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Assess an AI product as both a third-party relationship and, when it meets the applicable definition, a model-risk issue. Start with its use, data, connections and potential impact; then review the provider and system, test them in your institution’s workflow, contract for evidence and controls, and monitor the service after launch. For U.S. banking organizations, the depth of review should be proportionate to the activity’s risk, complexity and materiality—not to the vendor’s use of the word “AI.”
Start by defining the system’s role and risk
Before requesting a vendor’s sales materials, document what the tool will do in your institution. The answers determine the right reviewers, evidence and level of control.
Write down the use and potential consequences
- Business purpose, intended users and any customers or other people affected.
- Decisions, recommendations or workflows the system may influence, and whether staff review its output before acting.
- Inputs and outputs, including sensitive or customer information, and whether information passes to other systems.
- Connections to internal systems, the tool’s operational criticality and plausible outcomes if it is wrong, unavailable or compromised.
Name an accountable business owner and involve procurement, security, privacy, legal and compliance, risk, and model-validation specialists as appropriate. The voluntary NIST AI Risk Management Framework (AI RMF) treats risk management as a lifecycle activity involving management and other AI actors; it does not replace applicable law or supervisory obligations.
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Determine whether the service is a statistical or quantitative model, non-generative AI, generative AI, agentic AI, or a combination. A marketing label alone does not establish that model-risk guidance applies. The Federal Reserve’s guidance, revised April 17, 2026, defines a model using quantitative estimation grounded in statistical, economic or financial theory and expressly excludes generative and agentic AI. It says the guidance is most relevant to banking organizations above $30 billion in assets, while noting that it may also be relevant to smaller banks with significant model risk. Check the guidance’s scope against your institution and system rather than treating it as a universal rule: Supervisory Guidance on Model Risk Management.
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Set the third-party risk tier
Consider the tool’s access to sensitive information, role in transaction processing or essential services, customer contact, and potential effect on the bank’s operations or financial condition. A lower-impact internal productivity assistant and a system that can influence fraud detection, underwriting or customer treatment may warrant different diligence. Those examples illustrate proportionality; they do not mean every tool in a category has identical risk. The interagency guidance calls for more comprehensive oversight of higher-risk or critical activities: Interagency Guidance on Third-Party Relationships.
Assess the provider, not just the model
A capable model does not compensate for an unreliable provider, weak security or an unmanageable dependency. Ask for evidence that applies to the specific service and examine its coverage, date, exceptions and limitations.
- Governance and capability: named accountability, risk management, independent testing, remediation, experience, staffing, key-person dependencies and continuity planning.
- Business and financial resilience: ownership, financial condition, strategy and ability to sustain the service through disruption.
- Security and data controls: data handling, access controls, encryption, secure development, vulnerability testing, incident response and the service’s system boundaries.
- Subcontractors and dependencies: downstream providers’ roles, locations where relevant, data access, controls, incident notification and change practices.
- Assurance evidence: audit reports, SOC reports, certifications or conformity assessments. Confirm which service, systems and period they cover, what exceptions they identify, and whether they are relevant to your use.
These review areas reflect the interagency third-party relationship guidance. A certificate or audit report is evidence to evaluate, not blanket assurance about every component or use of a service.
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Ask what the AI service does—and what the vendor will disclose
Request documentation for the service as deployed or proposed, rather than relying only on generic descriptions of the vendor’s product family.
- Intended purpose, architecture, dependencies and the model or service version.
- Data provenance and use, including what information enters and leaves the service.
- Performance evidence, known limitations and failure modes relevant to your task.
- How updates are made, tested and communicated, and how you can identify which version produced an output.
- What the vendor will disclose for independent assessment, and what it will not disclose.
Some vendors may withhold underlying code, data or methodology. That does not transfer responsibility for the institution’s assessment: record the resulting uncertainty, seek alternative evidence, add controls where suitable, or reject the risk if it cannot be made acceptable. The Federal Reserve model-risk guidance recognizes limits on access to vendor information while retaining the relevance of validation principles.
For generative AI, examine data handling and components
Establish whether prompts, uploaded material, customer information or generated outputs are retained, disclosed or used for training, and what rights the vendor has to use them. Review privacy, information security, intellectual-property terms and risks from third-party components. The NIST Generative AI Profile recommends adapting governance and procurement controls to generative AI risks; it identifies software bills of materials, service-level agreements and attestation reports as possible transparency measures, not universal legal requirements.
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Validate the tool in the intended workflow
Vendor benchmarks can inform an assessment, but they do not show how a service will perform with your data, configuration, integration and human review. Set acceptance criteria before testing, document results and test the end-to-end workflow.
- Define the task and criteria. Specify acceptable performance, error types, reliability and escalation requirements for the proposed use.
