Traditional software governance often uses static compliance checklists, quarterly audits and after-the-fact reviews. But this method can’t keep up with AI systems that change in real time. A machine learning (ML) model might retrain or drift between quarterly operational syncs. This means that, by the time an issue is discovered, hundreds of bad decisions could already have been made. This can be almost impossible to untangle.  In the fast-paced world of AI, governance must be inline, not an after-the-fact compliance review. In other words, organizations must adopt what I call an “audit loop”: A continuous, integrated compliance process that operates in real-time alongside AI development and deployment, without halting innovation. This article explains how to implement such continuous AI compliance through shadow mode rollouts, drift and misuse monitoring and audit logs engineered for direct legal defensibility. From reactive checks to an inline “audit loop” When systems moved at the speed of people, it made sense to do compliance checks every so often. But AI doesn’t wait for the next review meeting. The change to an …