Privacy

At DevDay on September 29, OpenAI announced Private Intelligence. The headline version is easy to repeat: frontier models, confidential computing underneath, and your data stays private, even during inference. The documents tell a more careful story. Private Intelligence is two things, and they are at very different stages: Zero Data Retention with Private Safety Processing (PSP). This is rolling out to eligible API customers, and OpenAI published a 36-page technical white paper describing it. Private Inference. This is a preview “coming this fall.” OpenAI hasn’t published the hardware, the trust boundary, or how a customer would verify it. I’ve spent most of my career on attestation and key release, first in payments and then in confidential computing. The first item is the more interesting piece of engineering, and very few people are writing about it. The second is the bigger bet. This post is my reading of both, based only on what OpenAI and others have published as of early October 2026. ...

The Confidential Computing Consortium defines confidential computing as “the protection of data in use by performing computation in a hardware-based, attested Trusted Execution Environment.” It’s a powerful idea. Instead of trusting operators or cloud providers, you trust hardware itself to keep your data protected even while it’s being processed. When WhatsApp recently announced new AI features like summarizing messages or helping draft replies, most people saw just another set of productivity tools. What went unnoticed is that behind these features sits one of the most ambitious privacy technologies ever deployed: confidential computing at internet scale. ...