Ep 20: AI Compliance in Practice - Navigating Data Governance in AI

Data governance isn't sexy, but it's what makes or breaks your AI strategy. In this episode, Sam and Mac tackle the tactical reality of what happens inside companies trying to comply with AI regulations while keeping data governance practices intact. What you'll learn: Why you can't have compliant AI without proper data governanceData lineage: tracking where your data came from, how it's processed, and where it ends upReal-world bias example: How historical hiring data can violate EU AI Act principlesThe challenge of GDPR's "right to be forgotten" when data is baked into neural networksModel governance across the entire lifecycle—from selection to deployment monitoringWhy human oversight remains critical in high-risk systems like loan decisionsHow smaller companies can stay compliant without enterprise-level budgetsKey frameworks covered:  ✓ Data lineage and chain of custody  ✓ Audit trails throughout the AI lifecycle  ✓ Model cards for documentation (used by Google, Microsoft, Meta, Amazon)  ✓ Post-deployment monitoring: data drift, concept drift, and bias detection  ✓ Human-in-the-loop requirements for consequential decisions The unsexy truth: Compliance as a service companies are emerging to help startups navigate these requirements. Trust isn't just a nice-to-have—it's becoming a competitive advantage.