Cloud Security in the Age of AI: Balancing Automation and Human Oversight for Effective Compliance
Keywords:
environments, adaptability, regulatory, mitigate, resolutionAbstract
This paper explores the balance between AI-driven automation and human oversight in cloud security, focusing on effective compliance monitoring. AI models achieved high accuracy in detecting compliance violations, with detection rates ranging from 90% to 94% across tasks such as data privacy monitoring and access control. Additionally, these models reduced response times by 40% in routine incidents. However, high false positive rates (up to 5%) were observed in complex scenarios, highlighting the need for human intervention to mitigate alert fatigue and improve resolution accuracy. A combined AI + human approach further reduced false positives by 15% and improved incident response accuracy by 12% over AI-only systems. This study proposes a balanced model that maximizes the strengths of both AI efficiency and human expertise. The findings offer a comprehensive framework for building secure and compliant cloud environments while maintaining adaptability to evolving regulatory requirements and complex risk landscapes.
Downloads
References
Mahajan, Varun. "From Compliance to Cost Optimization: AI’s Role in Modern Cloud Security Strategies." Journal of Artificial Intelligence Research 3.1 (2023): 239-275.
Sathupadi, Kaushik. "Management strategies for optimizing security, compliance, and efficiency in modern computing ecosystems." Applied Research in Artificial Intelligence and Cloud Computing 2.1 (2019): 44-56.
Khadka, Prajwal. "AI-Enhanced Cloud Computing: A Comprehensive Review of Techniques, Challenges, and Future Directions in Resource Management, Fault Tolerance, and Security Automation." International Journal of Applied Machine Learning and Computational Intelligence 12.11 (2022): 1-10.
Nguyen, Mai Trinh, and Minh Quang Tran. "Balancing security and privacy in the digital age: an in-depth analysis of legal and regulatory frameworks impacting cybersecurity practices." International Journal of Intelligent Automation and Computing 6.5 (2023): 1-12.
Nadir, Javeria, and Jemma Farah. "Balancing AI Innovation and Data Protection: Regulatory Challenges and Opportunities." (2023).
Bahir Iqbal, Hamid Ali. "Network Security in the Era of AI: Leveraging Cloud-Based Solutions for Robust Infrastructure Protection." (2021).
Parveen, Najma, and Fahad Basit. "Securing Data in Motion and at Rest: AI and Machine Learning Applications in Cloud and Network Security." (2023).
Joseph, Ashly. "A Holistic Framework for Unifying Data Security and Management in Modern Enterprises." International Journal of Social and Business Sciences 17.10 (2023): 602-609.
Gozman, Daniel, and Leslie Willcocks. "The emerging Cloud Dilemma: Balancing innovation with cross-border privacy and outsourcing regulations." Journal of Business Research 97 (2019): 235-256.
Wall, Ana-Maria. Guidelines for artificial intelligence-driven enterprise compliance management systems. Diss. 2021.
Shneiderman, Ben. "Bridging the gap between ethics and practice: guidelines for reliable, safe, and trustworthy human-centered AI systems." ACM Transactions on Interactive Intelligent Systems (TiiS) 10.4 (2020): 1-31.
Chirra, Dinesh Reddy. "AI-Based Real-Time Security Monitoring for Cloud-Native Applications in Hybrid Cloud Environments." Revista de Inteligencia Artificial en Medicina 11.1 (2020): 382-402.
Aslam, Rehman, and Haida Rasheed. "Integrating AI, Cloud Computing, and IoT for Robust Cybersecurity Ecosystems and Adaptive Threat Mitigation." (2023).
Mohammed, Manzoor Anwar. "Ethical Implications of AI Adoption in HRM: Balancing Automation with Human Values." NEXG AI Review of America 1.1 (2020): 1-15.
Bolanle, Oluwapailerin, and Kehinde Bamigboye. "AI-Powered Cloud Security: Leveraging Advanced Threat Detection for Maximum Protection." International Journal of Trend in Scientific Research and Development 3.2 (2019): 1407-1412.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
