AI and Technology Risk: From Model Inventory to Assurance
AI adoption is exploding across enterprises, but most organizations are scaling models faster than they can scale confidence. Many AI governance efforts produce the right artifacts (principles, inventories, approvals), yet still fall short on assurance because the focus stops at governance rather than operational management. The result is familiar: slow reviews, inconsistent evidence, and limited ability to prove controls remain effective once models are in production.
Join John A. Wheeler of Wheelhouse Advisors for a practical, engineer-friendly session that shows how to move from model inventory to defensible assurance without throttling innovation. John will walk you through a practical model inventory and approval routine, demonstrate how to generate audit-ready evidence, and explore emerging opportunities to use AI agents for automated control testing.
Whether you are building your first AI governance program or hardening an existing one beyond documentation and periodic reviews, this webinar provides tactical guidance to balance innovation velocity with stakeholder trust.
Learn how to:
- Integrate AI governance with AI management by translating principles and standards into defined expectations, clear decision rights, and operational controls that can be validated continuously.
- Implement a minimum viable model inventory and approval routine that supports auditability (what is in production, why it was approved, how it is monitored) without creating a review bottleneck for engineering and data science teams.
- Design an evidence model for AI assurance by mapping controls to reliable signals data in motion, identifying where streaming architectures enable continuous evidence, and pinpointing where automation and AI agents can reduce manual control testing effort without weakening assurance.
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