• U.S. banking regulators are increasing scrutiny of AI deployment across the sector
• Supervisors are examining governance, data access, cybersecurity, and vendor management controls
• AI discussions are now reportedly part of routine bank examinations
• Regulators are focusing on high-risk activities including lending, KYC, and sanctions screening
• Human oversight, kill switches, and model guardrails are receiving particular attention
• Vendor and subcontractor risks are emerging as major supervisory concerns
• Regulators currently favor principles-based oversight rather than AI-specific rules
• Authorities are assessing whether existing supervisory frameworks remain fit for purpose

As artificial intelligence becomes increasingly embedded in banking operations, U.S. regulators are stepping up oversight of how lenders deploy the technology, raising questions about governance, cybersecurity, data privacy, and third-party risk management.

The heightened scrutiny comes as banks accelerate the use of AI across a growing range of activities, from customer-facing virtual assistants to more sophisticated applications such as credit underwriting, sanctions screening, anti-money laundering monitoring, and regulatory compliance.

The rapid expansion of these capabilities has prompted supervisors to take a closer look at how financial institutions are managing the risks that accompany them.

According to people familiar with ongoing supervisory discussions, the Office of the Comptroller of the Currency and the Federal Reserve have begun incorporating detailed questions about AI into routine bank examinations.

Institutions are being asked to explain how artificial intelligence is used in higher-risk activities, including lending decisions, know-your-customer processes, and sanctions compliance programs.

The inquiries extend well beyond technical deployment. Regulators are reportedly seeking detailed information about governance structures, human oversight mechanisms, data protection controls, and contingency arrangements should AI systems malfunction.

Supervisors are also examining whether banks have implemented safeguards such as "kill switches" that would allow potentially problematic systems to be shut down quickly.

One source familiar with the discussions said that conversations about artificial intelligence now form part of virtually every bank examination. However, regulators are not currently pursuing a prescriptive approach.

Instead, they are focused on understanding how firms are using the technology and identifying emerging risks before determining whether additional guidance or regulatory intervention may be required.

The growing regulatory attention reflects broader concerns about the risks associated with AI adoption throughout the financial sector.

Cybersecurity experts have warned that increasingly powerful AI models could be exploited to identify software vulnerabilities, automate cyberattacks, or facilitate sophisticated fraud schemes.

As a result, regulators and policymakers are examining whether financial institutions are adequately prepared to manage these evolving threats.

The issue gained additional prominence following public statements from federal agencies earlier this year.

In April, the OCC announced plans alongside the Federal Reserve and the Federal Deposit Insurance Corporation to seek industry input on the use of artificial intelligence, including both generative AI and emerging agentic systems.

While such requests do not create new regulatory requirements, they often serve as a precursor to future policy development.

For now, supervisors appear determined to rely on existing regulatory frameworks rather than create AI-specific rules.

Model risk management standards, third-party risk management requirements, consumer protection obligations, and operational resilience expectations are being used as the primary tools for evaluating banks' AI programs.

A central concern is ensuring that AI systems remain within clearly defined operational boundaries.

Supervisors are examining whether models can access or infer information beyond authorized limits, creating potential privacy, confidentiality, or compliance risks.

Banks are being asked to demonstrate that they have implemented guardrails governing both data access and model behavior.

Human oversight remains another major focus. Regulators want clear evidence that employees maintain ultimate authority over important decisions and that institutions can intervene when automated systems produce unexpected outcomes.

Questions are also being raised about who holds responsibility for monitoring and escalating potential issues.

Vendor risk has emerged as a particularly important area of scrutiny. As banks increasingly depend on external providers for AI capabilities, regulators are examining how firms assess the governance, security, and resilience of those vendors.

Institutions are additionally being asked whether they have viable exit strategies should a vendor experience operational problems, security breaches, or other failures.

Regulators face challenges of their own. The pace of AI development is significantly faster than traditional regulatory and rulemaking cycles, creating concerns that formal guidance could quickly become outdated.

As a result, authorities are expected to continue favoring broad principles-based supervision rather than detailed rulebooks in the near term.

That approach was echoed recently by Federal Reserve Vice Chair for Supervision Michelle Bowman, who said banks are currently relying on existing risk management frameworks to guide their use of artificial intelligence.

However, she also acknowledged the need to assess whether current supervisory tools remain suitable for a rapidly evolving technological landscape.