SR 26-2 vs NIST AI RMF: how bank model risk requirements map to Govern, Map, Measure, Manage

Function-by-function mapping

The table pairs SR 26-2 lifecycle elements with the NIST AI RMF function that fits best. The pairings are analytical inferences, not mappings either document states. Treat each row as a starting hypothesis to check against the full subcategory text.

• Model governance and policies maps to Govern: SR 26-2 says all lifecycle components benefit from governance supported by clear policies and effective controls. Govern 1 covers policies, processes and practices for mapping, measuring and managing AI risk. Govern 1.1 covers legal and regulatory requirements involving AI. Overlap is strong. Evidence: approved policies, a framework for assessing the magnitude of model risk, and documented regulatory obligations.
• Development purpose and intended use maps to Map: SR 26-2 says effective development begins with a clear statement of purpose aligned to intended use. Map is the AI RMF function that frames the context in which an AI system operates. Overlap is partial. Evidence: intended-use statements tied to each system and use case.
• Ongoing monitoring and validation activity maps to Measure: SR 26-2 says monitoring plans may assess model limitations at development and over time. Measure 1.2 calls for regular assessment of metrics and control effectiveness. Overlap is moderate. Evidence: monitoring plans, performance thresholds, outcomes analysis and validation results.
• Response to issues maps to Manage: SR 26-2 says monitoring plans may include procedures for responding to issues before and after approval for use. Manage is the AI RMF function for acting on identified risks. Evidence: documented escalation paths, remediation records and approval decisions.
• Model information and inventory maps loosely to Govern and Map: SR 26-2 notes it is common industry practice to maintain a comprehensive set of information for models under development or in use. The AI RMF has no single counterpart, so this row spans two functions. Evidence: a current record of each AI system, its purpose and its owner.
• Internal audit review maps to Govern: SR 26-2 describes internal audit as evaluating whether model risk management practices are rigorous and effective and whether policies are implemented accordingly. Govern is cross-cutting, which fits this assurance role. Evidence: audit plans, findings and follow-up on model risk practices.

Where the frameworks do not align

Authority is the first difference. SR 26-2 is supervisory guidance from the Federal Reserve, and it states that it sets no enforceable standards or prescriptive requirements. It also states that non-compliance will not result in supervisory criticism. The NIST AI RMF is voluntary. So the word "requirements" overstates what either document imposes. Each banking organization remains responsible for adopting model risk management practices that are appropriate and effective for its own risks.

Scope differs as well. SR 26-2 is expected to be most relevant to Federal Reserve-regulated banking organizations with over $30 billion in total assets, and it recognizes that practices vary with risk profile and model usage. The AI RMF is non-sector specific and use-case agnostic, intended for organizations of any size.

Structure and intent differ too. SR 26-2 follows the lifecycle of a model, from governance through development, monitoring and audit. The AI RMF is a set of functions with categories and subcategories, and Govern runs through the other three. A lifecycle stage can therefore touch several functions, and one function can serve several stages. That is why a one-to-one table will always involve judgment.

Gaps for AI systems and the evidence to hold

The mapping above covers model risk practices in general. AI-specific risk is a separate question. Do not assume SR 26-2 addresses generative or agentic AI in particular. That is something to confirm against its full text for your own institution. The NIST AI 600-1 Generative AI Profile is a cross-sectoral companion to the AI RMF and can serve as a supplemental layer for generative use cases. It is not bank-specific, and its risks and actions need review in the full document.

The practical gap for many teams is inventory and scoping. If the set of model information does not include generative AI use cases, the rest of the crosswalk has nothing to attach to. A second gap is evidence that controls are actually operating. Policies and validation reports show intent and periodic assessment. They do not show what a system did on a particular day.

For each mapped row, decide what artifact would demonstrate it. Governance rows need approved policies and documented obligations. Map rows need intended-use statements. Measure rows need monitoring results and evidence that metrics and control effectiveness are reassessed. Manage rows need records showing that issues triggered a response. Scale the depth of each artifact to the magnitude of model risk, as SR 26-2 contemplates.

What runs at runtime and what stays in process

Most mapped requirements are met by documentation, validation and governance processes. Policies, intended-use statements, validation, model inventory, model lineage and pre-deployment testing all sit in that category. A runtime control does not replace any of them.

A narrower set of controls operates in the execution path, on individual interactions and tool calls. Examples include allowing a call, redacting sensitive content, blocking an action, or escalating it for human approval. These controls relate to the Manage function, where a documented response becomes an action taken at the moment of use. They also relate to the Measure idea of assessing control effectiveness, because enforcement produces a record of what each control did.

The distinction matters for evidence. A validation report is produced periodically and describes a model. Evidence produced by runtime enforcement is generated per interaction and describes what the system was permitted to do and what it did. Both are useful to an examiner, but they answer different questions. Keeping them separate prevents a team from claiming that offline review covers behavior that only enforcement can show.

Building the crosswalk

A working crosswalk does not need to be elaborate. These steps keep it auditable and avoid duplicate effort.

• List SR 26-2 lifecycle elements: governance, development purpose, monitoring, internal audit and model information.
• Pair each with the closest AI RMF function and subcategory, and label the pairing as your interpretation.
• Name the evidence artifact and the owner for each pair, and mark whether it comes from documentation, validation or runtime enforcement.
• Add the Generative AI Profile as a supplemental layer for generative use cases and confirm they appear in the model information set.
• Define response procedures for issues found before and after approval, and scale effort to model risk.

Where Trussed fits

Trussed is relevant only to the runtime slice described above. It enforces policy in the execution path across models, agents, tools and MCP servers, with actions that include allow, redact, block and escalate for human approval, scoped to individual interactions and tool calls. That enforcement produces audit-ready evidence mapped to frameworks including NIST. It does not provide validation, model inventory, model lineage or pre-deployment testing, so it sits beside those processes rather than in place of them.

Insurance, a regulated financial sector, is where Trussed is deepest. Approved proof includes 18+ months of continuous production in a regulated enterprise, 99.95% uptime on live production traffic, and 100+ models under one policy plane. Trussed also holds NIST AI RMF alignment among its certifications and attestations. This page does not state or imply bank deployments.