AI-Driven Data Migration: Intelligent, Secure, and Scalable Approaches for Modernization
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V4I2P103Keywords:
AI-driven data migration, Machine learning in data migration, Data integrity, Anomaly detection, Predictive analytics, Federated learning, Explainable AI (XAI), Legacy system modernization, Data validation automation, Secure data migrationAbstract
AI-driven data migration is transforming the way organizations modernize legacy systems, offering intelligent automation, real-time error detection, and enhanced data integrity. This article explores how machine learning models automate complex processes such as data mapping, validation, and anomaly detection, enabling accurate and consistent data transformation. It highlights the role of adaptive algorithms and predictive analytics in identifying potential migration bottlenecks, optimizing resource allocation, and minimizing disruptions. The article also introduces federated learning as a privacy-preserving technique for handling sensitive, distributed datasets during migration. Additionally, it emphasizes the importance of explainable AI (XAI) tools to ensure transparency, traceability, and regulatory compliance throughout the migration lifecycle. By integrating these advanced AI methodologies, organizations can achieve secure, efficient, and scalable data transitions, critical for industries that demand precision and reliability. This comprehensive approach establishes a new standard for software systems modernization, addressing both operational complexity and compliance in today’s data-driven environment
References
1. Google Cloud. (2023). AI/ML for Data Migration. Retrieved from https://cloud.google.com
2. Amazon Web Services. (2022). Using Machine Learning for Data Cleansing and Migration. Retrieved from https://aws.amazon.com
3. Microsoft Azure. (2023). Data Migration with Azure Machine Learning. Retrieved from https://learn.microsoft.com/en-us/azure
4. Bonatti, P. A., & Liumbruno, R. (2021). Privacy-Preserving AI via Federated Learning. Journal of Artificial Intelligence Research, 70, 159–190.
5. Shapley, L. S. (2017). A Unified Approach to Explainable AI. In Advances in Neural Information Processing Systems (NeurIPS).
6. Intel. (2022). Federated Learning for Healthcare Data: Security and Efficiency. Retrieved from https://www.intel.com
7. IBM. (2023). Modernizing Legacy Systems with AI-Powered DataOps. Retrieved from https://www.ibm.com
8. Rajkomar, A., et al. (2018). Scalable and Accurate Deep Learning for Electronic Health Records. npj Digital Medicine, 1(1), 18.
9. Oracle. (2022). Data Validation Using Machine Learning in Autonomous Databases. Retrieved from https://www.oracle.com
10. Facebook AI. (2020). Open-Sourced Tools for Federated Learning at Scale. Retrieved from https://ai.facebook.com
11. Microsoft Research. (2022). SHAP and LIME for Transparent AI Deployment. Retrieved from https://www.microsoft.com/en-us/research
12. TensorFlow. (2023). TensorFlow Federated Documentation. Retrieved from https://www.tensorflow.org/federated
13. Datadog. (2023). Real-Time Observability in Migration Workflows. Retrieved from https://www.datadoghq.com
14. Zhang, Y., et al. (2021). A Survey on Explainable Artificial Intelligence (XAI): Fundamentals, Techniques and Applications. ACM Computing Surveys, 54(7), 1-38.
