Near Protocol (NEAR)

Venice just shipped end-to-end encrypted AI inference. Every major AI platform today runs on the same basic trust assumption. You have to trust the provider to handle your data responsibly. @AskVenice has operated with a slightly different architecture. Conversations are stored locally on your device and prompts are not persisted server-side. When you use frontier models, Venice proxies the request so the provider never receives your identity data. However, the same trust assumptions still apply here. If Venice or a partner wanted to intercept data, nothing in the architecture would prevent it. The new launch introduces two hardware-enforced privacy modes. TEE runs inference inside secure hardware enclaves operated by NEAR AI Cloud and Phala Network, isolating computation from the host OS and infrastructure operator. Remote attestation ties a cryptographic certificate to the physical hardware so anyone can independently verify the model is running inside a genuine enclave. You no longer need to trust the GPU operator but you are still trusting Venice's transit layer. E2EE removes that remaining trust assumption. Prompts are encrypted on device before transmission, stay encrypted through Venice's infrastructure, and only decrypt inside the verified enclave. Venice cannot see your data at any point during normal operation. The tradeoff is that responses may be slower, web search and memory are disabled since they would require decryption outside the enclave. Both modes currently run on a handful of open source models through NEAR AI Cloud and Phala Network and are exclusive to Pro subscribers. How robust these guarantees are in practice depends on the attestation implementation and whether independent audits confirm the claims.

Read the alpha insight here. https://t.co/wosc52Fs7M

https://t.co/sS0FN2KYL6

the research article is also live on 「thecoding」 https://t.co/8lI0zSwc0T https://t.co/8uBuzT3AOC