Automated compliance verification for AI models in enterprise Cloud MLOps Pipelines

Ravva, Karthik (2025) Automated compliance verification for AI models in enterprise Cloud MLOps Pipelines. World Journal of Advanced Research and Reviews, 26 (3). pp. 1035-1042. ISSN 2581-9615

Ensuring that AI models used in business intelligence systems comply with regulations represents a critical governance challenge as rapid development cycles enabled by MLOps on cloud platforms accelerate model deployment. Manual verification processes prove slow, error-prone, and unscalable in this environment. This article explores techniques and frameworks for automating compliance verification directly within cloud-based MLOps pipelines, investigating the integration of automated checks for fairness, explainability, privacy protection, and robustness testing. The integration of these verification capabilities as mandatory gates in the CI/CD pipeline transforms compliance from a periodic manual activity to an integral part of the development workflow. A reference architecture is proposed that leverages cloud-native services to enforce compliance checks, addressing the challenges of defining quantifiable metrics for complex regulations while enhancing the speed, reliability, and auditability of AI model governance in enterprise cloud environments. The proposed implementation demonstrates how organizations can balance regulatory adherence with innovation velocity, enabling responsible AI deployment at scale.