When enterprises feed private data into third-party large models, a covert battle over AI sovereignty has already begun. MIT Technology Review recently released a report revealing an anxiety spreading through global enterprise management: depending on cloud LLMs — does it enhance competitiveness, or hand over core assets? The report surveyed over 2,050 enterprise executives, and data shows 70% of global enterprise executives believe building sovereign AI and data platforms is a necessary condition for future development. EDB CEO Kevin Dallas put it bluntly: data is the new currency, the IP of many companies. When you deploy large models in the cloud, are you losing control of your own IP? This isn't unfounded worry. As agentic AI systems enter enterprise core business processes, the depth and breadth of AI's data access is rapidly expanding — no longer simple Q&A, but autonomous planning, action, and execution of business processes. Once model-vendor policies shift or data is used for training, enterprises will be in an extremely passive position.
NVIDIA CEO Jensen Huang publicly called out at the 2026 World Economic Forum in Davos: every country should build its own AI infrastructure, develop its own AI, and leverage its most fundamental resources — language and culture. The self-control of AI infrastructure has evolved from enterprise technology choice to national strategic issue. In the early GenAI days, enterprises generally accepted the implicit deal: get capability first, deal with control later. But as AI penetrates core business, this paradigm is being reexamined. Enterprises are starting to require that models must run in their own environments, that data cannot leak, and that audits must be transparent. This has given rise to the rise of sovereign AI platforms — based on open-source models, deployed on private cloud or local, fine-tuned with proprietary data. AI sovereignty isn't against using large models, but demands keeping the data bottom line while enjoying model capability.
For enterprises, this is a required question; for model providers, respecting data boundaries will become key to differentiated competition; for regulators, drawing the line between national security and enterprise needs will be the most complex policy game of the next phase. When AI moves from the lab to the boardroom, who controls the model and who owns the data is no longer just a CTO's technical question, but a CEO's strategic question.