Verifiably Private AI (ai.vp.net)

A startup announced what it calls the first “verifiably private” AI system: a platform that lets users cryptographically verify their interactions are never logged or stored. Instead of asking users to trust a company’s privacy promises, the system uses hardware attestation plus cryptographic guarantees so you can confirm exactly which model and system prompts are running and that no data persists. The company is opening access to a limited set of early users and emphasizes full visibility — no hidden behaviors or back‑end logging — with patents pending. Technically, the architecture relies on dual attestation using Intel SGX enclaves together with TPM 2.0 to provide hardware-level proof of the runtime environment and boot integrity, paired with cryptographic/mathematical proofs that data isn’t recorded. For the AI/ML community this matters because it addresses the trust barrier that causes user self‑censorship and limits adoption in privacy-sensitive or regulated contexts. If robust, verifiable privacy could drive new standards for auditable inference, encourage cloud providers to support attestation workflows, and enable safer public deployment of powerful models. Remaining considerations include hardware trust assumptions and side‑channel risks, so broader scrutiny and standardization will be needed as the approach scales.