9d • Data for AI
Practical Lessons on AI Governance in Production Systems
One thing I’m seeing repeatedly with AI governance:
Most governance frameworks fail because they live outside where decisions actually happen.
Top learnings from recent work:
- AI risk is rarely a model issue — it’s a context + data + ownership issue
- Policies defined upfront don’t survive runtime without enforcement hooks
- “Human in the loop” breaks down without clear decision rights and escalation paths
- Agents amplify governance gaps faster than dashboards ever did
Key challenge ahead:
Governance must move from review-time controls to runtime guardrails — embedded in data access, memory, orchestration, and action execution.
Curious how others here are handling governance inside live AI workflows, not just around them.
3
1 comment
Practical Lessons on AI Governance in Production Systems
powered by
skool.com/data-governance-hub-2335
A global community for data professionals and business leaders to learn, share, and grow together around Data Governance best practices.
Suggested communities
Powered by