The New Standard for Defensible AI Enabled Controls in Financial Services

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Caitlin Like argues that most firms are still operating with siloed, inconsistent controls that make regulatory examinations painfully slow and operationally risky. Policies live in one system, surveillance in another, case management somewhere else, leaving teams dependent on manual “treasure hunts for data” and unable to reconstruct a full evidence chain. The result is predictable: inconsistent outcomes, weak audit trails, and remediation cycles that lag behind the pace of modern risk. And with AI policies becoming obsolete in under a year, firms relying on manual updates and disconnected tooling are already behind.

In her session, she makes the case for a defensibility‑grade integrated architecture where obligations map directly to controls, controls map to monitoring, and monitoring feeds seamlessly into investigation, remediation, and continuous improvement. Purpose‑built, explainable AI dramatically improves detection accuracy and reduces residual risk, unlike random sampling or generic LLMs, which fail SR 11‑7 standards and leave blind spots. But technology alone isn’t the solution. Defensibility is a mindset: traceability, reproducibility, disciplined change control, and consistent coverage across every region and business line. In an era of accelerating regulatory expectations, integrated AI‑enabled controls aren’t a competitive advantage, they’re the minimum standard for credible risk management.