For fifteen years, SR 11-7 served as the foundation of model risk management across the banking industry. It established the expectations for validating, documenting, and governing models used to support everything from credit decisions to capital planning. Over time, those principles became deeply embedded in how financial institutions approached model governance.

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With the release of interagency guidance modernizing that framework, SR 26-2 (Federal Reserve), OCC Bulletin 2026-13 (OCC), and FIL 15-2026 (FDIC) each signal the same direction: proportional, risk-based governance that aligns oversight with the materiality and impact of each model.  Rather than prescribing a one-size-fits-all approach, the updated guidance emphasizes proportional, risk-based governance that aligns oversight with the materiality and impact of each model.  SR 26-2 acknowledges that generative and agentic AI are rapidly evolving technologies outside the formal scope of the guidance, while making clear that existing enterprise risk management expectations still apply.

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For institutions that have already begun deploying AI, the most important signal from this guidance is not what it regulates. It is what it does not. Generative and agentic AI fall outside the formal scope of all three documents. But that is not permission to wait for AI-specific model risk management guidance before building governance frameworks. If anything, the absence of prescriptive AI rules places greater responsibility on institutions to define credible internal frameworks now. The institutions that treat this moment as a starting point rather than a reason to pause will be significantly better positioned when AI-specific guidance does arrive.

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For banks and credit unions that have begun integrating large language models into their daily operations, this raises an important question. If regulators expect more thoughtful, risk-based governance for traditional models, what does this mean for AI systems that are inherently more dynamic, less deterministic, and often more difficult to explain?

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A More Flexible Framework Requires Greater Discipline

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One of the most significant changes introduced by SR 26-2 is its stronger emphasis on a risk-based, proportionate approach to model risk management. Rather than applying the same level of governance to every model, institutions can tailor oversight based on relevant factors such as a model’s materiality, purpose, exposure, and inherent risk. In practice, this gives  larger institutions greater flexibility to align model governance with their specific model risk profiles, size, and complexity, while concentrating resources and controls on models that pose greater risk.

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That flexibility, however, comes with greater responsibility. Supervisors will increasingly expect institutions to demonstrate not only that governance processes exist, but that they are appropriate, consistently applied, and capable of identifying and mitigating risk. SR 26-2 raises the bar for demonstrating that controls are functioning effectively. Documentation alone is no longer sufficient. Banks and credit unions must be prepared to explain why particular controls are appropriate for a given model and provide evidence that those controls are functioning effectively.

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General-Purpose AI Wasn't Built for Banking Governance

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Many financial institutions are experimenting with enterprise AI assistants or commercially available large language models to improve productivity across compliance, operations, customer service, and internal knowledge management. While these tools can accelerate workflows, they were never designed with the governance expectations of highly regulated financial institutions in mind.

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General-purpose AI models often provide limited visibility into how responses are generated, making it difficult to produce the audit trails, explainability, and decisioning documentation that regulators expect. And as AI begins influencing activities with regulatory implications, institutions will need greater confidence that outputs can be understood, validated, and appropriately governed and not simply accepted because they appear credible.

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This represents a fundamental shift in how banking organizations should evaluate AI. The question is no longer whether a model produces useful answers.  The question is whether those answers can be explained, reconstructed, and defended when an examiner asks for the record.

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Explainability Is Quickly Becoming a Competitive Advantage

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The principles underlying SR 26-2 reinforce something the banking industry has long understood: trust depends on transparency. No matter how sophisticated AI becomes, financial institutions will remain responsible for the decisions made within their organizations. That responsibility requires visibility into the information used to generate recommendations, the reasoning behind those recommendations, the controls governing model performance, and the role of human oversight throughout the decision-making process.

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LLM explainability is a notable challenge because banks need answers to be repeatable, auditable, and attributable, but third-party foundation models can change in ways that make prior outputs difficult or impossible to recreate.

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These capabilities are particularly important as AI moves beyond simple productivity tools and becomes embedded within core banking functions. Institutions that establish strong governance today will be better positioned not only to satisfy supervisory expectations but also to deploy AI more confidently across higher-value use cases. Governance built into the architecture from day one is what lets institutions move fast without creating examination risk.

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Purpose-Built AI Aligns More Naturally with Regulatory Expectations

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As more banks and credit unions evaluate their long-term AI strategies, the distinction between general-purpose AI and solutions designed specifically for regulated financial services becomes increasingly important. Purpose-built banking AI can incorporate governance capabilities from the outset, including explainable and repeatable outputs, comprehensive audit trails, policy-aware reasoning, human review workflows, and controls that align with existing risk management frameworks. For example, Titan's banking context layer encodes the relationships between the institution's products, records, policies, and regulatory logic directly into the platform's foundation, so every output is grounded in the institution's own governance framework rather than borrowed from a general-purpose model that has never seen the inside of a bank examination.

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These capabilities do not eliminate model risk, nor should they. Instead, they enable institutions to better manage that risk in ways that are transparent and defensible.

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Governance Today, Confidence Tomorrow

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The institutions that have been building governance infrastructure while others waited will enter the next phase of regulatory scrutiny with an advantage that cannot be acquired on an accelerated timeline.

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As Blake Paulson, former Acting Comptroller of the Currency and a member of Titan's Board of Directors, has often emphasized, effective governance and innovation are not competing priorities. “Strong governance is what gives financial institutions the confidence to innovate,” Paulson said. “Banks don’t need to choose between moving quickly and managing risk responsibly. When transparency, accountability, and appropriate controls are built in from the beginning, institutions can adopt new technologies with greater confidence while maintaining the trust regulators and customers expect.”

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SR 26-2 may not be an AI regulation, but it is a clear indicator of where regulatory expectations are headed. The institutions that treat it as a starting point rather than a ceiling will be the ones best positioned when AI-specific guidance arrives. The ones that wait will be catching up.

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