Venice has just released its end-to-end encrypted AI inference capability. Currently, all major AI platforms are based on the same fundamental trust assumption: you must trust your service provider to handle your data responsibly. @AskVenice employs a slightly different architecture. Conversations are stored on your local device, and prompts are not persisted to the server. When you use an edge model, Venice proxies the requests, so the service provider never receives your identity data. However, the same trust assumption still applies. If Venice or its partners wanted to intercept data, there is no mechanism in the architecture to prevent them. This release introduces two hardware-enforced privacy modes. TEE runs inference within a secure hardware enclave operated by NEAR AI Cloud and Phala Network, isolating computation from the host operating system and infrastructure operator. Remote authentication binds encrypted certificates to physical hardware, so anyone can independently verify that the model is running within a true enclave. You no longer need to trust the GPU operator, but you still need to trust Venice's transport layer. End-to-end encryption (E2EE) eliminates the remaining trust assumptions. The message is encrypted on the device before transmission, remains encrypted within Venice's infrastructure, and is decrypted only within a verified secure area. During normal operation, Venice cannot view any of your data. However, the downside is potentially slower response times, and network search and memory functionality are disabled due to the need for decryption outside the secure area. Currently, both modes operate on a limited number of open-source models on NEAR AI Cloud and Phala Network, and are only available to Pro subscription users. The reliability of these safeguards in practice depends on the implementation of certifications and whether independent audits confirm these claims.