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Data sovereignty is becoming an architectural problem as enterprises push artificial intelligence inference toward their most sensitive information. Pipelines assembled from separate tools struggle to keep embeddings, permissions and source data aligned, leaving many valuable data sets out of AI's reach.
The shift from model training to inference is changing what AI infrastructure has to consider. Enterprises must now manage sensitive data and associated permissions, complicating existing pipelines.
This means that creators and designers must think differently about how they use data and AI tools. They must now consider data sovereignty and its implications on their work.
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