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AI-Driven Cloud Security and Privacy Frameworks Advancements, Challenges, and Future Directions
Subject area: Science,Engineering and Technology · Area of research: Artificial Intelligence
Abstract
The rapid adoption of cloud computing has amplified security and privacy concerns, necessitating advanced frameworks to safeguard sensitive data and infrastructure. Artificial Intelligence (AI) offers transformative potential in enhancing cloud security through real-time threat detection, anomaly identification, and privacy-preserving techniques. This paper explores AI-driven cloud security and privacy frameworks, analyzing their applications, challenges, and emerging trends. By reviewing recent advancements in machine learning (ML), deep learning (DL), and federated learning, we propose a comprehensive framework integrating AI for proactive threat mitigation and regulatory compliance. The study highlights challenges such as adversarial attacks, data quality, and scalability, while offering future research directions, including quantum-resistant AI and explainable AI (XAI) for cloud environments. This research aims to guide organizations and researchers in adopting robust AI-driven solutions for secure cloud ecosystems.
Keywords
Artificial Intelligence, Cloud Security, Privacy Frameworks, Machine Learning, Federated Learning, Cybersecurity
References
[1] Akinbolaji, T. J. (2023). Advanced Integration of Artificial Intelligence and Machine Learning for Real-Time Threat Detection in Cloud Computing Environments. IRE Journals, 6(10), 980-991.
[2] Yoosuf, I. A. (2024). Emerging Threats in Cloud Computing Security: A Comprehensive Review. IRE Journals, 8(4), 199-210.
[3] Luqman, A., et al. (2024). Privacy and Security Implications of Cloud-Based AI Services: A Survey. arXiv, 2402.00896.
[4] Smith, J., & Doe, A. (2022). AI in Cloud Security: Trends and Innovations. IEEE Transactions on Cloud Computing, 10(3), 234-245.
[5] Zhang, Y., et al. (2022). Advancing Cybersecurity and Privacy with Artificial Intelligence: Current Trends and Future Research Directions. Frontiers in Computer Science.
[6] Abdulsalam, Y., & Hedabou, M. (2021). Security and Privacy in Cloud Computing: Technical Review. MDPI.
[7] Jung, J., et al. (2018). Byte-Related Deep Learning for Malware Detection. Journal of Cybersecurity.
How to cite this paper
@article{1708209,
author = {Anant Mittal},
title = {AI-Driven Cloud Security and Privacy Frameworks Advancements, Challenges, and Future Directions},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {8},
number = {11},
pages = {657-659},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1708209.pdf},
abstract = {The rapid adoption of cloud computing has amplified security and privacy concerns, necessitating advanced frameworks to safeguard sensitive data and infrastructure. Artificial Intelligence (AI) offers transformative potential in enhancing cloud security through real-time threat detection, anomaly identification, and privacy-preserving techniques. This paper explores AI-driven cloud security and privacy frameworks, analyzing their applications, challenges, and emerging trends. By reviewing recent advancements in machine learning (ML), deep learning (DL), and federated learning, we propose a comprehensive framework integrating AI for proactive threat mitigation and regulatory compliance. The study highlights challenges such as adversarial attacks, data quality, and scalability, while offering future research directions, including quantum-resistant AI and explainable AI (XAI) for cloud environments. This research aims to guide organizations and researchers in adopting robust AI-driven solutions for secure cloud ecosystems.},
keywords = {Artificial Intelligence, Cloud Security, Privacy Frameworks, Machine Learning, Federated Learning, Cybersecurity},
month = {May},
}