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. This talk explores building production-grade anomaly detection platforms serving Finance, Audit, and 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.