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On September 16, 2026, the Conference of State Bank Supervisors (CSBS) released a Artificial Intelligence Supervisory Framework designed to support state examiners in evaluating the use and risks of artificial intelligence (AI) at state-chartered banks and state-licensed nonbank financial institutions. The framework was approved by the CSBS State Supervisory Processes Committee (SSPC) and CSBS Non-Depository Supervisory Committee (NDSC) in August 2026. The framework serves dual purposes. It guides state examiners in identifying and understanding AI applications at financial institutions, considering interconnected risks, and determining when deeper review may be necessary. Simultaneously, it provides financial institutions with an understanding of the general approach, types of questions, and information that state examiners may request regarding the institution's AI-based products, services, and tools. Financial institutions may use the framework to review their own AI programs, create sound AI governance and risk management initiatives, and prepare for examinations. The framework is designed to consider each institution's size, complexity, risk profile, and use of AI. The framework consists of five elements: Core Examiner Guide: Articulates the core examination approach for identifying and considering AI use, including initial scoping questions, a document request list, and procedures for governance and oversight, AI inventory and use cases, and generative AI and emerging use. Examiner Work Program: Provides additional information and guidance for applying the Core Examiner Guide, giving examiners a structured set of questions to consider during an AI review. Nonbank AI Supplements: Provides additional detail about reviewing third-party and vendor risk, model risk, and consumer protection at nonbank financial services companies, reflecting the different risk profile and business models of nonbank entities. AI Use Case Risk Tiering Worksheet: An optional industry tool to assist with a risk-based assessment of individual AI use cases and to help examiners decide where additional review may be necessary, allowing institutions and examiners to prioritize oversight based on the level of risk presented by each use case. Source Support Document: Identifies the supervisory and risk management materials that were used in the development of the framework, providing transparency into the underlying references and standards. Each state financial regulatory agency will determine the extent to which the framework is incorporated into their supervisory programs.