AI Governance in Regulated Finance: A Framework

Financial services firms are moving from asking whether AI can help to asking how AI can be governed. In regulated environments, that shifts the focus away from model novelty and toward oversight, explainability, delegation limits, and evidence capture.

A credible governance framework has to answer practical questions. Who initiated the model-assisted action? What policy envelope constrained it? What evidence exists to explain the recommendation, approval, and final execution path? Those are operating questions, not presentation questions.

Turing Dynamics views AI governance as part of the same architectural discipline as execution governance. The underlying requirement is identical: if a system materially affects regulated outcomes, its controls and evidence need to be explicit and queryable.