GenAI is no longer a novelty in financial services, but on the compliance desk, trust is still hard-won. While generative models promise faster analysis of regulations, risks, and controls, concerns around explainability, accountability, and regulatory scrutiny continue to slow adoption. As pressure mounts to do more with less, compliance teams are being forced to decide whether GenAI can be relied on for defensible decision-making — or whether it remains a powerful assistant that still needs close human supervision.
In the first part of a two-part series, RegTech Analyst spoke to key industry leaders to get their take on whether GenAI is able to secure the trust of compliance leaders.
For many compliance leaders, the trust question hinges on whether GenAI can be treated like any other regulated system. Paul Burleton, CPO at Corlytics, argues that firms must apply the same discipline they would to traditional compliance models. Outputs need to be version-controlled, traceable, and defensible, supported by a clear audit trail showing inputs, model changes, and resulting decisions. While the growing complexity of large language models increases opacity, Burleton believes combining explainability techniques with rigorous testing by subject-matter experts can meet regulatory expectations — provided these controls are designed in from the outset rather than retrofitted later.
Guardrails are emerging as a critical layer in that design. Rather than limiting GenAI’s usefulness, well-constructed workflows can reinforce governance while delivering real operational gains. These include confidence thresholds, policy validation, and tracking human overrides so that models improve over time. When implemented properly, Burleton notes, such frameworks do more than manage risk — they also streamline day-to-day compliance operations and regulatory change management.
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