Algorithmic governance is the set of rules, controls, and oversight processes used to manage systems that make or support decisions with algorithms. It covers who can deploy a model, what data it may use, what outcomes are allowed, and how decisions are reviewed, logged, and explained.
In cybersecurity, algorithmic governance matters because automated systems can scale mistakes as fast as they scale efficiency. Poorly governed models may expose sensitive data, create biased access decisions, or be manipulated through poisoned training data, adversarial inputs, or unauthorized model reuse. Defenders use governance to enforce approval workflows, data minimization, access segregation, audit trails, human review, and rollback procedures. In regulated environments such as healthcare, it also helps ensure that AI-based decision support stays within its intended purpose and that every automated action remains accountable and traceable.