# Paper Proposes SR 26-2-Compatible Generative AI Governance Framework > Source: > Published: 2026-07-07 04:00:00+00:00 # Paper Proposes SR 26-2-Compatible Generative AI Governance Framework The **arXiv:2607.04103** paper submitted on **July 5, 2026** proposes a **Generative AI Control Framework** for financial institutions trying to align generative AI controls with SR 26-2. The Federal Reserve's SR 26-2, issued on April 17, 2026, replaces SR 11-7 and modernizes model risk management, while the paper argues that generative and agentic AI create control gaps around monitoring interpretation, policy analysis and adverse-action drafting. For banking AI teams, the useful takeaway is not new regulation but a proposed control-mapping approach: treat generative outputs as auditable workflow inputs when they can affect regulated decisions. The useful point for regulated AI teams is that governance can fail around the workflow, even when a generative model is not itself treated as a traditional model. The paper translates that gap into a control framework banks can debate and test. ### What happened The arXiv paper proposes a Generative AI Control Framework for financial institutions seeking alignment with SR 26-2. The Federal Reserve's SR 26-2 letter, issued with the OCC and FDIC on April 17, 2026, supersedes SR 11-7 and updates model risk management guidance. The paper argues that generative and agentic AI uses can sit outside formal model scope while still affecting regulated workflows. ### Policy context The paper focuses on generative outputs used around monitoring interpretation, policy analysis or adverse-action language drafting. Those outputs may not be credit models, but they can still shape how staff document, explain or execute regulated decisions. That creates a control problem for traceability, review, materiality and human accountability. ### For practitioners Banking AI teams should map each generative use case to the business decision it can influence, then document prompts, retrieval sources, output review, escalation thresholds and audit evidence. The framework is most useful as a checklist seed, not as proof that supervisors have endorsed any specific GenAI process. ### What to watch The next signal is whether regulators, bank model-risk teams or audit groups cite similar controls in examination guidance, validation playbooks or public risk-management templates. ## Key Points - 1The paper maps generative-AI workflow risks onto SR 26-2-style model-risk controls for regulated financial institutions. - 2Official SR 26-2 replaces SR 11-7, but the GenAI framework is the authors' proposal, not regulation. - 3Practitioners should document prompts, retrieval context, review steps and materiality when generative outputs touch regulated workflows. ## Scoring Rationale The topic is important for regulated GenAI adoption because it connects SR 26-2 model-risk expectations to generative-AI workflows. The score is lowered from 6.8 to 6.3 because the framework is a preprint proposal rather than official supervisory guidance or industry adoption evidence. ## Sources Public references used for this report. Practice with real Banking data 90 SQL & Python problems · 15 industry datasets [Suspicious Online TransactionsEasy](/problems/sql/suspicious-online-transactions) [Delinquent Loans Over 30 DaysMedium](/problems/sql/delinquent-loans-over-30-days) [Credit Card Utilization Risk ReportHard](/problems/sql/credit-card-utilization-risk-report) 250 free problems · No credit card [See all Banking problems](/problems/datasets/banking)