AI-Driven Encryption Techniques for Data Security in Cloud Computing

Rajashekhar Reddy Kethireddy

doi:10.70589/JRTCSE.2021.1.3

Authors

Rajashekhar Reddy Kethireddy DevOps Engineer, IBM, USA Author

DOI:

https://doi.org/10.70589/JRTCSE.2021.1.3

Keywords:

AI-Driven Encryption, Cloud Security, Machine Learning Algorithms, Adaptive Encryption, Threat Detection

Abstract

As cloud computing continues to revolutionize how organizations store and manage data, ensuring robust security remains a paramount concern. Traditional encryption methods, while effective, often struggle to keep pace with the dynamic and scalable nature of cloud environments. This paper explores the integration of artificial intelligence (AI) into encryption techniques to enhance data security in cloud computing. By leveraging machine learning algorithms and neural networks, AI-driven encryption can adapt to evolving threats, optimize key management, and automate the detection of vulnerabilities. We examine various AI-based approaches, including predictive analytics for threat anticipation, adaptive encryption schemes that respond to real-time data patterns, and intelligent anomaly detection systems that identify and mitigate unauthorized access attempts. Through comparative analysis and case studies, we demonstrate how these advanced techniques not only strengthen encryption mechanisms but also improve overall system resilience and efficiency. The findings indicate that AI-enhanced encryption offers significant advantages in protecting sensitive information against increasingly sophisticated cyberattacks. Furthermore, the paper discusses the challenges of implementing AI-driven solutions, such as computational overhead and the need for comprehensive training data, while proposing strategies to overcome these obstacles. Ultimately, this study highlights the potential of AI to transform data security in cloud computing, providing a foundation for future research and development in this critical area.

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