AI governance is the organizational system of rules, processes, and technical controls that ensure models operate safely, transparently, and predictably. It intersects policy, engineering, ethics, and risk management. A mature governance framework touches every layer of the model lifecycle: data sourcing, training, evaluation, deployment, access control, drift monitoring, documentation, and auditability.

Ethics frameworks extend this foundation by defining the values and standards your models must uphold: fairness, accountability, explainability, and respect for user autonomy. These systems do not restrict innovation. Instead, they create a controlled environment where models can evolve responsibly, where decisions can be understood, and where outcomes can be defended to regulators, customers, and internal auditors alike.

Governance is how enterprises transform trust into infrastructure.