Generative AI is becoming part of everyday banking operations. Lending teams are exploring faster document reviews. Risk and compliance teams are evaluating AI for research and reporting. Employees are using AI-powered tools to simplify administrative work.
According to the 2026 Conference of State Bank Supervisors survey, approximately 53% of responding community bankers reported piloting AI or using it in limited business functions. Cybersecurity remained their top internal risk for the ninth consecutive year.
For community and regional banks, these developments create two security challenges: protecting sensitive information as AI becomes part of operations and defending against increasingly sophisticated AI-enabled fraud.
Both require greater visibility into how technology, employees, data and third-party providers interact.
Where Generative AI Creates Security Risks for Banks
1. Sensitive Customer Data Can Become Overexposed
Banks manage substantial amounts of confidential information, including account records, loan applications, financial statements and personally identifiable information.
When employees use unauthorized AI tools, sensitive information may be entered into services that haven’t been evaluated against the bank’s security and privacy requirements.
Approved tools introduce another consideration: existing access permissions.
For example, Microsoft 365 Copilot operates within a user’s existing permissions. If SharePoint or OneDrive files are shared too broadly, Copilot may surface information that an employee can technically access but shouldn’t have access to for their role.
Microsoft’s 2026 security guidance emphasizes correcting excessive permissions, protecting sensitive content and monitoring AI-related activity.
For banks, that makes data governance and identity management essential parts of AI security.
2. AI-Enabled Fraud Is Becoming Harder to Recognize
Cybercriminals are using generative AI to create more convincing phishing messages, impersonate trusted individuals and produce fraudulent identification documents.
A familiar voice or seemingly legitimate request can no longer serve as sufficient proof of identity.
The Financial Crimes Enforcement Network (FinCEN) has historically warned financial institutions about deepfake-related fraud, including attempts to bypass identity verification through AI-generated documentation.
For banks, the exposure extends across several operations:
- Wire transfers and payment authorization
- Customer identity verification and account opening
- Executive and employee impersonation
- Commercial banking and treasury management
- Customer service and account recovery
Strong authentication, independent verification procedures and employee awareness are increasingly important to preventing these schemes.
3. Third-Party Technology Can Introduce AI Without a Formal Rollout
AI capabilities are appearing inside software banks already use, from productivity platforms to customer service and financial technology applications.
An existing vendor relationship doesn’t automatically answer how new AI functionality processes, retains or shares customer data.
Before enabling these capabilities, banks should understand what information the technology accesses, which subcontractors are involved, how activity is monitored and what controls exist over automated actions.
The same oversight matters when a third-party provider manages AI services on the bank’s behalf.
4. AI-Generated Decisions Require Oversight
Generative AI can produce convincing answers that are incomplete, inaccurate or unsupported.
That creates operational risk when outputs influence lending, compliance interpretations, customer communications or other consequential decisions.
AI agents introduce additional considerations because they can interact with connected systems and perform actions rather than simply generating information.
Banks need clearly defined approval authority, appropriate testing, human review and the ability to restrict or stop automated activities.
What Banking Regulators Expect in 2026
Banking regulators continue to evaluate emerging technology through established expectations for cybersecurity, information security, operational risk, vendor management and governance.
In September 2026, the Office of the Comptroller of the Currency updated its Cybersecurity Supervision Work Program, aligning its structure and references with existing supervisory guidance and the NIST Cybersecurity Framework. The update did not establish new examination procedures.
For community and regional banks, the practical priority is understanding how AI affects existing responsibilities for protecting customer information, overseeing third parties and managing technology risk.
That means maintaining documented policies, appropriate controls, clear ownership and evidence that safeguards operate as intended.
Five Steps Banks Can Take to Strengthen AI Security
Effective AI security begins with understanding how the technology is already being used across the institution.
- Identify existing AI usage. Review approved applications, embedded AI features and employee use of unapproved tools. Document which systems access sensitive information and where data may travel.
- Strengthen access and data protection. Review Microsoft 365 permissions, identity controls, data classification and data loss prevention policies. Restrict unnecessary access to sensitive banking information.
- Update fraud prevention procedures. Reevaluate identity verification, payment approvals and procedures for responding to unusual requests. Train employees to recognize AI-generated impersonation and verify sensitive instructions through independent channels.
- Establish AI governance. Define approved use cases, prohibited data sharing, vendor review requirements, human oversight and ownership across IT, security, compliance and business leadership.
- Monitor, test and improve. Incorporate AI-related exposure into risk assessments, security monitoring, incident response exercises and periodic management reporting.
These measures help banks support legitimate AI use while managing the risks associated with customer information and critical operations.
Build AI Security Around the Bank You Already Operate
Community and regional banks have already invested in core banking systems, cybersecurity controls, internal expertise and trusted technology partners.
Those investments provide a starting point for stronger AI security.
Secur-Serv works alongside banking IT teams and existing providers to identify AI-related risks, strengthen technical safeguards and support secure adoption.
Whether your institution is exploring its first AI applications or managing tools already in use, the objective remains consistent: protect customer information, maintain operational control and make informed technology decisions.
Know Where AI Creates Risk in Your Bank
AI may already be influencing how information moves, how employees work and how fraud attempts reach your institution.
Understanding that exposure is the starting point for deciding what needs attention.
See how Secur-Serv can help your bank identify AI security gaps and strengthen existing controls.
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