Responsible GenAI Use Policy for Financial Services: What to Include (with a Practical Template)

A chief compliance officer at a regional bank put it plainly in a recent working session: “Our staff are already pasting things into ChatGPT and Copilot. I do not need a philosophy paper on AI. I need a policy I can publish next week.” That is the right urgency and the right artifact. A responsible generative AI use policy for a financial institution should name the approved tools, forbid the dangerous uses, define exactly what data may never enter a prompt (including nonpublic personal information under GLBA), require human review for any customer-facing or decisioning use, and assign clear ownership, all mapped to the NIST AI RMF GOVERN function and to SR 11-7 model-risk governance.

This guide gives you that policy structure section by section, explains why a generic corporate AI policy will not survive an exam-room conversation in a regulated bank, and ends with a practical template outline you can lift into a draft. None of this is legal advice, and any policy you adopt must be reviewed by qualified counsel before it goes live.

Why a generic corporate AI policy is not enough for a bank

Most off-the-shelf AI acceptable-use policies were written for software companies and marketing teams. They tell employees to “be thoughtful,” “protect confidential data,” and “check outputs.” That is fine for a SaaS startup. It is dangerously thin for a bank or fintech that is supervised on fair lending, that holds nonpublic personal information under the Gramm-Leach-Bliley Act, and whose customer-impacting models fall under SR 11-7.

A regulated institution has obligations a generic policy never contemplates. It must show that customer-affecting decisions are explainable and non-discriminatory under ECOA. It must protect NPI under the GLBA Safeguards Rule. It must avoid unfair or deceptive acts or practices (UDAP) in anything customer-facing. A policy that does not name those regimes, and does not translate them into concrete prompt-level rules, leaves the institution exposed precisely where supervision is most intense.

The gap is not philosophical, it is operational. A bank policy has to answer questions a generic one ignores: Can a loan officer use Copilot to draft an adverse-action notice? May a fraud analyst paste a customer’s transaction history into a public chatbot? Who approved the model behind the chat assistant, and where is that recorded? Those are the questions an examiner or your own second line will ask, and a “be thoughtful” policy has no answer.

Where this policy sits in your governance stack

Treat the GenAI use policy as one document inside a larger structure, not as your entire AI governance program. It lives within the NIST AI RMF GOVERN function, which is where an organization establishes the policies, accountability, and culture that make the MAP, MEASURE, and MANAGE functions possible. The use policy is a GOVERN artifact: it sets the rules of the road for how people and systems may use generative AI.

It also has to mesh with SR 11-7, the Federal Reserve and OCC supervisory guidance on model risk management. Where a generative AI system informs a customer decision (credit, pricing, collections, fraud dispositions), that system is a model in the SR 11-7 sense and carries development, validation, and governance obligations. The use policy does not replace model validation. It draws the line between the everyday productivity uses that need lightweight controls and the decisioning uses that must be escalated into your model-risk framework.

For the deeper treatment of how that line works, see our piece on how SR 11-7 applies to machine learning and generative AI, and for the broader distinction between writing policy and proving adherence, our explainer on AI governance versus AI compliance.

The sections a finserv GenAI use policy must contain

Below is the substantive content. Each section names what to include and why it matters for a regulated institution. Read it as the specification, and the template outline at the end as the skeleton.

1. Scope and approved tools

State who the policy binds (all employees, contractors, and third parties acting on the institution’s behalf) and which generative AI tools are approved. Name them explicitly. In most banks that means enterprise-tenant tools with contractual data protections, such as Microsoft Copilot and Azure OpenAI running in the institution’s own tenant, rather than consumer endpoints. Distinguish clearly between an approved enterprise tool and a consumer ChatGPT account, because the data-handling terms are not the same.

2. Prohibited uses

Spell out what is never permitted. Typical prohibitions include entering nonpublic personal information into unapproved or consumer tools, using GenAI to make a final credit, pricing, or adverse-action decision without human review, generating customer communications that go out unreviewed, and using AI output as authoritative legal, tax, or compliance advice. A prohibited-uses list is the most read section of any acceptable-use policy, so make it concrete and example-driven.

3. Data classification and what must never enter a prompt

This is the heart of a finserv policy. Tie it to your existing data classification scheme and state, in plain language, that nonpublic personal information under GLBA, authentication credentials, material nonpublic information, and confidential customer or counterparty data must never be entered into a prompt on a tool not contractually approved for that data class. Where an enterprise tool is approved for restricted data, say so and name the conditions. Where it is not, the rule is simple: that data does not go in.

