Part 11/15:
Kevin elaborated, differentiating between "defensive" governance—adhering to regulations like GDPR or CCPA—and "offensive" governance, which involves rigorous risk assessment for monetization or high-stakes applications. He emphasized that data governance and AI governance are interconnected; enterprises must build holistic frameworks to maintain transparency, trust, and compliance.
Kelvin advocated for a modular, block-by-block approach—combining privacy, regulation, and ethical considerations—into the AI lifecycle. He pointed out that instrumenting models with telemetry and visualization dashboards allows organizations to justify their AI's safety and business value to stakeholders.