E-23 governance: what changes when AI model risk must scale?

TL;DR: OSFI’s E-23 expectations expose a scaling problem in AI model risk, according to ValidMind, as organisations can no longer rely on manual reviews, periodic inventories and added checkpoints when AI use cases are multiplying quickly. Governance has to move into the lifecycle itself, with automation, tiering and live controls replacing slow approval chains.

Editorial analysis by NHI Mgmt Group, based on content published by ValidMind: “Operationalizing E-23: Scaling AI Governance Without Slowing Down AI”.

Key questions

Q: How should organisations scale AI governance without creating production bottlenecks?

A: Use risk tiering, automated evidence capture and workflow routing so high-risk models receive deeper human review while lower-risk systems move through a lighter, repeatable lifecycle.

Q: Why do manual model risk processes break down for fast-moving AI programmes?

A: They assume stable inventories, slow change and enough expert time to review every model in sequence.

Q: What happens when AI governance reviews do not keep up with change?

A: The programme can approve a system that no longer matches its actual permissions, integrations, or business purpose.

Practitioner guidance

• Build a defensible AI inventory Create a current inventory of models and AI systems, then tie each entry to an owner, risk tier and production status so governance reflects reality rather than spreadsheets.
• Define tiering criteria before scaling Use a consistent risk model to separate high-risk AI from lower-risk use cases so expert review is reserved for the systems that truly need it.
• Automate documentation and validation loops Use templates, test automation and workflow checks to capture evidence during the lifecycle instead of reconstructing it after deployment.

Bottom line: Manual AI governance does not scale when model inventories, updates and ownership changes are expanding faster than review cycles can absorb.

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