Advances and Challenges in Cloud Data Storage Security: A Systematic Review

Abstract

Cloud computing has significantly changed how data is stored by offering enhanced flexibility and scalability. However, its rapid growth has introduced serious security challenges, particularly concerning data integrity, confidentiality, and availability. This systematic review investigates recent research in cloud data storage security, focusing on research published between 2020 and 2024. A structured selection process led to the inclusion of 77 relevant studies that addressed key research questions. The review synthesizes current knowledge, identifies ongoing challenges, and evaluates six main security techniques, including, encryption, access control, data loss prevention (DLP), blockchain, machine learning, and data redundancy. Each method is analyzed based on its principles, application context, advantages, and limitations, along with a comparative assessment. Encryption is widely adopted and offers strong confidentiality but may reduce system performance. Access control enables accurate access management but is often complex to implement. DLP helps prevent sensitive data leaks but can result false positives. Blockchain improves transparency and trust but introduces latency and integration challenges. Machine learning enhances anomaly detection but depends on large datasets and computational resources. Data redundancy supports data availability but increases storage costs. The findings show that relying on a single method is not sufficient to ensure a complete data protection in cloud environments. A multi-layered approach, integrating various techniques, is necessary, particularly with the increased reliance on cloud services due to the expansion of the Internet of Things and the impact of the COVID-19 pandemic. This review contributes to the field by offering a comprehensive comparison of modern security models and provides direction for future research.