Most AI governance sessions are written by people who have never had to defend a decision to a federal auditor but this one isn't.
I began in aerospace engineering, where a component either gets certified or it does not fly, and the person who signs is named in the file forever. I then directed the $386 million ARPA Implementation PMO for the City of New Orleans, running 40 concurrent projects on a federal clock with AI integrated tracking, under Treasury rules where undocumented funds are clawed back from a city that cannot absorb the loss. We closed at 100 percent federal audit compliance.
That combination taught me something the data industry has not yet internalized. Model performance was never the hard part. Defensibility was, and aerospace solved defensibility seventy years ago: bounded authority, traceable decisions, named sign off, and a documented rationale that survives the departure of the person who wrote it.
This session walks through the governance architecture that actually held under audit, including the point where it nearly failed. You will see the three tier risk classification that decided which AI assisted outputs required a human signature, the documentation standard that made 40 parallel decision trails readable by a stranger, the vendor accountability questions that changed which tools we were permitted to buy, and the specific failure mode that almost produced a finding.
The content is deliberately platform neutral and applies whether your stack is SQL Server, Fabric, Snowflake, or Databricks. You leave with a risk classification tool sized for teams with no compliance department, a documentation checklist, a vendor evaluation script, and a one page governance policy template.
LEARNING OBJECTIVES
1. Classify AI assisted outputs into three risk tiers and determine which require documented human sign off before deployment.
2. Apply a documentation standard that allows an external reviewer to reconstruct a model assisted decision without access to the original team.
3. Evaluate a vendor or platform against accountability criteria that survive audit, rather than against feature lists.