“Governance Should Be Invisible to Developers” Says OneTrust Product Head

In a recent episode of The AI in Business Podcast, Shane Wiggins, Director of Product at OneTrust, argued that enterprises need to rethink governance if they want AI to succeed at scale. The frameworks designed for yesterday’s data systems cannot keep up with the speed and complexity of modern AI. Without a fundamental shift, enterprises risk stalling innovation and opening themselves up to compliance failures.

“Traditional data governance models, often designed for structured databases, are not built to manage today’s dynamic, unstructured AI workflows,” Wiggins said. The result, he explained, is “compliance gaps, inconsistent oversight, and even internal conflict over ownership of AI decision-making.” The problem is most visible in industries with sprawling data sets, like insurance and healthcare.

This lack of visibility into unstructured data is exactly what legacy governance tools were not designed to address. And yet, as Wiggins pointed out, unstructured data (images, documents, audi) is increasingly where the most valuable AI applications are emerging. Unless governance evolves, enterprises risk losing the trust of regulators, customers, and their own teams.

The regulatory environment only amplifies this pressure. More than 144 countries now have national privacy laws, covering over 80 percent of the world’s population. On top of that, at least 45 AI-specific regulations are expected globally by 2026. The European Union’s AI Act alone allows for fines of up to €35 million or 7 percent of global turnover. “It is not just about the existence of the laws,” Wiggins noted, “but also the evolving nature of them. You have to think about designing your processes to be flexible enough to adapt to tomorrow’s rule changes without necessarily grinding innovation to a halt.”