Building AI products is hard, but building them when half of the architecture lives behind a customer’s firewall is a different game altogether. In this talk, I’ll unpack lessons from designing and scaling hybrid AI systems in heavily regulated, edge-driven industries like telecom, manufacturing, and public safety.

I’ll cover:

- Design patterns for AI in hybrid environments (on-prem + cloud)
- Trade-offs in data access, latency, and model explainability
- Privacy-aware ML pipelines, RBAC enforcement, and air-gapped fallback strategies
- Real-world examples of deploying AI in zero-trust, high-compliance environments

This talk is ideal for AI architects, infra engineers, and product leaders navigating the challenges of bringing modern AI to legacy, siloed systems without compromising trust or performance.