AI Governance, Risk & Audit Readiness

Criminals aren't waiting for regulators to provide clear AI guidance. Your institution shouldn't either. Generative AI is already showing up in fraud schemes, from deepfake voice calls to phishing that no longer reads like a scam. Deloitte projects gen-AI-enabled fraud losses climbing from $12.3B in 2023 to $40B by 2027, and 59% of credit unions have deployed generative AI in some form, often without documentation to back it up. Regulators have said plainly that they have no AI-specific rulebook, which means the tools you already have, human review, explainability, and a documented paper trail, are what examiners will lean on. In this session, RiskScout's Corey Lynch, CAMS, and Sepideh Rowland, partner at Klaros Group, walk through where AI already lives in the back office, what the regulatory landscape actually says (and doesn't), and the governance framework your board needs before AI adoption outpaces your controls. What You'll Learn Where AI is already operating inside your program and what uses carry the most regulatory exposure How to manage AI costs to fit your budget and improve ROI What regulators have said about examining AI, and where the gap between adoption and oversight actually sits A five-pillar governance framework and a practical audit-readiness checklist, so your AI use is documented rather than accidental How to vet an AI vendor the same way you'd vet any third party, and how to spot automation dressed up as AI