Building Secure AI Workflows Across Edge, Cloud & AI Without Breaking Trust

Today’s enterprise workflows don’t live in one place — they move across edge devices, cloud infrastructure, and on-prem systems in milliseconds. Some start at the edge — like sensor data or customer interactions — while others finish there, after running through cloud analytics or AI inference. The result is a constantly shifting architectural boundary where speed, security, and compliance collide. This session looks at how to design cross-architectural workflows that maintain consistency, security, and data integrity from end to end. We’ll unpack how different architecture types — edge-native, hybrid, and multi-cloud — impact data handling, governance, and latency. And we’ll dive into the new layer of complexity AI introduces: model hallucinations, prompt injections, and evolving regulatory constraints when sensitive customer data moves from edge to cloud. Join Eugina Jordan, CEO of YOUnifiedAI, as she breaks down how to architect distributed systems that are both intelligent and compliant — not in theory, but in practice. Key takeaways: - Understand how modern workflows traverse edge, cloud, and on-prem environments — and where control points break down - Design architectures that protect customer data and maintain compliance even when processing happens across geographies - Implement AI workflows that minimize hallucination risks and prevent prompt-injection attacks at the edge - Balance performance with governance — using architecture, not just policy, to enforce trust - Build an operational model that lets security, data, and AI teams collaborate on a single distributed workflow