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Stop unsafe decisions first, then establish exactly what happened, who was affected, and whether the bank can safely resume. A sound response treats an AI failure as both a model-risk event and an operational-resilience incident: assign an incident lead, preserve evidence, contain the model or route work to a validated fallback, investigate the cause, remedy affected outcomes, and reopen only with documented approval and heightened monitoring.
This checklist is designed for bank directors, model-risk teams, incident managers, compliance and legal functions, and technology leaders. It can be fitted to an existing incident-management process rather than run as a separate AI procedure.
Which rules and frameworks apply?
The required response depends on the bank’s jurisdiction, the use case, and the harm. The following authorities are not interchangeable: some are supervisory guidance, some are voluntary frameworks, and some create obligations only when specific facts trigger another law or rule.
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| Authority | Status | Scope | How it helps during an incident |
|---|---|---|---|
| Federal Reserve Board, OCC and FDIC revised interagency model-risk guidance (April 17, 2026) | U.S. supervisory guidance; not a prescriptive enforceable checklist | Most relevant to organizations above $30 billion in total assets, while smaller institutions with significant model-risk exposure may also use it; covers model development and use, validation and monitoring, governance, controls and third-party products | Sets expectations for governance, independent validation, monitoring, vendor oversight and risk-based controls. It superseded earlier interagency guidance, including material commonly associated with SR 11-7. |
| NIST AI Risk Management Framework 1.0 (January 26, 2023) | Voluntary, non-sector-specific framework | Any organization; not a banking regulation | Its Manage outcomes support planned responses, recovery from previously unknown risks, assigned authority to supersede, disengage or deactivate systems, and consideration of viable non-AI alternatives. |
| NIST AI RMF Playbook | Voluntary suggestions; expressly not a checklist or ordered sequence | Any organization | Provides ideas for post-deployment monitoring, user feedback, appeals and overrides, incident response, recovery, decommissioning and change management. |
| UK PRA Supervisory Statement SS1/23 (current version published and effective April 23, 2026) | UK supervisory statement | UK-incorporated banks, building societies and PRA-designated investment firms using internal models for specified regulatory-capital approaches | Provides principles for model identification and classification, governance, development, implementation and use, independent validation and model-risk mitigants. It does not apply to every bank or every AI system. |
| Basel Committee operational-risk principles | International supervisory principles implemented through national regimes | Bank operational-risk and resilience programs | Supports integrating AI incidents with governance, ICT incident management, business continuity, timely information transmission and disclosure processes. It does not establish one universal AI-notification deadline. |
The 2026 U.S. interagency guidance says generative and agentic AI are outside its scope because they are novel and rapidly evolving. Banks should still apply their existing governance and risk-management practices to systems outside that scope. The agencies announced plans for a future request for information on banks’ use of AI; check the current agency position before relying on any later development.
None of these sources supplies a universal incident clock. Notification, customer-remedy and disclosure duties must be determined from the incident facts, the applicable product and consumer rules, privacy or cybersecurity requirements, contracts and the governing jurisdiction.
The eight-step response checklist
1. Detect the failure and formally declare an incident
Define “suspected model incident” in the bank’s policy before an event occurs. Practical categories include material performance drift or error, unexpected or inconsistent outcomes, harmful decisions, contaminated data, misuse, a security attack, a vendor outage or defect, and loss of a required safeguard. This is a bank-defined operating taxonomy, not an exhaustive regulator-published list.
- Record the detection time, reporter, model name and version, application, deployment environment and suspected affected period.
- Open an incident record and appoint an accountable incident lead. Set an initial severity that can change as evidence improves.
- Preserve the model artifact, code or configuration version, input and output logs, monitoring alerts, change tickets, vendor notices and downstream decision records under normal retention and any legal-hold rules.
- Freeze routine changes to the affected workflow unless the incident lead authorizes them as containment actions.
Do not wait for a perfectly proven root cause before declaring an incident. A credible indication of unsafe outcomes is enough to start controlled containment.
2. Contain unsafe outcomes and protect people
Compare observed behavior with the model’s approved intended use, operating limits, monitoring thresholds and risk appetite. Ask whether an automated workflow is still making, communicating or executing decisions while the investigation is under way.
Use the least risky control that actually stops ongoing harm, with authority and escalation paths defined in advance:
Rank #2
- Limit a feature, product, geography, customer segment or transaction type.
- Add mandatory human review before a decision is communicated or executed.
- Route new cases to a validated prior model, a separately approved alternative or a manual process.
- Roll back to a validated version when the rollback itself is understood and tested.
- Disable or decommission the model when outcomes are inconsistent with intended use, harm is severe or irreversible, risk is accelerating, or no safe compensating control exists.
NIST’s framework specifically calls for assigned mechanisms to supersede, disengage or deactivate a system and for consideration of viable non-AI alternatives. A fallback is only viable if it has enough capacity, trained staff, access to required data and a known control standard; an untested “manual” queue is not a safe plan.
3. Scope the impact, not just the technical defect
Build an affected-population view that can be updated as facts change. Identify:
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- Model and dependency versions, third-party components, upstream data feeds and downstream systems.
- Start and end dates, processing volumes, users, customers and counterparties.
- Confirmed errors, plausible exposure and cases already reviewed, separating each category.
Estimate error rates and severity through validated comparisons, sample review and affected-population analysis. Examine financial loss, credit outcomes, fair-lending or other consumer-protection concerns, fraud or anti-money-laundering effects, privacy, cybersecurity, operational disruption, legal exposure and reputation as relevant to the use case. Do not assume that every dimension applies to every model.
Document evidence, assumptions, unknowns and the basis for the severity and escalation decision. Preserving uncertainty is part of the record; replacing it with an unsupported point estimate makes later remediation and regulatory explanation harder.
