What may be obvious to data teams may be out-of-sight, out-of-mind for business stakeholders. Heather Gentile, director of product management of AI risk and compliance at IBM, suggests reinforcing that the results of a model are only as good as the data on which it is built and trained. “The transparency and explainability of governance also successfully accelerates and scales AI initiatives and business impact,” says Gentile.
Embrace new AI data governance priorities
CDOs, data governance, and data scientists must also consider AI-specific capabilities. For example, modelops is the discipline of monitoring ML models for drift and other conditions necessitating retraining. For genAI, data teams should be explicit about what data was used to train an LLM, RAG, AI agents, and other AI capabilities.
“An AI Data Bill of Materials (AI DBoM) is the foundation for responsible AI at scale and should be a part of the CDO’s governance strategy,” says Kapil Raina, data security evangelist at Bedrock Security. “An AI DBoM tracks all data feeding AI models—training, fine-tuning, and inference—ensuring quick project turnarounds with full transparency into what AI systems access and generate. Without it, CDOs are flying blind—exposed to security gaps, non-adherence to the rapidly evolving regulatory landscape, and stunted innovation.”