4. Human-in-the-loop and review requirements

Define where a human must review and approve before output is used, and make the requirement scale with risk. Customer-facing content and any decisioning use require a qualified human reviewer who is accountable for the output. Internal drafting and summarization can carry lighter review. The principle to encode is that the AI assists, a named person decides, and that person owns the result.

5. Model and vendor approval and inventory

No generative AI tool or model enters production use without going through an approval path and landing in an AI inventory. The policy should require that every approved tool, model, and material use case is recorded, with an owner, a data classification, and a risk tier. You cannot govern what you have not inventoried, and shadow AI is the first thing a serious review surfaces. Our note on building a real AI inventory covers how to find what is already in use.

6. Fair-lending and UDAP guardrails for customer-impacting use

For any use that touches a customer decision or communication, the policy must require fair-lending and UDAP review. Generative AI used in or around credit decisions implicates ECOA, so the policy should bar uses that could introduce or amplify disparate treatment or disparate impact, and require that customer-affecting uses route through fair-lending review. Customer-facing content must be screened so it is not unfair, deceptive, or abusive. This is the section that most distinguishes a bank policy from a generic one.

7. Logging, monitoring, and incident handling

Require that use of approved enterprise GenAI tools is logged and monitored consistent with the institution’s security and privacy controls, and define what counts as an AI incident: a data-leakage event, a harmful or discriminatory output, a hallucinated fact that reached a customer. State how incidents are reported and who handles them, and connect this to the institution’s existing incident-response process rather than inventing a parallel one.

8. Training and attestation

State that employees must complete GenAI use training before access and attest to the policy, with periodic re-attestation. Training and attestation are what turn a published document into a controlled, demonstrable practice, which is exactly what your second line and any examiner will ask you to evidence.

9. Roles and governance ownership

Name the roles. A growing number of institutions designate a Chief AI Officer or an AI governance committee that owns the policy, approves tools and use cases, and reports to the board or a board committee. The policy should name the accountable owner, the approval body, and the escalation path for exceptions, so that ownership is unambiguous rather than diffuse.

A practical GenAI use policy template outline

Use this as the skeleton for your draft. It is a structure, not legal language, and it must be tailored to your institution and reviewed by counsel before adoption.

1. Purpose and scope - 1.1 Purpose and relationship to the institution’s AI governance program and NIST AI RMF GOVERN posture - 1.2 Who it binds (employees, contractors, third parties) - 1.3 Systems and tools in scope

2. Approved tools and access - 2.1 List of approved enterprise tools (for example Microsoft Copilot, Azure OpenAI in the institution’s tenant) - 2.2 Prohibited and unapproved tools (for example consumer chatbot accounts) - 2.3 How to request a new tool or use case

3. Prohibited uses - 3.1 Data prohibitions (no NPI or credentials into unapproved tools) - 3.2 Decisioning prohibitions (no unreviewed customer decisions) - 3.3 Communication prohibitions (no unreviewed customer-facing output)

4. Data classification and prompt rules - 4.1 Mapping to the institution’s data classification scheme - 4.2 What must never enter a prompt (NPI under GLBA, credentials, MNPI, confidential data) - 4.3 Conditions under which restricted data may be used in an approved tool

5. Human-in-the-loop and review - 5.1 Risk tiers (internal-assist vs customer-facing vs decisioning) - 5.2 Required reviewer and accountability per tier - 5.3 Documentation of review for customer-impacting uses

6. Model and vendor approval and inventory - 6.1 Approval path for new tools, models, and use cases - 6.2 AI inventory fields (owner, data class, risk tier, use case) - 6.3 Link to SR 11-7 model-risk escalation for decisioning models

7. Fair-lending, UDAP, and consumer-protection guardrails - 7.1 ECOA and fair-lending review for credit-adjacent uses - 7.2 UDAP screening for customer-facing content - 7.3 Explainability and adverse-action handling expectations

8. Logging, monitoring, and incident response - 8.1 Logging and monitoring of approved-tool usage - 8.2 Definition of an AI incident - 8.3 Reporting and response, tied to the existing incident process

9. Training and attestation - 9.1 Pre-access training requirement - 9.2 Policy attestation and periodic re-attestation - 9.3 Recordkeeping of completion

10. Roles and governance - 10.1 Policy owner (for example Chief AI Officer or AI governance committee) - 10.2 Approval body and board reporting line - 10.3 Exception and escalation process

11. Enforcement and review - 11.1 Consequences for violations - 11.2 Policy review cadence and version control - 11.3 Counsel and compliance sign-off record

What this template is / What it is not

What it is: A practitioner skeleton for a generative AI acceptable-use policy at a bank or fintech, organized to align with the NIST AI RMF GOVERN function and to hand off decisioning uses into SR 11-7 model-risk governance.

