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> Data will not leak across workspaces. For any model that will be used broadly across all of our customers, we do not build or train these models in such a way that they could learn, memorize, or be able to reproduce some part of Customer Data.

I feel like that is explicitly what this is saying.

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The problem is, it's really really hard to guarantee that.

Yes if they only train say, classifiers, then the only thing that can leak is the classification outcome. But these things can be super subtle. Even a classifier could leak things if you can hack the context fed into it. They are really playing with fire here.

yes, i certainly agree with you. i think oftentimes these policies are written by non-technical people

i'm not entirely convinced that classifiers and LLMs are disjoint to begin with

If is also hard to guarantee that, in a multi-tenant application, users will never see other users' data due to causes like mistakes AuthZ logic, caching gone awry, or other unpredictable situations that come up in distributed systems—yet even before the AI craze we were all happy to use these SaaS products anyway. Maybe this class of vulnerability is indeed harder to tame than most, but third-party software has never been without risks.

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