AI is moving from experimentation to production. However, most data enterprises need exists outside the public cloud. This includes sensitive information like patient records, market research, and legacy systems containing enterprise knowledge. There’s also a risk of using private data with AI models, and adoption is often slowed or blocked by privacy and trust concerns.  Enterprises building next-generation AI factories—specializing in high-performance infrastructure to manufacture intelligence at scale—must be built on a zero-trust foundation. This security architecture eliminates implicit trust in the underlying host infrastructure by using hardware-enforced Trusted Execution Environments (TEEs) and cryptographic attestation. This post describes the full-stack architecture needed to integrate the zero-trust foundation into AI factories. On-premise requirements often limit enterprises to building their own models or using open source models for agentic AI workloads.…