This session explores the design and deployment of enterprise-scale anomaly detection platforms serving Finance, Audit, and Compliance functions. Enterprise governance demands AI systems that balance risk detection, operational efficiency, and regulatory compliance. Beginning with the evolution of governance AI adoption, Vineeth will discuss Schneider Electric’s Anomaly Detection Framework encompassing metric development (KPIs), feedback-as-a-service architecture, continuous model monitoring and updates, fallback model strategies for system resilience, and policy adherence mechanisms. We’ll examine MLOps infrastructure(AWS) for cross-functional deployment, interpretability requirements in high-stakes decisions, and balancing detection sensitivity with false positive rates. Engineering details highlight AWS-based  model inference, retraining, model versioning strategies, and patterns for scaling anomaly detection across organizational silos while maintaining regulatory compliance.