- Use representative cases. Include ordinary cases, edge cases and conditions that could produce different outcomes for affected groups. Use data and scenarios appropriate to the use, with suitable safeguards for sensitive information.
- Test relevant behaviors. Assess accuracy, stability, robustness, security behavior and the degree of explainability or interpretability needed by decision-makers. Check whether staff can identify, correct or escalate failures.
- Test the full path. Include data transformations, interfaces, thresholds, human review and downstream decisions; each can change real-world outcomes beyond the vendor’s model-level benchmark.
- Record the decision. Preserve assumptions, test cases, results, limitations, approvals and remediation actions so the launch decision can be reviewed later.
NIST recommends iterative, documented testing, evaluation, validation and verification in its Generative AI Profile. For systems within its scope, Federal Reserve model-risk guidance calls for understanding conceptual soundness, design, development data and performance. In higher-impact workflows, define who remains accountable and make sure authorized staff can override, escalate or suspend the system.
Compare vendors on the same evidence
Use one use case and a consistent evidence set for every option. The comparison should reflect governability and relationship risk as well as task performance; these criteria synthesize regulator and NIST guidance rather than rank vendors.
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| Comparison area | Questions to ask | Evidence to compare |
|---|---|---|
| Use-case fit | Does the service perform the intended financial-services task, and where are its limits? | Results from representative testing, known failure modes and version-specific performance evidence. |
| Transparency and evidence | Can your institution assess the service and identify meaningful changes? | Documentation, test results, change notices, audit scope and support for independent validation. |
| Data governance | What data can be accessed, retained, reused, transferred or deleted, and under what rights? | Data-handling terms, training-use restrictions, location information, privacy and intellectual-property provisions. |
| Security and resilience | How are system boundaries, incidents, recovery and continuity managed? | Relevant security controls, incident processes, recovery arrangements and assurance reports. |
| Dependencies and exit | Can you see downstream dependencies, move the service or data, and transition if needed? | Subcontractor disclosures, portability, transition support, switching costs and feasible alternatives. |
| Governability | Can staff oversee outputs and can the institution monitor, escalate and remediate problems? | Human controls, logs, monitoring hooks, escalation paths, remediation commitments and contract rights. |
| Relationship risk | How consequential is dependence on this provider for your institution and customers? | Provider stability, service criticality, customer impact, concentration and regulatory access arrangements. |
Do not let a strong score on one dimension obscure a material weakness elsewhere. If an evidence gap cannot be resolved, record its effect on the decision and consider compensating controls or whether the service is acceptable at all.
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Agree on protections before production use, tailored to the service’s risk tier. The contract should give the institution enough information and leverage to oversee performance and respond when the service changes or fails.
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- Data rights and limits: specify permitted uses, access, retention, return or deletion, reuse, resale, disclosure and restrictions on training with institutional or customer data.
- Reporting and change notice: arrange timely access to relevant performance, security, financial, audit and control information; specify notice for incidents, material service or model changes, new subcontractors and compliance lapses.
- Assurance and remediation: address audit or independent assessment rights, remediation obligations and regulatory access where appropriate.
- Subcontractors: set approval or notice expectations, require relevant flow-down controls and keep the provider accountable for subcontracted work.
- Continuity and exit: cover resilience, recovery objectives, testing where appropriate, transition support, reasonable transition periods, data and records export, termination rights and secure deletion.
These topics align with the interagency third-party relationship guidance. NIST’s generative AI profile also identifies service-level agreements as a possible control; neither source makes every listed term mandatory in every contract.
Monitor the service and define when to intervene
Assign an owner and review cadence based on risk. Ongoing oversight should cover the service in operation, the provider relationship and the controls promised in the contract. The Federal Reserve describes monitoring as a way to confirm control quality and contractual performance, escalate significant concerns and respond to them: Third Party Risk Management, May 2024.
- Track performance and outcomes, complaints, incidents, audit findings, security or compliance changes and service-level performance.
- Review changes in provider financial condition, ownership, staffing, subcontractors and relevant data locations.
- Reassess when the use case, model or service version, data, integrations or provider changes.
- Set clear triggers and decision authority for corrective action, restricted use, suspension, transition or termination.
Apply the right framework to the right system
For U.S. banking organizations, the interagency third-party guidance addresses the lifecycle of third-party relationships. Federal model-risk guidance is relevant only when the system and institution fall within its scope; its April 2026 revision expressly excludes generative and agentic AI. That exclusion is not a finding that those systems are risk-free or exempt from other obligations. NIST’s AI RMF is voluntary, and requirements beyond these sources depend on the institution, jurisdiction, product and use. See the interagency guidance, model-risk guidance and NIST AI RMF for their respective scopes.
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