Enterprise AI Model Risk Scoring and Continuous Assurance Framework

AI model portfolios now include predictive models, Generative AI, foundation models, RetrievalAugmented Generation systems, and agentic components with widely different risk profiles. Static approval processes and one-time validation are insufficient because model behavior, data, usage, integrations, and external dependencies can change after deployment. This paper proposes an Enterprise AI Model Risk Scoring and Continuous Assurance Framework that combines risk dimensions, quantitative scoring, control requirements, lifecycle gates, monitoring, change detection, reassessment triggers, and residual-risk reporting. The framework evaluates business impact, autonomy, data sensitivity, model complexity, explainability, external exposure, reversibility, regulatory relevance, security, robustness, provider dependency, and operational resilience. It introduces inherent-risk scoring, control-effectiveness adjustment, residual-risk calculation, confidence scoring, and risk-tier-based assurance frequencies. The proposed approach enables enterprises to prioritize validation and monitoring resources, identify deteriorating controls, and maintain evidence-based assurance throughout the AI lifecycle.