The Next-Generation Cloud Security Model: AI-Powered Zero Trust and Adaptive Threat Prevention
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V6I1P110Keywords:
Cloud Security, Zero Trust Architecture, Adaptive Threat Prevention, Artificial Intelligence, Machine Learning, Threat Detection, Real-Time Response, Data PrivacyAbstract
Cloud computing has revolutionized the way enterprises manage their IT resources, but it has also introduced new challenges in terms of security. Traditional cloud security models, which primarily rely on perimeter-based defences, have proven inadequate in addressing sophisticated cyber threats and insider attacks. The shift towards next-generation security frameworks has led to the adoption of Zero Trust Architecture (ZTA) and Adaptive Threat Prevention (ATP) models, both of which emphasize continuous monitoring and dynamic responses to emerging threats. The integration of Artificial Intelligence (AI) into these models promises to further enhance cloud security by providing predictive analytics, automating threat responses, and facilitating real-time decision-making. Zero Trust, based on the principle of "never trust, always verify," ensures that no user or device is implicitly trusted, regardless of its location within the network. When coupled with AI, Zero Trust becomes more efficient, enabling adaptive and context-aware security policies that are continuously updated based on real-time data. ATP, on the other hand, utilizes machine learning and AI to predict, detect, and mitigate threats before they can cause damage, ensuring that security measures evolve in line with the constantly changing threat landscape. This paper explores the synergy between AI-powered Zero Trust and Adaptive Threat Prevention, examining how these technologies can transform cloud security. The combination of these advanced models offers significant advantages, including enhanced detection of anomalous behaviour, automated incident response, and scalability. However, challenges remain in implementing these systems, particularly in terms of complexity, resource requirements, and data privacy concerns. This work aims to provide a comprehensive understanding of these emerging models, their potential benefits, and the challenges associated with their adoption in cloud environments
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References
[1] S. H. L. Akinyele, O. O. Fagbohun, "A Survey of Cloud Computing Security Issues and Challenges," International Journal of Computer Science and Network Security, vol. 12, no. 7, pp. 101-109, 2022.
[2] S. K. Sharma, M. S. Gaur, "Zero Trust Security Model: Evolution, Architecture, and Key Principles," Journal of Cybersecurity and Privacy, vol. 3, no. 4, pp. 12-20, 2021.
[3] P. Kumar, R. Verma, "Artificial Intelligence in Cybersecurity: Threat Detection and Prevention in Cloud Networks," IEEE Access, vol. 8, pp. 90710-90724, 2021.
[4] Patel, R. Singh, "Adaptive Threat Prevention: A Machine Learning-Based Approach for Dynamic Security," International Journal of Security and Networks, vol. 17, no. 2, pp. 90-102, 2021.
[5] M. K. M. Singh, K. Tiwari, "AI in Cloud Security: Automated Threat Detection and Response," IEEE Transactions on Cloud Computing, vol. 10, no. 3, pp. 1348-1356, 2020.
[6] J. S. T. McKay, L. S. Jeffries, "The Role of AI in Evolving Cloud Security: Zero Trust and Beyond," Journal of Cloud Computing, vol. 11, no. 3, pp. 145-158, 2021.
[7] R. C. Bradley, R. Anderson, "Machine Learning in Cloud Security: A Survey of Threat Detection Approaches," Journal of Network and Computer Applications, vol. 21, no. 5, pp. 299-311, 2022.
[8] T. Johnston, H. R. Patel, "Securing the Cloud: Emerging Technologies and Techniques in Cloud-Native Security," IEEE Transactions on Cloud Computing, vol. 8, no. 4, pp. 532-545, 2021.
[9] M. J. Green, T. D. Foster, "Adversarial Machine Learning: Risks and Countermeasures in Cloud Security," IEEE Transactions on Information Forensics and Security, vol. 15, no. 6, pp. 986-998, 2022.
[10] J. L. Harris, "Enhancing E-Commerce Security: The Role of Adaptive Threat Prevention Systems," International Journal of Cloud Security, vol. 6, no. 2, pp. 43-55, 2021.
[11] F. G. Turner, "Insider Threat Detection in Financial Services: A Case Study of Adaptive Threat Prevention," Journal of Financial Technology and Security, vol. 13, no. 3, pp. 112-126, 2022.
[12] Kodi D, “Multi-Cloud FinOps: AI-Driven Cost Allocation and Optimization Strategies”, International Journal of Emerging Trends in Computer Science and Information Technology, pp. 131-139, 2025.
[13] Aragani, V. M. (2022). “Unveiling the magic of AI and data analytics: Revolutionizing risk assessment and underwriting in the insurance industry”. International Journal of Advances in Engineering Research (IJAER), 24(VI), 1–13.
[14] L. N. R. Mudunuri and V. Attaluri, “Urban development challenges and the role of cloud AI-powered blue-green solutions,” In Advances in Public Policy and Administration, IGI Global, USA, pp. 507–522, 2024.
[15] Praveen Kumar Maroju, Venu Madhav Aragani (2025). Predictive Analytics in Education: Early Intervention and Proactive Support with Gen AI Cloud. Igi Global Scientific Publishing 1 (1):317-332.
