Algorithm governance is the set of oversight, policies, and review controls that keep automated decisions accountable. It covers who can deploy a model, what data it may use, how often it is tested, and how people can review or override its output. In practice, it turns a scoring system from a black box into a controlled business process.
In cyber security, algorithm governance matters because automated systems can be manipulated, misconfigured, or fed poor data. Weak governance can let bad inputs, unauthorized model changes, or hidden logic affect access, pricing, fraud checks, or eligibility decisions. Defenders use approval workflows, version control, audit logs, access restrictions, validation tests, and human challenge paths to reduce that risk. Good governance also helps prove that a decision was explainable, traceable, and defensible under regulatory review.