4. Mobilize the right owners and decision authority
Use the bank’s established incident-command structure, with explicit responsibility for decisions and communications. Bring in, as facts require:
Rank #3
- The business and model owners, including the developer or implementation owner.
- Independent model validation or model-risk management.
- Operations, data and technology teams, plus cybersecurity for suspected attack or access-control issues.
- Operational risk, compliance, legal and privacy advisers.
- Vendor-management staff and the relevant supplier.
- Customer support, complaints, communications and conduct-risk teams.
For a vendor model, obtain the supplier’s incident timeline, affected versions, known customer impact and proposed fix, but do not outsource accountability. The U.S. interagency guidance expects a bank to understand and monitor third-party products used in its model-risk activities. Test the vendor’s explanation against the bank’s own data, implementation and outcomes.
5. Investigate the cause and choose treatment
Examine the entire decision chain rather than only the model file:
- Input data quality, pipeline breaks, schema changes, missing or contaminated records and feature drift.
- Model, prompt, rule, configuration or dependency changes, including changes outside the model repository.
- Implementation defects, scoring-to-decision mapping, threshold changes and batch or real-time processing differences.
- User behavior, access controls, adversarial manipulation and unusual workload or external events.
- Vendor services, APIs, infrastructure and version dependencies.
Classify the contributing causes as deterioration, implementation defect, misuse, adversarial activity, novel behavior or an external event; more than one can apply. Do not keep using a model simply because the cause is unknown. Compare mitigation, avoidance, transfer and acceptance of residual risk using these factors:
- Severity and reversibility of potential harm.
- Materiality of the model and sensitivity of the decision.
- Speed at which exposure is growing.
- Confidence in diagnosis and in the proposed correction.
- Safety and capacity of a manual, prior-version or alternative process.
- Effects on availability and on outcomes for different groups.
- Third-party dependencies and contractual constraints.
- Legal, regulatory, customer-notice and disclosure duties.
Before relying on a fix, overlay or new threshold, use independent validation and outcome analysis proportionate to the model’s risk and use. A technically plausible patch is not evidence that affected decisions are now safe.
6. Decide remedies and communicate through the right channels
Map the affected decisions to the people and entities that received them. For each population, determine whether the bank needs case review, reversal, reprocessing, correction, an appeal opportunity, compensation or another remedy under applicable law and policy.
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Give affected users a practical channel to report a problem and, where appropriate, obtain human review or override. The NIST Playbook identifies user input, appeals and overrides as elements to capture in post-deployment monitoring. Keep a record of who was contacted, what was disclosed, response rates, unresolved cases and decisions not to contact particular populations.
7. Recover only under controlled approval
Set objective acceptance criteria tied to the failure mode and intended use. The evidence should show that the fix, replacement or fallback performs acceptably on relevant data, that safeguards operate, and that residual risk is understood.
- Complete testing and independent validation appropriate to the model’s materiality.
- Obtain required business, model-risk, compliance, legal and technology approvals.
- Record residual risk, compensating controls and any time-limited restrictions.
- Re-enable in stages when practical, beginning with a controlled population or low-risk path.
- Increase monitoring frequency and define rollback triggers before expanding use.
- Keep incident tracking, user feedback and change management active after service resumes.
Approval to reopen is a controlled risk decision, not a declaration that the model can never fail again. If acceptance criteria are not met, keep the model disabled or restricted and continue the fallback.
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8. Learn and update the control system
Close the incident with a root-cause analysis and a factual timeline covering impact, decisions, evidence, communications, remediation and residual risk. Review near misses as well as confirmed harm.
Update the controls that the event exposed, which may include:
- Monitoring metrics, thresholds, alert routing and population-level outcome tests.
- Validation plans, model inventory records and intended-use documentation.
- Vendor due diligence, contractual notification terms and independent testing rights.
- Incident playbooks, business-continuity capacity and staff training.
- Access controls, change approvals, release testing and rollback procedures.
- User-feedback, appeal, override, decommissioning and recovery processes.
When should a bank turn off an AI model?
Turn it off, disengage it or remove it from a decision path when continued operation can produce material harm faster than the bank can detect and correct it, when outcomes are outside approved use, when a required safeguard has failed, or when no validated compensating control is available. Immediate shutdown is not automatically the right answer for every anomaly: a narrowly scoped human-review gate or safe fallback may stop harm while preserving essential service. The decision should weigh reversibility, model materiality, diagnosis confidence, alternative capacity, fairness and legal duties, with the incident lead using the authority assigned in the bank’s policy.
A shutdown without a capable fallback can create its own operational and customer harm. Before deployment, document who can deactivate the system, how dependent workflows will be stopped, what alternative will take the load, how records will be preserved and which rollback or reopening conditions apply.
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What a board or executive committee should require before the next incident
- An inventory showing every material AI or statistical model, owner, version, intended use, dependencies and fallback.
- Clear severity definitions, incident authority and escalation contacts across business, model risk, technology, operations, legal and compliance.
- Tested deactivation, rollback and manual-processing procedures, including capacity assumptions.
- Monitoring that can detect drift, unexpected outcomes, subgroup effects, data-quality failures and vendor events.
- Pre-agreed evidence-retention, customer-remedy and communications workflows.
- Controlled re-entry criteria, independent validation requirements and rollback triggers.
- Periodic exercises that include a vendor failure, a data-contamination event and a harmful decision pattern.
These controls connect model risk to operational resilience, cybersecurity, business continuity and third-party risk. That integration is the practical lesson shared by the U.S. supervisory guidance, NIST’s voluntary framework and Playbook, the PRA’s model-risk principles and Basel operational-risk principles.
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