Risk, Controls, and Remediation

AI can help risk teams navigate complex internal control environments by answering questions about frameworks, control definitions, test results, and open issues with sourced evidence. 

In scenarios where multiple frameworks (operational risk, technology risk, SOX, regulatory guidance) intersect, the RAG model can quickly map a process to required controls and show the latest testing outcomes. This improves control coverage and reporting accuracy for executives and regulators.

Documents to Consider
  • Risk frameworks
  • Control catalogs
  • Prior control test results
  • Issue management logs
  • Historical audit findings

Scenarios

Control Design for a New Process: A business line introduces a new process (e.g., outsourced customer onboarding) and needs to understand the required controls. The RAG assistant retrieves relevant risk frameworks and controls, then proposes a baseline control set where testing evidence is required. It also flags other audit findings for similar processes.

Benefit: faster, more consistent control design that reduces the chance of process gaps being found by auditors or regulators.

Regular Risk Updates: Risk leaders need to produce a quarterly controls update for the board risk committee. The RAG assistant summarizes control test results and pulls key items from issue management logs and audit findings. It drafts an executive narrative with supporting data and suggests management actions.

Benefit: less manual compilation, clearer insight into systemic weaknesses, and more credible reporting because statements are traceable to underlying evidence.

Remediating Control Findings: An issue owner must close multiple control findings within a tight deadline. The RAG model generates a remediation plan for each finding and a checklist of artifacts required for validation based on related test evidence and prior remediation patterns. It further summarizes the status for leadership directly from the issue log. 

Benefit: faster remediation and fewer re-opened issues because closure packages are complete and aligned to control testing expectations.

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