AI systems increasingly impact critical sectors, but their data pipelines lack transparency and standard verification methods, creating compliance risks and eroding trust. Most current AI deployments rely on centralized systems, requiring stakeholders to trust internal controls that cannot be independently verified. Decentralized solutions like Walrus address this by cryptographically anchoring data integrity, allowing external verification of data provenance, integrity, and availability. As part of the Sui Stack, Walrus assigns unique content-derived IDs to datasets, tracking every change and ensuring data can be audited. Other Sui Stack components enable secure offchain computation, enforce granular access control, and record all activities on-chain for shared, verifiable access. "Verifiable AI" refers to systems where inputs, storage, and modifications are transparent, allowing for auditability—not guaranteeing correctness or ultimate truth of outputs. This is particularly important in regulated domains such as finance and healthcare. Walrus has seen adoption among AI teams seeking to ensure data integrity for regulatory and operational trust. For organizations, moving critical datasets to verifiable, content-addressed storage is a foundational first step toward trustworthy AI workflows.