Policy-to-Control Mapping for AI

Policy-to-Control Mapping for AI

Identify every decision, data source and accountable owner before mapping an AI use case to policy. Cicrim helps banking teams turn broad principles into controls that can be operated, tested and evidenced.

Map decisions to policy

Start with the business decision and its consequences for customers, employees and the institution. Document permitted data, prohibited uses, review requirements and approval authority. Trace each policy obligation to the workflow step where it must take effect, including vendor services and manual exceptions.

Define an executable control

For each obligation, specify a preventive or detective control, its operator, test method and evidence. An approval gate needs a named approver, a decision record and a fallback when approval is missing. A monitoring control needs an input, threshold, review cadence and escalation path.

Maintain the mapping through change

Connect the control register to model, policy and workflow versions. Review changes before release and confirm that evidence remains available after a vendor or integration update. Cicrim can facilitate workshops and prepare a policy-to-control matrix, exception register and implementation backlog for the bank to approve.

Plan the next working session

Bring the current process, the accountable business and control owners, and the questions your team needs to resolve. Cicrim can help define a focused scope, expected working outputs and acceptance criteria before delivery begins.

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