AI policies are meaningful when they lead to specific obligations and control activities. Consider mapping policies and controls to your standards and regulations.
Assessments surface gaps. Action closes them.
After an AI governance assessment, many financial institutions see similar findings. Ownership is unclear. No single inventory exists. Validation practices vary by team. Third-party oversight is uneven.
The path forward is to translate these findings into a simple operating model supported by the right technology.
Start with the source of truth: The AI inventory
Every decision depends on knowing what exists. Build an inventory capturing the purpose of each AI use case, data sources and sensitivity, third-party involvement, model lineage, performance measures, and business impact.
Treat this inventory as a living record. Link it to owners, risk ratings, control requirements, testing activities, and issue history. An integrated risk and compliance platform can make the inventory actionable by driving workflows and consolidating evidence.
Turn policies into obligations and controls
Policies are meaningful when they lead to specific obligations and control activities. Map policy statements to the standards and regulations you follow, then to the controls satisfying those obligations. Define tests proving a control is working.
This mapping creates traceability, which simplifies internal reporting and regulator interactions. When obligations change, your mapping shows exactly what to update.
Build the minimum viable operating model
- Intake and approval — Standardize the questions teams must answer before building or buying an AI capability.
- Model validation — Establish criteria for conceptual soundness, performance, and outcome monitoring.
- Change control — Define when retraining or feature updates require new approvals, new testing, or rollback plans.
- Third-party lifecycle — Apply the same discipline to vendors.
- Issues and actions — Centralize findings, remediation plans, and due dates.
- Reporting — Provide clear dashboards for leadership, risk committees, and audit.
Practical example: Closing common gaps
Suppose a financial institution identified undocumented AI features inside a customer service platform. After intake, the feature is added to the inventory with owner, data sources, and risk rating. Third-party obligations are recorded, and a validation plan is created based on criticality.
Monitoring thresholds are defined for accuracy and potential drift. Evidence of testing is stored in the same system, and a dashboard alerts leadership to status changes. What used to be a scramble becomes routine governance.
People and culture still lead
Technology is important, but it supports people and process. Establish a regular cadence for a model and AI risk committee. Provide training for product owners and reviewers. Encourage early engagement with risk and compliance so teams see governance as an accelerator, not a blocker.
How CLA can help financial institutions with AI governance
When assessment findings flow directly into a clear operating model and technology-enabled workflows, you shorten time to value. You also improve exam readiness, reduce duplicate effort, and create transparency for leadership.
CLA’s digital and business risk teams can help you from assessment, policy design, and operating models to implementation of workflows, control testing, and reporting sustaining governance.