AI usage policy template
AI Usage Policy Template for Financial Institutions
Financial institutions need a written AI usage policy before AI adoption spreads faster than governance. The policy should tell employees, vendors, risk teams, and business owners what is allowed, what needs review, and what evidence must be retained.
Use this page as a preview of a practical AI policy structure for banks, credit unions, and fintechs. The full Starter Kit includes a policy template that can be adapted to your governance committee, model risk framework, vendor program, and incident response process.
Why financial institutions need an AI policy now
AI use is already present in many financial institutions even when the institution has not formally approved an AI program. It may appear in vendor platforms, fraud detection, marketing analytics, document processing, customer service, cybersecurity tools, internal productivity software, and model-driven decision processes.
The policy gap is usually not awareness. It is repeatability. Without written boundaries, teams answer the same questions differently: who can approve a use case, whether customer data can be entered into a tool, whether vendor AI must be reviewed, how incidents are escalated, and what evidence should be retained.
A useful AI usage policy gives staff a practical operating model. It defines permitted and prohibited uses, clarifies review steps, aligns AI activity with model risk and third-party risk expectations, and creates a governance record for audit, board, and examiner review.
What to include in the policy
A strong AI policy should be specific enough to guide day-to-day decisions and flexible enough to adapt as tools change. The goal is not to ban useful technology. The goal is to establish a controlled path for responsible use.
The policy should address both internally developed AI and third-party AI embedded in vendor products. Many institutions underestimate vendor AI exposure because the feature is marketed as automation, analytics, workflow assistance, or decision support rather than as AI.
- Purpose, scope, and covered AI definitions
- Governance roles and approval authority
- Approved, restricted, and prohibited use cases
- Employee acceptable use and data-entry rules
- Vendor AI due diligence and contract requirements
- Model risk inventory, validation, and monitoring handoffs
- Data, privacy, security, and consumer-impact controls
- Incident reporting, exceptions, and policy review cadence
Regulatory references to align
AI policy language should connect to the institution's existing risk framework. For third-party AI, the policy should reference the interagency third-party risk management life cycle reflected in OCC Bulletin 2023-17. For model-related AI, it should use current model risk governance language, including SR 26-2 and OCC Bulletin 2026-13 where applicable.
SR 11-7 may still appear in legacy model risk policies, validation templates, audit programs, and board materials. A good AI policy can preserve continuity with those documents while moving current governance language forward. That matters because many exam and audit questions still arrive in familiar model-risk terms.
The policy should also support fair lending, privacy, cybersecurity, operational resilience, complaint management, and records retention obligations. AI governance is not a separate island; it is a coordination layer across existing control disciplines.
Sample policy language excerpts
Approved use cases: AI tools may be used only for institution-approved business purposes and only after the required review has been completed based on risk tier, customer impact, data sensitivity, vendor involvement, and model reliance.
Customer data: Employees may not enter confidential customer, member, account, employee, or nonpublic institution data into public AI tools unless the tool has been approved for that data type and contractual protections are documented.
Incident reporting: Any suspected AI-related incident, including inaccurate customer-impacting output, unauthorized data exposure, material model degradation, or prohibited use, must be escalated through the defined incident response process.
What to include: 10 policy sections
A policy should be concrete enough that teams can follow it without inventing a new approval process each time a tool appears. These sections form the backbone of the full template.
- 1Policy purpose, scope, and AI definitions
- 2Governance ownership and committee responsibilities
- 3Use case intake, risk tiering, and approval workflow
- 4Approved, restricted, and prohibited AI uses
- 5Employee acceptable use rules for generative AI tools
- 6Customer, member, employee, and institution data restrictions
- 7Third-party AI vendor diligence and contract requirements
- 8Model inventory, validation, monitoring, and change-control handoffs
- 9Incident, exception, complaint, and escalation requirements
- 10Annual review, board reporting, training, and evidence retention
Download the full AI policy template
The Starter Kit includes an editable AI policy framework with governance roles, acceptable use rules, vendor requirements, incident escalation language, and evidence prompts for board, audit, and examiner review.
FAQ
Do banks and credit unions need a formal AI policy?
A formal AI usage policy is a practical way to document ownership, approval paths, acceptable use, vendor expectations, monitoring, and incident escalation. Even smaller institutions benefit from a written policy because AI exposure often appears through vendors and employee tools before a formal AI program exists.
What is the difference between an AI policy and model risk management?
An AI policy sets institution-wide boundaries for AI use. Model risk management governs models through inventory, validation, monitoring, change control, and challenge. The two should connect, but they are not substitutes for each other.
Who should own the AI usage policy?
Ownership varies by institution, but compliance, enterprise risk, technology risk, legal, model risk, vendor management, information security, and affected business lines should all have defined roles. The policy should name an accountable executive owner.
How often should the AI policy be reviewed?
Review at least annually and whenever the institution approves a material new AI use case, changes a high-risk AI vendor, updates model risk standards, or receives relevant audit, board, or supervisory feedback.
Move AI use out of the gray zone.
Start with the free assessment, then use the AI policy template to define ownership, approvals, restrictions, vendor expectations, and escalation rules before the next AI tool becomes business critical.
Important limitation
This resource is for informational and educational purposes only. It does not constitute legal, regulatory, audit, supervisory, model validation, privacy, security, or compliance advice. Institutions should consult qualified counsel and risk, compliance, audit, privacy, security, and model risk professionals regarding their specific obligations.
- Name one executive owner for the policy.
- Require intake for new AI use cases.
- Restrict public AI tools for sensitive data.
- Review and update the policy at least annually.