In this episode of Corruption, Crime and Compliance, Michael Volkov traces the decades-long relationship between compliance and data, from the profession’s earliest, checkbox-style attempts to measure program effectiveness through crude proxies like hotline volume and training completion rates, through the rise of continuous monitoring systems, integrated dashboards, and key risk indicators that enabled expedited auditing and near-real-time visibility, and finally to the current AI-driven era, where machine learning and natural language processing can surface subtle risk patterns no human-authored rule would catch and compress the gap between detection and action to hours rather than months. He argues that each phase of this evolution was driven not by strategic foresight but by escalating business, regulatory, and litigation risk, first around proving program effectiveness itself, then data privacy and cybersecurity, and now AI governance, and closes by cautioning that AI monitoring tools will only perform as well as the underlying data infrastructure and discipline a compliance function has already built.