Generative AI in compliance: oversight, governance and risk

January 12, 2026

Generative AI is no longer an emerging concept for compliance teams. Over the past year, it has become a practical tool embedded into everyday workflows, driven by rising regulatory complexity, pressure on resources, and the need for greater consistency across jurisdictions.

What began as cautious experimentation has now evolved into real-world deployment across a growing number of firms.

Yet as adoption accelerates, a critical question has emerged. How can compliance teams benefit from AI-driven efficiency without surrendering responsibility?

Zeidler Group recently delved into AI in compliance and how to bring efficiency without abdication.

One effective way firms are approaching this challenge is by treating AI output in the same way they would work produced by a third-party provider, it said. Compliance functions already assess external inputs based on risk and materiality, applying lighter scrutiny to low-risk activities and much tighter oversight where decisions carry regulatory or legal consequences. Applying this same risk-based logic to AI allows firms to gain efficiency without compromising control.

As a result, human-in-the-loop models have become standard practice, Zeidler explained. Concerns around hallucinations, bias, and over-automation have reinforced the need for people to remain firmly in charge of high-stakes decisions. The most effective implementations use AI as an assistant, supporting compliance professionals rather than replacing their judgement. Accountability, ultimately, still sits with the firm.

Regulators have also shown a more pragmatic stance over the past year. Rather than rejecting AI-assisted compliance, there is increasing openness to tools that improve consistency and operational efficiency. At the same time, expectations are rising. If compliance processes become easier, regulators will expect better documentation, stronger audit trails, and clearer explanations, it said.

Governance and bias remain ongoing challenges, particularly when firms rely on third-party models. Generic, firm-wide AI policies have often fallen short. More effective governance is task-specific, supported by subject matter experts, structured reviews, and continuous monitoring of outputs to identify potential issues early.

For more insights, read the full story here.