The corporate rush to adopt agentic workflows and large language models (LLMs) has outpaced classic security architecture. While standard public AI tools offer massive productivity boosts, they introduce quiet compliance vulnerabilities: untraceable data routing, a lack of deep application sandboxing, and financial risks from rogue background loops. This session goes beyond standard checkboxes to look at the intersection of data privacy, regional data residency laws, and enterprise infrastructure. Attendees will walk away with practical strategies for deploying secure, containerized AI automation that satisfies both developers and security officers.
Navigating the Edge: Why Data Sovereignty and Privacy Are Mandatory for Next-Gen Enterprise AI
As rapid enterprise adoption of agentic AI and LLMs outpaces traditional security, standard public tools expose organizations to critical data sovereignty and compliance risks. Discover actionable strategies to deploy secure, containerized AI infrastructu