SR 26-2 Explained: 2026 Model Risk Management Updates for AI

SR 26-2, jointly issued by the Federal Reserve, OCC, and FDIC in April 2026, updates and extends the previous SR 11-7 guidance to address the complexities of AI and machine learning systems in model risk management, requiring rigorous validation and governance controls for AI-based models, including those from third-party vendors. This revision narrows the definition of what constitutes a model, demanding institutions document and justify the exclusion of tools from oversight, and emphasizes continuous monitoring, explainability, and data governance throughout a model's lifecycle. The guidance introduces materiality-based tiering, which scales validation efforts according to the potential impact of model failures, and places a significant burden on institutions to validate vendor models and maintain audit trails that can reconstruct decision-making processes. Additionally, it highlights the need for governance at the action level for agentic systems, which perform sequences of actions without human intervention, requiring audit trails detailed enough to track each tool call and decision sequence. This comprehensive framework aims to ensure that all models, regardless of their origin, are subject to stringent oversight to mitigate risks associated with AI deployment in financial institutions.

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