Deployment is the beginning of model responsibility, not the end. This chapter develops monitoring, fairness, and controlled deployment as a unified discipline for keeping risk models trustworthy in production. It covers data monitoring (coverage, freshness, and distribution shifts), score monitoring (drift, calibration decay, and decision-rate changes), and outcome monitoring (losses, disputes, and operational feedback) using time-respecting signals. Fairness and explainability are treated as measurable controls that preserve legitimacy and reduce concentrated harm in high-stakes decisioning. The chapter then describes controlled rollout patterns-shadow testing, canary releases, champion-challenger evaluation, and rollback mechanisms-that reduce blast radius while preserving iteration speed. Throughout, monitoring is tied to action: Alerts must lead to investigation, mitigation, or retraining, rather than dashboards alone. Special attention is given to selecting monitoring thresholds, avoiding alert fatigue, and ensuring that fairness assessments remain meaningful as populations, products, and attacker behavior shift. The chapter concludes with practical release governance patterns that keep experimentation safe while maintaining operator trust and transparency. By combining live health measurement with disciplined release governance, platforms sustain performance under drift and adversarial adaptation while maintaining transparency, accountability, and operational control.