AI Controls

AI controls are the specific safeguards an organization puts in place to limit how AI systems are built, deployed, and used, such as access restrictions, human review steps, testing requirements, logging, and monitoring. For enterprise compliance teams, they are the concrete measures that turn high-level AI policies into something auditors and regulators can check. Regulators and standards bodies increasingly expect documented controls, so knowing which ones apply to a given AI use case is central to demonstrating due diligence.

1 item

ResearchUS2026-10-08

Banks Set Five-Layer AI Control Benchmark as Supervisory Pressure Grows

A Banking Dive report published October 6, 2026 documents how U.S. banking organizations are building layered AI controls. These cover data inputs, model outputs, permitted tools, post-deployment monitoring, and escalation for customer, regulatory, financial, and reputational harm. The report reflects accelerating AI adoption across the sector alongside rising supervisory expectations. Compliance teams at banks and other regulated institutions can use the control layers described as a peer benchmark.

banking model risk management AI controls financial services monitoring SR 26-2 post-deployment oversight