AI governance, monitoring, and controlled model change
Explore an operating model for model inventory, performance monitoring, escalation, and controlled change. This illustrative scenario explains a proposed Cicrim approach. It does not describe a verified client engagement or measured client results. Benefits would need to be evaluated against the institution’s own baseline.
Illustrative scenario
A bank expanding its use of AI needs a shared view of models, purposes, owners, risk tiers, dependencies, and oversight responsibilities.
The business challenge
Inconsistent artifacts and manual monitoring can make it difficult to identify gaps in coverage or ownership.
Thresholds require an actionable response, and model changes need review proportionate to their impact.
Proposed approach
The proposed approach connects inventory, monitoring, issue management, and controlled change in a repeatable operating cadence.
Capabilities to consider for a controlled implementation:
- Model inventory: Record purpose, owner, risk tier, version, dependencies, and review status.
- Monitoring thresholds: Define performance indicators, data-quality checks, escalation, and response ownership.
- Change control: Require impact assessment, testing, approval, and release evidence.
- Issue management: Track limitations, incidents, remediation, and closure decisions.
Benefits to evaluate
- Monitoring coverage: Assess which material uses have current measures and responsible reviewers.
- Evidence availability: Measure the effort needed to prepare governance records for review.
- Lifecycle consistency: Check artifact completeness and approval status across models.
- Response discipline: Track threshold breaches, overdue actions, and remediation effectiveness.