Read your governance artifacts as semantics, and the audit work you already do becomes the foundation for a best-in-class AI strategy – with cheaper models and more trust.
by Srikanth Mandalapu, Travis Paulson and Bernie Kuan
Ask most organizations what data governance for AI means, and you’ll hear a security answer: lock it down, restrict access, pass the audit. In healthcare, security is non-negotiable — but it’s incomplete. Security tells you who can touch data. It says nothing about what the data means, whether it can be trusted, or whether an AI model should ever learn from it.
Our Data Empowerment Program (DEP) starts from a different premise: governance is knowledge, context, and ontology; not just controls. Artifacts most teams treat as compliance overhead, such as classification tags, de-identification policies, model cards, and data contracts are raw material for enterprise data semantics.
When seen this way, you are not choosing between governance and AI, but instead, governance helps build AI. New approaches to governance need to be implemented in the AI era. The only question is whether you do the work later just to pass the audit, or now, to lay the foundation your AI runs on.
