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 governance
• Data lineage: tracking where your data came from, how it's processed, and where it ends up
• Real-world bias example: How historical hiring data can violate EU AI Act principles
• The challenge of GDPR's "right to be forgotten" when data is baked into neural networks
• Model governance across the entire lifecycle—from selection to deployment monitoring
• Why human oversight remains critical in high-risk systems like loan decisions
• How smaller companies can stay compliant without enterprise-level budgets

Key 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.