Data Is the Real Model: Governance, Lineage, and Provenance

Short summary

Part 3 of a series on trustworthy AI infrastructure, this post argues that data governance — not model architecture — is the primary bottleneck for enterprise AI adoption, citing 81% of organizations stalling initiatives over data-permission issues. It defines provenance (origin and rights) and lineage (end-to-end data flow tracing) as the two controls that make data layers legible, enabling compliance, security, and governance simultaneously. The post outlines three data failure categories and prescribes capturing provenance at ingestion, distinguishing data categories, and preserving consent limitations.

• •81% of enterprises have delayed or abandoned AI initiatives due to data governance problems
• •Provenance (origin/rights) and lineage (flow tracing) together make the data layer auditable and compliant
• •Three failure modes: unauthorized data use, sensitive data leakage, and adversarial data poisoning — all require upstream controls

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