Concern 220 · Working draft · editorial review in progress
Model-risk-management extension to gen-AI
Extending model-risk management to generative AI means adapting established oversight practices to systems that produce open-ended content or take actions.
Understanding the concern
Banks have long used processes for checking assumptions, validating models, and monitoring their use. Generative systems introduce different questions, such as unsupported statements, changing prompts, and tool actions. Applying an old checklist without examining those differences can leave gaps.
The regulatory reference needs care. In April 2026, US agencies replaced SR 11-7 with revised model-risk guidance, and its scope explicitly excludes generative and agentic AI models. That does not remove the need to manage their risks; it means this guidance should not be presented as directly covering them.
An example
A bank adapts its checks for a numerical forecasting tool to a writing assistant, then discovers that factual accuracy needs a different kind of test.
Illustrative unless identified as a documented case.
Words in plain English
- AI
- Software that learns patterns from examples or uses rules to perform tasks such as recognizing images or generating text. It can make mistakes and does not understand every situation as a person would.
- Agent
- An AI system that can choose and carry out several steps toward a goal, using the tools and access people give it.
- Model
- The learned part of an AI system. It uses patterns found during training to produce an answer or prediction.
- Prompt
- The instructions or information given to an AI to guide its response.
Evidence and its limits
The 2026 guidance explicitly excludes generative and agentic AI from its scope. NIST supplies separate generative-risk context; neither is a universal compliance checklist.
Federal Reserve, FDIC and OCC · April 2026 · Official supervisory guidance
Supervisory Guidance on Model Risk Management: SR 26-2 attachment ↗ (opens in a new tab)Replaces prior guidance; explicitly excludes generative and agentic AI models from scope and does not set enforceable standards by itself.
NIST · AI 600-1 · 2024 · Official risk framework
Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile ↗ (opens in a new tab)Describes generative-AI risk categories and management actions; it does not measure the failure rate of every product.
Broader frameworks and background
These resources provide context. They do not establish every claim in this concern.
A question to discuss
Which established controls still help, and which risks require a different evaluation method?
Keep track of different views, what needs checking, and any next step in your own discussion notes.
Open discussion notes →Principles for the conversation
Before you finish · reflect together
Virtues and principles to consider
Virtues are qualities we practice in how we treat people and make choices. They are not scores for judging someone. Use these ideas to explore what a thoughtful response could look like.
Trustworthiness
Trustworthiness means giving people good reasons to rely on you. It grows when promises, actions, and evidence line up. With AI, a friendly voice or a famous brand is not enough: people need to know what was checked and what could still go wrong.
In practice, keep promises small enough to test, record mistakes, and tell people when the tool's limits affect them. If a service promises human review, make sure a person really has time and authority to review it.
Ask together: What would give the people affected a sound reason to trust this use?
Accountability
Accountability means being answerable for decisions and taking responsibility for repair. It requires more than saying that the software made the choice.
Name the person or team responsible, keep useful records, and give them authority to correct problems. Make the route to a complaint clear to the people affected.
Ask together: Who will answer for the outcome and help put things right?
Truthfulness
Truthfulness means making claims that match the evidence and being clear about what we do not know. It includes correcting mistakes rather than protecting a polished story.
Check sources, label guesses, and distinguish a trial result from a proven benefit. If AI helped produce a claim, someone still needs to take responsibility for checking it.
Ask together: What can we honestly say, and what still needs checking?
Editorial ratings
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