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Article
AI Governance Fails When Risk Arrives Too Late
Artificial intelligence governance must become operational, cross-functional, and embedded from the earliest stages of development if firms are to manage the growing risks posed by AI and generative AI. Speakers argued that model risk professionals are uniquely positioned to lead this evolution, helping organizations balance innovation with accountability, fairness, and regulatory expectations
Jun 02, 2026
Center for Financial Professionals ,
Tags:
Regulation and Compliance
AI and Technology (including Fintech)
The views and opinions expressed in this content are those of the thought leader as an individual and are not attributed to CeFPro or any other organization
- AI governance must
move beyond policies and become embedded in day-to-day operations
- Generative AI
introduces new risks around opacity, accountability, bias, and data
quality
- Regulatory
expectations are evolving but remain fragmented across jurisdictions
- Cross-functional
governance structures are essential for effective oversight
- AI inventories and
lifecycle risk assessments help organizations identify and prioritize
risks
- Risk assessments
should begin during ideation rather than after deployment
- Model risk teams possess skills that position them to lead AI governance efforts
- Fairness, transparency, and accountability must remain central as AI adoption accelerates
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