Zero Trust has become a foundational security model, but it assumes predictable systems and well-defined behavior. AI systems challenge these assumptions by introducing dynamic decision-making, evolving inputs, and opaque execution paths.

This session explores how AI fundamentally breaks traditional Zero Trust boundaries and what needs to change.

We will cover:

New attack surfaces: prompt injection, data poisoning, model abuse
Why identity-based access control is insufficient for AI workflows
Applying Zero Trust principles to model inference and data flows
Securing AI pipelines across distributed cloud environments

Using real-world infrastructure patterns, we will demonstrate how to redesign Zero Trust architectures to account for autonomous systems.

Attendees will gain a practical framework to secure AI systems without sacrificing performance or scalability.