Intelligent Framework for Legacy-to-Cloud Data Migration Using AI-Based Mapping Suggestions and Schema Alignment

Abstract

Organizations across industries increasingly recognize cloud migration as a strategic necessity to achieve scalability, flexibility, modern analytics capabilities, and cost optimization. However, legacy systems—often based on outdated architectures, monolithic databases, inconsistent schemas, and poorly documented data structures—pose significant challenges to seamless cloud adoption. Manual migration approaches are slow, labor-intensive, error-prone, and lack semantic understanding of heterogeneous legacy schemas. This research presents an Intelligent AI-Based Framework for automated legacy-to-cloud data migration using machine learning–driven schema matching, semantic alignment, transformation rule discovery, anomaly detection, and human-in-the-loop validation. By integrating NLP techniques, pattern recognition, and predictive models, the proposed framework automates schema mapping, standardizes legacy data, improves mapping accuracy, and reduces migration time. A real-world case study demonstrates the effectiveness of the framework in an insurance organization migrating from COBOL-based mainframe systems to AWS cloud databases. Results indicate that the intelligent framework enhances accuracy, minimizes data-loss risks, and significantly improves overall migration efficiency. This work expands on principles discussed in your sample paper on blockchain-enabled AI systems and adopts a similar structure and analytical depth