Confidential Computing Summit 2026

AI workloads handle some of the most sensitive data in modern enterprises, from proprietary training datasets to user prompts and high-dimensional embeddings. Yet many AI pipelines are built without the rigorous security practices applied to traditional systems, leaving critical gaps.

Josh and Nina from the CNCF AI Working Group show how to apply Zero Trust principles to secure AI data at every stage: at rest, in transit, and in runtime. Attendees will learn why conventional approaches fail for AI, highlighting risks like prompt injection, embedding poisoning, and GPU memory leakage, and how Zero Trust, combined with confidential computing, provides a stronger security foundation.

We’ll demonstrate how agent identity, continuous attestation, and trusted execution environments (TEEs) enforce runtime trust, while encryption, fine-grained access control, and mTLS protect data at rest and in transit.

Attendees will gain actionable strategies for securing every stage of the AI data lifecycle using modern encryption, policy enforcement, and runtime hardening.