How Do You Keep Sovereign Data Under Control When AI Spans Multiple Clouds?

Multi-cloud AI data sovereignty requires organizations to maintain legal, technical, and operational control over data as it moves among providers such as AWS, Azure, and GCP, extending beyond residency to cover access, use, transfer, and jurisdiction. Governance can become inconsistent across cloud-specific identity, storage, compute, and catalog systems, creating gaps in permissions, policy enforcement, lineage, and audit visibility, while cross-cloud transfers can add egress costs and introduce further compliance risks. AI governance must encompass training and fine-tuning data, prompts, retrieved context, embeddings, vector indexes, inference outputs, logs, and telemetry because weaknesses in underlying data controls can affect model decisions and automated actions. The text recommends an architecture that minimizes unnecessary data movement by placing compute near governed data, using open storage and table formats for portability, applying runtime policy enforcement, and maintaining end-to-end observability across data lineage, workload activity, quality, and cost. It presents Acceldata’s VPC-native xLake platform as an example of a zero-egress, vendor-neutral approach intended to keep data and processing within customer-controlled cloud environments.

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