Toward Trustworthy Data Pipelines in the AI Era: Provenance, Sovereignty, and Agent Access

Data pipelines have evolved over the past three decades as an optimization for speed and volume. However, the rise of AI is fundamentally transforming what pipelines must deliver. This paper presents the thesis that data pipelines are undergoing a role transformation from "tools for efficiently moving and transforming data" to "infrastructure for AI to safely access trustworthy data," and examines this transformation along three axes: (1) cryptographic provenance verification using W3C Verifiable Credentials and Decentralized Identifiers; (2) a data-plane access layer for AI Agents through protocols such as the Model Context Protocol; and (3) structural guarantees of data sovereignty grounded in email federation architecture. The paper contributes a three-stage trust model — Implicit Trust, Regulatory Attestation, and Cryptographic Provenance — derived inductively from an analysis of 20 industries.