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Banks protect customer data in AI systems by controlling what information the system can access, how it may be used, who operates it, and how its performance and security are monitored. AI does not create an exemption from existing privacy, information-security, cybersecurity, consumer-protection, or third-party oversight responsibilities. The safeguards a bank needs depend on the use case: a tool that summarizes internal documents presents different risks from an agent that can access customer accounts or authorize transactions.
This explanation focuses on U.S. federal banking regulators. It describes recognized control areas, not a claim that every bank uses an identical technical design or that any safeguard eliminates risk.
What protecting customer data in AI means
An AI system is part of a bank’s broader information and technology environment. Customer data may be involved when a system is trained, supplied with information to answer a question, connected to internal applications, or operated by a vendor. The practical task is to manage those data flows and the system’s capabilities—not simply to label a tool “AI” and assume a special set of protections applies.
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That baseline matters because the right protection depends on the system’s purpose and reach. A bank needs to understand what information a system handles, what it can do with that information, and what could happen if the system or a provider fails. Regulatory guidance describes control areas and responsibilities; it does not establish one universal technical architecture for every bank and AI application.
How banks assess an AI system before relying on it
Define the use case and accountability
Before deployment, governance begins with a clear description of the system’s job and the decisions or work it supports. The bank needs to determine who owns the use, who is responsible for oversight, what data the system can reach, and how consequential an error or unauthorized action could be. A system used for a narrow internal task is not automatically equivalent in risk to one that influences customer decisions or can interact with accounts.
The OCC, Federal Reserve, and FDIC issued revised model-risk guidance in 2026. For systems within its scope, it discusses model development and use, validation and monitoring, governance and controls, and vendor products. Its approach is risk-based: practices appropriate for one institution or use may not suit another. The guidance does not establish prescriptive, enforceable standards, and it expressly excludes generative and agentic AI. It should not be treated as a comprehensive rule for every generative AI deployment.
Match controls to the system’s data and capabilities
A useful way to reason about risk is to ask what the system processes and what it can do. These are practical comparison factors drawn from the risks and governance considerations identified by regulators, not an official regulator checklist:
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- Data sensitivity and volume: Does the system handle sensitive customer information, and how much information is involved?
- Provider relationship: Is the system operated by the bank, or does it rely on an outside provider such as a technology vendor, cloud provider, or fintech?
- Output behavior: Could customer or proprietary information appear in generated answers or other outputs?
- Access and action: Can the system reach customer information, interact with accounts, or authorize transactions?
- Use-case criticality: What would be the consequences if the system produced an incorrect result, exposed information, or became unavailable?
- Oversight and jurisdiction: How well can the bank assess the provider and system, and what laws and supervisory framework apply?
Considering these factors helps a bank decide how much scrutiny and oversight a specific use warrants. They do not imply that a particular technical measure is required in every case.
How privacy and security controls apply to AI data
AI can involve large volumes of data, which makes privacy and security part of the system’s design and operation. OCC testimony to Congress on May 13, 2022, emphasized that banks must meet applicable privacy and information-security requirements while using AI. It also highlighted cybersecurity practices to safeguard sensitive data and to prevent or limit corrupted data from compromising AI applications.
For a bank, the central questions are what information is being used, for what permitted purpose, and under whose control. The bank must account for the data involved in the particular application and maintain appropriate cybersecurity practices around it. The sources do not prescribe a single technical architecture or a complete control checklist that applies identically to all AI uses.
Generative AI creates an output risk
Generative AI can return information in its responses, so a data-protection review cannot stop at the point where information enters a system. Federal Reserve Governor Michael Barr warned on April 4, 2025, that proprietary or customer information could be included inadvertently in generative AI outputs or responses. This is a risk to manage, not evidence that every generative AI system exposes bank data.
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That output concern differs from a system’s ability to take action. Barr also noted that AI agents accessing sensitive customer data or authorizing transactions can attract malicious actors. A system with account access or transaction authority therefore raises questions beyond what it might say: the bank must consider the risks associated with those capabilities and the information they can reach.
How banks oversee AI vendors and other third parties
Banks may rely on outside providers for AI systems, models, cloud services, or expertise. Using a provider can add risk because information, operations, or technical capabilities are involved outside the bank’s direct control. It does not transfer the bank’s responsibility for safe and lawful activity.
