Panel - The Future of AI is Sovereign A Strategic Dialogue on Innovation and Trust
summary
Hyperscalers offer click-to-use shared models that ingest prompts and continue training, creating real risk of exposing sensitive inputs; organisations therefore split into three approaches: use public shared models for non-sensitive tasks, private LLM + RAG on sovereign cloud for regulated data (example: a healthcare platform running anonymised inference on a dedicated OpenStack/VMware stack), or build own LLM on small GPU farm as costs drop from the 10,000‑GPU myth; EU AI Act and data sovereignty laws drive those choices.
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chapters (14)
- 0:00Sovereign AI discussion intro
- 1:09Defining sovereign AI
- 5:45Risks of hyperscalers
- 9:20Regulatory compliance in AI
- 16:08Deploying AI on sovereign infra
- 21:30Sovereign AI in healthcare
- 24:44EU AI Act impact
- 27:57EU Cloud and AI Act
- 30:05Hybrid cloud strategies
- 31:53Barriers to sovereign AI
- 34:52Innovation versus sovereignty
- 36:58Emerging sovereign AI trends
- 40:06AI agents and MCP protocols
- 41:31Closing sovereign AI remarks
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