Insights · Report · Data & AI · Oct 2025
A self-assessment covering model inventory, human oversight, and documentation expectations emerging from supervisory guidance.
Governance for AI systems extends traditional change management. The scorecard maps controls across data sourcing, evaluation harnesses, deployment gates, and post-production monitoring.
We weight human-in-the-loop requirements by decision impact. Not every model needs the same approval path, but high-impact decisions demand traceable overrides and appeals handling.
Completion takes roughly 90 minutes with security, legal, and product leads in the room; outputs are suitable for committee discussion rather than shelf-ware.
We can present findings in a working session, map recommendations to your portfolio and risk register, and help you prioritize next steps with clear owners and timelines.
