Not your keys, not your coins. Not your model, not your data?

Over the summer, the tech industry was consumed by a debate about AI use in the enterprise and the need to protect IP. If an enterprise used proprietary models, was data leakage a necessary evil?

Companies seemed to have two options: They could use state-of-the-art, proprietary models and risk losing control of their data, or they could use open-weight models and never kiss the frontier.

Consider the concern: Company A wants to use LLM B from AI Lab C, and they want to avoid training AI Lab C how to eat Company A’s lunch by building its capabilities into LLM B. A good way to resolve the tension would be to let Company A run LLM B on its own infrastructure, so there’s no risk of its information fleeing on the wind.

AI agents are “creating a whole different set of requirements at the data layer.”
–Vast Data co-founder Jeff Denworth

But that raises another problem: AI Lab C doesn’t want to allow Company A to run LLM B on its own GPUs because it doesn’t want to hand over its model weights. It’s the same IP issue the company ran into, in reverse. You have to solve the trust problem in both directions!

Enter VAST Data co-founder Jeff Denworth and a new product called DataEnclave , which aims to let AI labs and enterprise-scale companies deploy proprietary models in secure compute environments without risking data transfer in either direction.

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