Intelligent Cloud ERP Migration Security through AI-Based Dependency Analysis and Automated Cutover Validation Frameworks

Abstract

Enterprise Resource Planning (ERP) systems integrate critical business functions, including finance, human resources, supply chain management, inventory, and customer relationship management. Migrating these interconnected systems to cloud environments introduces security risks associated with application dependencies, data inconsistencies, access-control misconfigurations, integration failures, and operational disruptions. Traditional migration approaches often rely on manual dependency mapping and post-migration validation, which may not adequately address complex enterprise environments. This study proposes an intelligent cloud ERP migration security framework that integrates artificial intelligence (AI)-based dependency analysis with automated cutover validation to improve migration reliability, data integrity, and operational continuity. The framework uses machine learning to identify application relationships, classify migration risks, detect anomalous dependencies, and prioritize critical components. Automated validation mechanisms evaluate data consistency, application functionality, security configurations, interface connectivity, and transaction accuracy before and after cutover. A risk-based migration strategy combines dependency graphs, predictive analytics, automated testing, and rollback decision mechanisms to reduce the likelihood of migration failures. The proposed methodology includes architecture design, dataset preparation, AI model development, controlled migration simulations, and comparative performance evaluation. Key evaluation metrics include dependency detection accuracy, validation coverage, security incident rate, migration downtime, data integrity, and recovery time. The framework aims to establish a scalable, secure, and intelligent approach to cloud ERP migration while supporting business continuity and minimizing operational risks.