Predictive strength without governance creates fragility. This chapter presents model governance and data integrity as production infrastructure for risk systems. It outlines practical controls: model inventory and ownership, documentation standards, governed datasets with lawful provenance, time-stamped snapshots, feature lineage, and reproducible training artifacts. The chapter explains how data integrity failures-schema drift, logging gaps, silent feature corruption, or broken label pipelines-can degrade models as severely as adversarial behavior. Change control and approvals are framed as mechanisms for safe iteration, ensuring that improvements do not introduce regressions or compliance risks. Governance is not bureaucracy; it enables teams to answer, under pressure, what model ran on what data, when, and why. We also connect governance to organizational practice: who owns models, how exceptions are handled, and how audit-ready artifacts reduce friction during incidents, reviews, and regulator inquiries. This foundation enables controlled deployment, monitoring, and continuous improvement without sacrificing accountability or speed. It also supports credible communication with stakeholders when outcomes are challenged.