Models earn trust in the ordinary days after launch. This chapter consolidates operational assurance practices that sustain e-commerce risk systems over time. It describes feedback loops that convert review outcomes, support signals, disputes, merchant appeals, and incident findings into structured data and policy refinement, rather than isolated operational work. Retraining cadence is framed as an engineered process triggered by drift, seasonality, and product change, supported by reproducible pipelines and clear promotion criteria. The chapter also covers incident and security response runbooks, emphasizing ownership, containment, rollback, and post-incident learning when fraud spikes or pipelines fail. Finally, it addresses cost, latency, and capacity engineering, recognizing that model choices are constrained by throughput and reliability requirements. Operational assurance is presented as continuous: Metrics track governance effectiveness, monitoring quality, and business impact so that systems do not decay into brittle, opaque stacks. We close by emphasizing governance-effectiveness metrics and operational SLAs that keep teams aligned, ensuring the risk stack remains maintainable, explainable, and resilient over multiyear horizons. The objective is sustained production trust: rapid response to incidents, consistent iteration, and clear accountability from data to decision.