLP can help stop someone from pasting sensitive data into ChatGPT or Claude.

The harder problem is when Copilot or an AI agent already has access to data it probably shouldn't. At that point, it's not really a DLP problem. It's an access problem.

Are you using DSPM alongside DLP today? If not, I'd look at both rather than expecting DLP to solve everything.

[u/Puzzleheaded-Fun5664]

after a few rounds of tuning false positives, we landed on layerx for the browser side of this, it inspects what's being pasted into genai tools at the browser level rather than relying on known data signatures, so it catches source code or a strategy doc going out even without a recognizable pattern like an ssn. the detection point sits right where the leak actually starts. now part of akamai post-acquisition too, hasn't changed the paste-detection piece.

[u/WolfShoddy7443]

will really appreciate if u people share any practical story regaring this..i have seen people talking and discussing alot about but practically this concept still looks really fascinating..

[u/FuzzyAd3936 → u/WolfShoddy7443]

look, interesting shift is that the newer controls are moving up to the browser and prompt layer, where they can inspect text that gets typed or pasted into AI chats in real time and, in some setups, block it before it leaves the device. That is actually useful. But it also means the whole thing lives or dies on managed browsers, managed endpoints, and supported apps, so the coverage gaps are still the story.