OCC testimony identifies several relevant vendor controls: robust due diligence, contract management, ongoing oversight scaled to the service’s criticality, clear roles and responsibilities, data ownership, permitted uses, and privacy and security limits on data shared with providers. These controls address both the relationship and the handling of information within it. For example, a bank needs to establish what the provider is allowed to do with shared data and how the parties’ responsibilities are defined, rather than leaving those questions implicit.
In June 2023, the FDIC, Federal Reserve, and OCC issued final interagency guidance on third-party risk management. It describes oversight across planning, due diligence, contract negotiation, ongoing monitoring, and termination. It also states that a banking organization’s use of third parties does not diminish or remove its responsibility to perform activities safely and comply with applicable laws and regulations, including those protecting customer information.
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The agencies proposed revised third-party risk guidance on September 11, 2026. That announcement was a proposal for public comment, not a final rule. It should not be described as final unless a later official announcement confirms that status.
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For models within its scope, the 2026 revised model-risk guidance discusses testing, validation, outcomes analysis, monitoring, governance, and controls. These practices support a bank’s ability to assess how a model performs and maintain oversight after it is put to use. The guidance takes a risk-based approach rather than setting one testing protocol for every model or institution.
Scope is important: the revised model-risk guidance expressly excludes generative and agentic AI. Its discussion of model oversight should not be presented as a rule that directly governs every generative AI system. Banks still have broader privacy, information-security, cybersecurity, and third-party responsibilities when using AI; the distinction is about the scope of this particular guidance.
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Monitoring also matters because oversight is not just a decision made before deployment. The interagency third-party guidance includes ongoing monitoring as part of vendor oversight, while the model-risk guidance discusses validation and monitoring for models within its scope. The sources identify these areas of practice but do not establish a single universal test schedule, technical method, or quantified effectiveness measure.
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What U.S. regulatory guidance does—and does not—establish
The regulatory record shows how oversight has developed, but documents have different purposes and legal status. An early information request is not a rule; testimony describes supervisory concerns; final guidance is distinct from a later proposal. The key milestones are:
| Date | Document or event | What it establishes |
|---|---|---|
| March 31, 2021 | OCC request for information on financial institutions’ AI use | Sought views on AI use, governance, risk management, controls, data processing and use, and third-party oversight; it is background, not a current rule. |
| May 13, 2022 | OCC Deputy Comptroller Kevin Greenfield’s congressional testimony | Discussed AI risks, including privacy, security, and third-party risk, and stated that banks must comply with applicable privacy and information-security requirements when using AI. |
| June 2023 | Final interagency third-party risk guidance from the FDIC, Federal Reserve, and OCC | Explained that third-party use does not remove bank responsibility and described oversight practices, including protection of customer information. |
| November 22, 2024 | Federal Reserve Governor Michelle Bowman’s speech on AI and banking | Stated that AI remains subject to current legal and regulatory requirements and scrutiny. |
| April 4, 2025 | Federal Reserve Governor Michael Barr’s discussion of generative AI and banking | Highlighted possible inadvertent exposure of proprietary or customer information in outputs and risks involving agents with sensitive-data access or transaction authority. |
| 2026 | Revised interagency model-risk guidance | Covered model development and use, validation and monitoring, governance and controls, and vendor products; it is risk-based, not prescriptive or enforceable, and excludes generative and agentic AI. |
| September 11, 2026 | Proposal for revised interagency third-party risk guidance | Was proposed for public comment; the proposal itself is not a final rule. |
This is a U.S. federal banking-regulator-focused account, not a survey of each bank’s actual controls or a description of every country’s law. The cited materials support control categories and institutional responsibilities, not a universal mandatory technical design or a measured estimate of how much safeguards reduce exposure.
How to understand a bank’s AI data safeguards
For customers, the most accurate takeaway is that AI use remains within ordinary banking duties to protect information and manage security and third-party risks. Whether a particular safeguard is adequate depends on the system’s purpose, the data it can handle, its access or action capabilities, and any provider involved. The available federal guidance explains those responsibilities and risk areas; it does not certify that every bank’s systems are identical or risk-free.
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