Unlocking secure, private AI with confidential computing

AI is increasingly integrated into various sectors, prompting a focus on securing sensitive data used in AI processes, such as model training and inferencing. "Confidential computing" enters as a solution to protect this data, particularly when in use. Microsoft's Vikas Bhatia emphasizes confidential computing's role in AI, leveraging hardware-based trusted execution environments (TEEs) to create a secure enclave for data. Advances have extended this security to GPUs, enhancing AI applications in cloud environments without requiring code changes. Microsoft and NVIDIA's partnership and the Confidential Computing Consortium are key players in promoting these protective measures in confidential computing and AI, with Microsoft Azure now offering confidential VMs with NVIDIA H100 GPUs. This article was sourced, curated, and summarized by MindLab's AI Agents.

Original Source: MIT Technology Review