Ever tried building cool new AI feature, only to discover that the required data cannot leave a regulated boundary? Many architectures assume that data can be sent to an AI model with minimal friction. In real organizations, things are rarely that simple.

In this session, we’ll look at practical ways to design AI agents when data sovereignty is a core architectural constraint. We’ll cover patterns for handling confidential data, setting retrieval boundaries, and enforcing compliance, grounded in real challenges we face daily. What happens when a manufacturing client wants to use generative AI to improve operational decision-making but cannot expose sensitive factory floor data? Or when a financial institution wants to automate customer support without disclosing confidential client information to AI models? How do you deliver AI value while keeping critical organizational data protected?

This talk is rated at level 300. It is intended for architects and technical leaders who are building AI platforms in enterprise or regulated environments. Attendees should have a basic understanding of LLM-based applications, retrieval-augmented generation, and modern software architecture, but the session will remain practical and technology-neutral enough to be valuable for product and business stakeholders as well.