AI Risk Management for Cloud Platforms with Quantitative Scoring Aligned with NIST AI RMF

Abstract

Cloud-based AI deployment turns model risk into a continuous governance problem because model behavior can change across data updates, inference traffic, monitoring conditions, and adversarial exposure. This article presents a quantitative AI risk management framework aligned with the NIST AI RMF for assessing model governance, bias detection, adversarial resilience, explainability, runtime monitoring, and remediation readiness in enterprise cloud platforms. The framework uses a scorecard matrix to convert each risk domain into measurable indicators, thresholds, and cloud governance triggers. Radarbased maturity analysis shows how AI risk posture improves from initial to adaptive governance levels, while bubble-chart deployment analysis highlights how risk exposure, fairness drift, and adversarial vulnerability change across development, validation, staging, production, and continuous monitoring stages. The study demonstrates that enterprise AI governance becomes stronger when risk scores are measurable, traceable, and directly connected to cloud platform actions such as release blocking, fairness review, adversarial hardening, rollback planning, and retraining workflows.