AI can make third-party risk management (TPRM) more manageable by organizing evidence, surfacing changes and routing potential issues for review. It should not make consequential risk decisions on its own. People still need to set risk tolerance, judge evidence and context, investigate exceptions, and approve actions. That division—automation for repeatable information work, human accountability for judgment and decisions—is consistent with the voluntary NIST AI Risk Management Framework and the risk-based lifecycle in U.S. banking guidance.
What AI can—and cannot—do in TPRM
Third-party assessments involve information from many sources and recur throughout a relationship. AI tools can assist with collecting and organizing that information, summarizing assessment materials, identifying apparent gaps or changes, and supporting ongoing monitoring. These are useful capabilities when they make evidence easier for a reviewer to find and assess.
An AI-generated summary, score or alert is not proof that a supplier is safe or unsafe. It is an output to check against the underlying evidence and the relationship’s context. NIST warns that third-party technologies may be complex or opaque, and that their risk tolerances may not align with those of the organization using them. A flag should therefore prompt inspection, not automatically settle a decision.
The official sources cited here do not establish a specific improvement in TPRM accuracy, review speed, cost or risk reduction from AI. Treat those outcomes as matters to validate in your own environment rather than assumed benefits.
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Where human oversight belongs
Oversight is more than a person approving a model’s final answer. It requires clear decision rights, reviewers with suitable training, and a way to inspect, challenge and escalate outputs. NIST’s AI RMF calls for defined human-AI roles and oversight, as well as executive responsibility for risk decisions.
- Set risk tolerance and policy: People decide which risks are acceptable and what evidence or controls are required.
- Assess context and evidence quality: A reviewer checks whether a source is current, relevant, complete and credible, and whether a summary faithfully represents it.
- Investigate exceptions: Conflicting evidence, uncertain outputs, material changes and high-impact alerts need a route to a qualified person.
- Approve consequential decisions: People retain accountability for decisions such as accepting material residual risk, imposing remediation, escalating concerns or ending a relationship.
- Maintain oversight of the AI system: The organization documents relevant AI components, monitors their behavior, tests them against its needs and plans for failures.
These are practical controls drawn from NIST’s framework and playbooks, not a prescribed automated TPRM workflow. The NIST AI RMF is voluntary U.S. guidance, and NIST describes it as a living framework that is being revised. See the NIST AI Risk Management Framework, AI RMF Core, and Manage Playbook.
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Apply automation across the relationship lifecycle
For U.S. banks, the 2023 interagency guidance describes third-party risk management across planning, due diligence and selection, contract negotiation, ongoing monitoring, and termination. It says practices should be proportionate to the bank’s risk profile and the relationship’s complexity and criticality. This is banking guidance, not a universal legal requirement for every industry.
- Planning: A system can help maintain a third-party inventory and organize information about proposed relationships. People decide the business need, assess the relationship’s importance and determine the depth of review.
- Due diligence and selection: AI can help collect and summarize assessment evidence or identify missing items. Reviewers validate the sources and evaluate findings in light of the proposed service and the organization’s risk tolerance.
- Contract negotiation: AI may help organize requirements and surface terms for review. People determine which obligations are necessary and approve the agreement; the tools cited here do not prescribe automated contract decisions.
- Ongoing monitoring: Automation can help surface reported or detected changes for investigation. People determine whether a change is material, what response is appropriate and whether escalation is needed.
- Termination: Systems can help keep relationship records organized. People remain responsible for deciding when to end a relationship and managing the associated risks and obligations.
OCC Bulletin 2023-17 sets out the banking guidance; the interagency release describes its issuance. The lifecycle is a useful organizing model beyond banking, but applicability depends on sector and jurisdiction.
How to evaluate an AI-enabled TPRM approach
Whether assessing an internal tool or a provider’s system, look beyond the presence of an AI score. The following comparison criteria synthesize NIST AI RMF outcomes and U.S. banking-agency TPRM guidance; they are not a published vendor ranking or certification checklist.
| What to assess | Questions for reviewers |
|---|---|
| Evidence provenance and traceability | Can a reviewer trace a summary or alert to the source evidence and see what information it used? |
| Uncertainty and exception handling | Can the system signal uncertainty, missing information or conflicting evidence and route those cases for review? |
| Human review and decision rights | Are review, escalation and override responsibilities clear, and can people challenge an output before a consequential action? |
| Third-party AI transparency | Can the organization understand and document relevant AI systems, components and data well enough to manage their risks? |
| Monitoring and contingency support | Are there processes to test and monitor the system, respond to incidents and manage a failure or interruption? |
| Fit to risk and criticality | Does the approach reflect the organization’s risk tolerance and the relationship’s importance, complexity and applicable sector guidance? |
NIST’s Govern Playbook and Manage Playbook offer suggestions for governing and managing AI risks, including third-party AI. NIST AI RMF 1.0 provides additional context on opacity and alignment of risk tolerance in its framework PDF.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What U.S. banking teams should know about guidance status
As of October 4, 2026, the 2023 interagency TPRM guidance remains the issued guidance described by OCC Bulletin 2023-17. On September 11, 2026, federal banking agencies proposed replacing it. The proposal is not final and should not be described as a binding replacement standard. Check the OCC proposed bulletin and joint release for status before relying on it; final replacement depends on future agency action.
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