The integration of Generative Artificial Intelligence (GenAI) into financial risk functions offers significant efficiency gains but introduces nondeterministic risks that challenge traditional supervisory oversight. Unlike traditional econometric models, GenAI systems use transformer-based architectures to synthesize unstructured data, potentially mitigating operational frictions in cross-jurisdictional risk data aggregation and reporting. However, the stochastic nature of these systems fundamentally disrupts the “static validation” and “periodic review” assumptions inherent in deterministic frameworks such as the Federal Reserve Guidance SR 11-7 and Basel Committee on Banking Supervision’s Standard 239. This study assesses GenAI applications across market, credit and operational risk stripes, utilizing a methodology grounded in organizational control theory and a global systemically important bank case study. We propose a six-pillar governance framework anchored in the theory underlying the National Institute of Standards and Technology AI Risk Management Framework and the Committee of Sponsoring Organizations of the Treadway Commission Internal Control–Integrated Framework. Effective deployment requires an architectural shift from output-based backtesting to continuous supervisory overlays and radical prompt auditability. Empirical validation via a controlled pilot on 100 excerpts from publicly available financial risk documents – using a GPT-4-0613 snapshot to control for model drift – demonstrates that structured governance reduces the hallucination rate from 14.2% to 3.1% (p < 0:001). These findings suggest that while legacy infrastructure remains a primary hurdle to implementation, aligning GenAI with robust model risk management principles will facilitate the transition toward strategic, augmented risk oversight.