What it is not: It is not legal advice, a finished policy, or a guarantee of any outcome. Any policy built from this outline must be tailored to your institution and reviewed by qualified counsel before adoption. DSE prepares organizations for audit and assessment. We do not certify, and we do not guarantee any regulatory or exam outcome.

FAQ

Do we need a separate GenAI policy from our existing AI policy? Usually yes, or at least a dedicated section. Generative AI introduces risks a general AI policy rarely addresses: employees pasting nonpublic personal information into prompts, hallucinated outputs reaching customers, and consumer chatbot accounts outside your data agreements. A finserv GenAI acceptable-use policy translates GLBA, ECOA, and UDAP obligations into concrete prompt-level rules and approved-tool lists, which a generic AI policy does not. You can implement it as a standalone policy or a clearly scoped module of your broader AI governance policy, but the GenAI-specific rules must be explicit.

Can employees use ChatGPT or Copilot with customer data? It depends entirely on the tool and the data class. A consumer ChatGPT account is not appropriate for nonpublic personal information under GLBA. An enterprise tool running in your own tenant with contractual data protections, such as Microsoft Copilot or Azure OpenAI configured for your institution, may be approved for certain data classes if your policy and security controls permit it. The policy must name which tools are approved for which data classifications, and state clearly that NPI and credentials never go into unapproved or consumer tools.

Who owns the GenAI policy in a bank? A growing number of institutions assign ownership to a Chief AI Officer or an AI governance committee that approves tools and use cases and reports to the board or a board committee. In practice the policy is co-owned across compliance, risk, security, and the data or AI function, with one named accountable owner. The key is that the policy names the owner, the approval body, and the escalation path, so ownership is unambiguous rather than diffuse across three teams.

How does a GenAI use policy map to SR 11-7? The use policy sits inside the NIST AI RMF GOVERN function and draws the line between everyday productivity uses, which need lightweight controls, and decisioning uses, which are models in the SR 11-7 sense. Where a generative AI system informs a credit, pricing, collections, or fraud-disposition decision, it carries SR 11-7 development, validation, and governance obligations. The policy does not replace model validation; it requires that decisioning uses are escalated into your model-risk framework.

Is a template enough, or do we need counsel review? A template is a starting skeleton, not a finished policy, and it is not legal advice. Any GenAI use policy at a regulated institution must be tailored to your tools, data classes, and risk appetite, and reviewed by qualified counsel and compliance before adoption. The template accelerates drafting and ensures you cover scope, prohibited uses, data rules, human review, fair-lending guardrails, and ownership, but counsel review is non-negotiable before the policy goes live.

The Bottom Line

A responsible generative AI use policy for a financial institution is not a values statement, it is an operating control. It names the approved tools, forbids the dangerous uses, defines what must never enter a prompt (NPI under GLBA above all), requires a human to own any customer-facing or decisioning output, puts fair-lending and UDAP guardrails around customer-impacting use, and assigns clear ownership to a Chief AI Officer or AI governance committee. A generic corporate AI policy does none of that with the specificity a supervised institution needs.

Position the policy where it belongs: inside the NIST AI RMF GOVERN function, with a clean hand-off into SR 11-7 model-risk governance for any use that informs a customer decision. Use the template outline above as your skeleton, fill it with your real tools and data classes, and then route it through compliance and qualified counsel before you publish. The point is not a perfect document. The point is a controlled, attestable practice that holds up when someone asks how your people actually use AI.

If you want a structured starting point for the policy, the inventory, and the readiness evidence behind it, download the AI Governance Checklist for the scope, tiering, and documentation a finserv program needs. When you are ready to pressure-test your posture, start with the Governance Readiness Snapshot, and for the senior-team engagement that maps your GenAI use to your model-risk and fair-lending obligations, see our banking AI governance work.