SR 26-2

SR 26-2 is a supervisory guidance issued by the Federal Reserve in 2023 that establishes expectations for responsible artificial intelligence practices in the banking sector. The guidance addresses how banks should govern AI systems, manage associated risks, and ensure compliance with existing regulatory frameworks as AI adoption accelerates across financial institutions. It matters for enterprise governance because it sets the federal standard that large banks must follow when implementing AI models, making it essential context for any financial services organization deploying AI systems.

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ResearchGlobal2026-09-01

PwC Banking AI Framework Maps Five Gaps SR 26-2 Left Unresolved

PwC Germany published a whitepaper structuring AI governance for banks around five core challenges: scope definition, three-lines-of-defense adaptation, proportionality, third-party risk, and AI-specific model validation. The paper offers a practical implementation scaffold for financial institutions working through model risk management reform. It does not introduce regulatory obligations, but provides detailed control-ownership guidance banks can use to close gaps left by existing supervisory requirements.

PwC banking model risk management three-lines-of-defense third-party risk SR 26-2 financial services validation

ResearchGlobal2026-09-01

SR 26-2 Gets a GenAI Control Layer: New Framework Maps Compliance to Practice

A research paper published on arXiv proposes a layered control framework for generative AI risk that is structured to align with SR 26-2, the Federal Reserve's updated model risk management guidance. The framework covers approved-use boundaries, risk-tier assignment, input assessment, output evaluation, monitoring, and auditability. It is designed to help compliance and risk teams build traceable governance evidence for GenAI deployments subject to model risk management expectations.

SR 26-2 model risk management generative AI auditability risk tiering financial services Federal Reserve

ResearchUS2026-09-01

SR 26-2 Implementation Guide Exposes Legacy Model Inventory Gaps

Lumenova AI has published a practitioner implementation guide for SR 26-2, the Federal Reserve and OCC's updated model risk management supervisory guidance. The guide identifies concrete steps including model inventory rationalization, revised materiality-based tiering, strengthened validation independence, and a separate governance lane for agentic and generative AI. Compliance teams at regulated financial institutions can use the operating model recommendations as a readiness benchmark ahead of examinations.

SR 26-2 model risk management agentic AI generative AI banking OCC Federal Reserve model inventory

ResearchUS2026-09-01

SR 26-2 Forces Banks to Rethink Model Governance From Inventory to Board Oversight

The OCC's revised model risk management guidance, SR 26-2, resets supervisory expectations for U.S. banks by shifting to a materiality-based approach that covers both traditional statistical models and AI systems. CRA's practitioner analysis identifies four practical redesign areas: inventory scope, model tiering, validation, and governance alignment. Banks that operate AI governance and model risk management as separate programs face immediate pressure to harmonize them.

model risk management SR 26-2 banks OCC model validation inventory financial services AI governance