Why Verifiable Data Is the Missing Layer in AI: Walrus

AI models are getting faster, larger, and more capable. But as their outputs begin to shape decisions in finance, healthcare, enterprise software, and beyond, an important question needs to be answered—can we actually verify the data and processes behind those outputs?

“Most AI systems rely on data pipelines that nobody outside the organization can independently verify,” states Rebecca Simmonds, Managing Executive of the Walrus Foundation—a company which supports the development of decentralized data layer Walrus.

As she explains, there is no standard way to confirm where data came from, whether it was tampered with, or what was authorized for use in the pipeline. That gap doesn’t just create compliance risk—it erodes trust in the outputs AI produces.

“It’s about moving from ‘trust us’ to ‘verify this,'” Simmonds said, “and that shift matters most in financial, legal, and regulated environments where auditability isn’t optional.”