Artificial Intelligence is no longer just a future consideration for financial institutions but something that is being used every day, oftentimes without the guardrails in place to safely guide employees.
When the realization sets in that shadow AI is already happening within the organization, many institutions immediately jump to performing a risk assessment and drafting a simple acceptable use statement. While both are important, AI is fundamentally a governance issue. The solution is not simply another risk assessment, but instead it is a governance framework that enables responsible innovation while protecting customer information, controlling risk, and maintaining regulatory compliance. Which often leads to the question “What should an AI Policy include?”
In our experience, an effective AI policy for banks should address eight key components: Roles and Responsibilities, the AI Governance Framework, AI Risk Response, AI Use Case Intake and Approval, Human Oversight, Data Protection, Vendor AI Due Diligence, and Monitoring, Logging, and Key Risk Indicators.
Every successful AI governance program starts with a simple principle: somebody must own it.
One of the most common mistakes organizations make is assuming AI governance belongs solely to Information Technology or Information Security. In reality, AI affects nearly every area of the institution, including operations, compliance, lending, fraud management, vendor management, human resources, and executive leadership. A strong AI policy should clearly define who is responsible for evaluating, approving, monitoring, and overseeing AI use throughout the organization.
At a minimum, responsibilities should be assigned to:
The Board and Executive Management establish oversight and risk appetite, while business owners remain accountable for how AI is used within their departments. Information Security, Compliance, and Risk Management provide expertise and challenge assumptions, while committees such as the ITSC help ensure AI decisions are reviewed consistently across the institution.
The goal is simple: every AI use case should have a clearly identified owner and governance process.
An effective AI policy should establish a governance framework that provides structure, consistency, and accountability for AI-related decisions across the organization. The objective is not to create barriers to innovation but to ensure AI adoption occurs intentionally, with appropriate oversight and risk management.
Just as financial institutions maintain governance structures for cybersecurity, vendor management, business continuity, and information security, AI requires a similar level of oversight. Without a framework, decisions are often made inconsistently across departments, leading to increased risk, duplicated effort, and limited visibility into how AI is actually being used throughout the institution.
While there is no single framework that every bank must adopt, the policy should clearly describe the institution's approach to governing AI throughout its lifecycle, from initial evaluation and approval through ongoing monitoring, oversight, and periodic reassessment. Done effectively, the framework becomes the foundation upon which every other component of the AI policy is built.
Not every AI tool carries the same level of risk.
Using Microsoft Copilot to draft meeting notes presents a vastly different level of risk than using AI to influence lending, fraud, BSA/AML, or customer-impacting decisions. Because of this, every AI policy should include a formal methodology for evaluating and responding to AI-related risk.
A practical risk framework often includes:
Many institutions benefit from grouping AI use cases into broad risk categories that help guide governance, approval requirements, and monitoring expectations.
Factors that may influence risk tiering include:
The objective is not necessarily to assign a precise score to every use case. Rather, it is to establish a repeatable process that distinguishes routine productivity use from more consequential applications that may warrant additional oversight.
A policy should also define the institution's overall appetite for AI adoption.
Every organization approaches technology differently. Some may pursue AI aggressively to gain efficiencies and competitive advantages, while others may choose a more measured approach focused on risk reduction and regulatory certainty. Establishing these expectations early helps create consistency in decision-making and provides clear direction as new opportunities emerge.
Risk Responses
Once an AI use case has been evaluated, management should determine the most appropriate response based on the level of risk, business value, available controls, and alignment with organizational objectives.
The goal of risk response is not to eliminate all risk, an impossible task with any technology. Instead, it is to ensure risks are understood, decisions are intentional, and AI adoption occurs within a framework that balances innovation, security, compliance, and customer trust.
One of the easiest ways to identify shadow AI is to establish a formal intake process.
Many organizations discover employees have already adopted AI solutions before management is even aware they exist. Sometimes those tools are free public services. Other times they are AI features embedded inside existing software products.
An AI intake process helps ensure management understands:
The objective is visibility, not paperwork.
Perhaps the most important principle in any AI policy is that accountability remains with people.
AI tools can produce impressive results, but they can also produce inaccurate information, incomplete conclusions, outdated content, biased outcomes, or entirely fabricated responses. Technology may support decision-making, but responsibility for decisions should remain with employees and management.
If there is one area regulators and examiners are likely to focus on first, it is data protection.
Many AI solutions rely upon data inputs provided by users. That means an employee can unintentionally expose customer information simply by submitting prompts, uploading files, or connecting AI tools to institutional systems.
A strong AI policy should address data classification, privacy protection, and access controls. By addressing these three data protection components, an institution should be able to define which information may and may not be used, understand where data is stored and how it is utilized, and who has access to the data.
Many banks will encounter AI through vendors long before they purchase a dedicated AI platform.
Core processors, fraud monitoring solutions, customer service tools, marketing platforms, and other technologies increasingly include AI functionality by default. In many cases, those features can be enabled with a simple configuration change.
Because of this, AI governance must extend beyond employee usage and include vendor-provided capabilities. Vendor-embedded AI should be reviewed based on the data it accesses and the decisions it influences.
One common misconception surrounding AI governance is that once a tool has been reviewed and approved, the work is complete. In reality, AI technologies, vendor capabilities, regulatory expectations, and employee usage patterns can all change rapidly over time. An effective AI policy should therefore include mechanisms for monitoring, oversight, and periodic reassessment to ensure AI use remains aligned with the institution's objectives, risk appetite, and control environment.
In addition to monitoring individual use cases, institutions should consider developing Key Risk Indicators (KRIs) that help management evaluate the overall health and maturity of the AI governance program. These metrics should be meaningful to the institution's size, complexity, and level of AI adoption and should provide management with sufficient information to identify trends, emerging concerns, and opportunities for improvement.
For banks and credit unions, the question is no longer whether Artificial Intelligence will be used within the organization. The real question is whether its use will be governed appropriately.
The institutions that succeed with AI will not be those that rush to adopt every new tool. They will be the organizations that establish strong governance first and then adopt AI in a controlled, transparent, and risk-based manner.
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