Concern 896 · Working draft · editorial review in progress
Model risk management (MRM)
Model risk management is the work of identifying, testing and controlling harm from models that are wrong, poorly used or no longer suited to their task.
Understanding the concern
A model can fail because of its design, its data or the way people interpret its output. Managing that risk means keeping an inventory, assigning responsibility, checking limitations and watching performance over time. Generative AI adds challenges because a fluent answer is harder to evaluate than a single numerical estimate.
Banking guidance is often cited here, but its scope matters. US SR 26-2 replaced SR 11-7 in April 2026 and excludes generative and agentic AI from its scope. Organizations can still learn from model-governance practices without claiming that this guidance directly covers every chatbot. Think of an equipment inspection program: useful habits can transfer, but different equipment needs different tests.
An example
A company assumes its existing spreadsheet-model review fully addresses an AI assistant that can generate advice and use tools.
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.
- Model risk management
- The work of identifying, checking, and managing the risks of using models in decisions.
Evidence and its limits
SR 26-2's scope excludes generative and agentic AI. NIST's profile is voluntary guidance, not an extension of that banking supervisory letter.
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.
- NIST AI RMF (opens in a new tab)
framework
- ISO/IEC 42001 (opens in a new tab)
standard
A question to discuss
Which risks does the current review actually cover, and what additional checks does this AI use require?
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?
Stewardship
Stewardship means caring for resources and systems that others depend on, including people who will use them later. It looks beyond the benefit of a quick launch.
Plan for maintenance, energy use, data protection, and eventual shutdown. Ask who will look after the tool when the original team moves on and how future users can avoid inheriting hidden costs.
Ask together: What are we responsible for caring for over time?
Prudence
Prudence means using careful judgment before and during action. It balances worthwhile opportunities with the need to avoid harm that could have been anticipated.
Start with a limited trial, set stopping points, and prefer choices that can be undone when uncertainty is high. Review what happens rather than assuming the original plan remains sound.
Ask together: What is a careful next step, and when should we stop or change course?
Editorial ratings
These are editorial judgments from the register, not measured probabilities or predictions about your situation.
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