AI Risk Officer¶
A model can be perfectly legal, perfectly on-strategy, and still wrong. That's what the AI Risk Officer exists to catch — model-level risk: accuracy, validation, and drift, rooted in bank model-risk-management practice and pointed at AI/ML models. This bundle is the operator's manual for that seat.
It's one entry in AI Accountability's role-by-role map — distinct from AI Enablement (adoption), Chief AI Officer (strategy), and AI Transformation Lead (program execution): this seat owns whether the models themselves can be trusted.
New here? Start with What the AI Risk Officer Is — and the Role.
In this section¶
| Page | Last updated |
|---|---|
| What the AI Risk Officer Is — and the Role What model risk means, why the function is rooted in banking, and what it deliberately doesn't cover yet. |
Updated 2026-07-02 |
| Validating and Monitoring Models The three-part discipline: conceptual soundness, outcomes analysis, and ongoing monitoring for drift. |
Updated 2026-07-02 |
| Glossary Plain-language definitions of the terms used across this bundle. |
Updated 2026-07-02 |
| Validation How this bundle is validated — sourced claims are checked and each sweep is recorded here. |
Updated 2026-07-02 |