Citation:

Rajendra Prasad Sola, 2026. "Advances AI-Enabled Identification of Threats within Zero-Trust Architectures for Secure Cloud Infrastructures: A Comprehensive Survey ", ESP Journal of Engineering & Technology Advancements  6(1): 55-64.

Abstract:

The cloud computing has become a significant component of the majority of the current digital infrastructures. But with its extensive usage has come several complicated security challenges which are difficult to address using the traditional models of depending on perimeters. Among the effective ways of addressing these threats is the concept of Zero Trust Architecture (ZTA), which is premised upon the idea of never trust, always verify. Simultaneously, it is possible to find more complex and unknown cyber threats in the dynamic cloud environment with great potential of AI and ML technologies. The survey provides an elaborate account of the AI-based threat detection systems deployed on Zero Trust Architectures to secure cloud systems. It discusses the fundamental concepts of ZTA, its major architectural layers and plans of its implementation in the cloud ecosystems. The paper also examines the ML,DL and reinforcement-based techniques critically that are used in intrusion detection, anomaly detection and automatic response to security. The gap in the research that is filled by this survey analysis that uses AI intelligence and the zero trust paradigm allows understanding the future trend, illuminates the future direction, and course when creating resilient, flexible, and saleable cloud security systems.

References:

[1] A. Meshram, “Hybrid Cloud Strategy for Mission-Critical Financial Software Applications,” Int. J. Adv. Res. Comput. Commun. Eng., vol. 14, no. 12, p. 136, 2025, doi: 10.17148/IJARCCE.2025.1412136.

[2] S. Srinivasan, R. Sundaram, K. Narukulla, S. Thangavel, and S. B. Venkata Naga, “Cloud-Native Microservices Architectures: Performance, Security, and Cost Optimization Strategies,” Int. J. Emerg. Trends Comput. Sci. Inf. Technol., vol. 4, no. 1, pp. 16–24, 2023, doi: 10.63282/3050-9246.ijetcsit-v4i1p103.

[3] M. S. Mahajan, “Zero Trust Cloud Security and AI for Secure Multi-Cloud Architecture,” vol. 11, no. 4, pp. 750–755, 2024, doi: 10.17148/IARJSET.2024.114110.

[4] S. R. Kurakula, “Cloud-native microservices in financial services: Architecting for scalability and flexibility,” World J. Adv. Res. Rev., vol. 26, no. 2, pp. 1435–1442, May 2025, doi: 10.30574/wjarr.2025.26.2.1690.

[5] V. Prajapati, “Enhancing Threat Intelligence and Cyber Defense through Big Data Analytics: A Review Study,” J. Glob. Res. Math. Arch., vol. 12, no. 4, 2025.

[6] P. Chandrashekar and M. Kari, “Design Machine Learning-Based Zero-Trust Intrusion Identification Models for Securing Cloud Computing System,” Int. J. Res. Anal. Rev., vol. 11, no. 4, pp. 901–907, 2024.

[7] M. Mangla, “AI-Driven Zero Trust Architecture: A Scalable Framework for Threat Detection and Adaptive Access Control,” IJST, vol. 2, no. 3, 2023, doi: 10.56127/ijst.v2i3.22.

[8] R. Palwe, “Onboarding for AI features: Reducing friction at the first use,” Int. J. Comput. Artif. Intell., vol. 6, no. 2, pp. 393–400, Jul. 2025, doi: 10.33545/27076571.2025.v6.i2e.227.

[9] M. Shore, S. Zeadally, and A. Keshariya, “Zero Trust: The What, How, Why, and When,” Computer (Long. Beach. Calif)., vol. 54, no. 11, pp. 26–35, 2021, doi: 10.1109/MC.2021.3090018.

[10] S. Narang and A. Gogineni, “Zero-Trust Security in Intrusion Detection Networks: An AI-Powered Threat Detection in Cloud Environment,” Int. J. Sci. Res. Mod. Technol., vol. 4, no. 5, pp. 60–70, Jun. 2025, doi: 10.38124/ijsrmt.v4i5.542.

[11] S. Ameer, L. Praharaj, R. Sandhu, S. Bhatt, and M. Gupta, “ZTA-IoT: A Novel Architecture for Zero-Trust in IoT Systems and an Ensuing Usage Control Model,” ACM Trans. Priv. Secur., vol. 27, no. 3, Aug. 2024, doi: 10.1145/3671147.

[12] C. Zanasi, S. Russo, and M. Colajanni, “Flexible zero trust architecture for the cybersecurity of industrial IoT infrastructures,” Ad Hoc Networks, vol. 156, p. 103414, Apr. 2024, doi: 10.1016/j.adhoc.2024.103414.

[13] S. Ahmadi, “Zero Trust Architecture in Cloud Networks: Application, Challenges and Future Opportunities,” J. Eng. Res. Reports, vol. 26, pp. 215–228, 2024, doi: 10.9734/JERR/2024/v26i21083.

[14] V. Shah, “Securing the Cloud of Things : A Comprehensive Analytics of Architecture , Use Cases , and Privacy Risks,” ESP J. Eng. Technol. Adv., vol. 3, no. 4, pp. 158–165, 2023, doi: 10.56472/25832646/JETA-V3I8P118.

[15] S. Amrale, “Proactive Resource Utilization Prediction for Scalable Cloud Systems with Machine Learning,” Int. J. Res. Anal. Rev. (IJRAR, vol. 10, no. 4, pp. 758–764, 2023, doi: 10.56472/25832646/JETA-V3I8P119.

[16] V. Varma, “Secure Cloud Computing with Machine Learning and Data Analytics for Business Optimization,” ESP J. Eng. Technol. Adv., vol. 4, no. 3, pp. 181–188, 2024, doi: 10.56472/25832646/JETA-V4I3P119.

[17] S. K. Chintagunta and S. Amrale, “Enhancing Cloud Database Security Through Intelligent Threat Detection and Risk Mitigation,” Tech. Int. J. Eng. Res., vol. 9, no. 10, pp. 49–55, 2022, doi: 10.56975/tijer.v9i10.159996.

[18] N. Mohamed, “Current trends in AI and ML for cybersecurity: A state-of-the-art survey,” Cogent Eng., vol. 10, no. 2, pp. 1–30, 2023, doi: 10.1080/23311916.2023.2272358.

[19] V. M. L. G. Nerella, “Architecting secure, automated multi-cloud database platforms strategies for scalable compliance,” Int. J. Intell. Syst. Appl. Eng., vol. 9, no. 1, pp. 128–138, 2021.

[20] M. H. Hnini and Z. Bensakif, “Exploring The Cloud Security Landscape: Challenges, Solutions, And Insights From Academic Inquiry,” Ensa,” ENSA, vol. January, pp. 1–21, 2025.

[21] H. P. Kapadia, “Zero Trust Architecture In Banking Web Applications,” vol. 14, no. 2, pp. 112–118, 2024.

[22] D. Patel, “Zero Trust and DevSecOps in Cloud-Native Environments with Security Frameworks and Best Practices,” Int. J. Adv. Res. Sci. Commun. Technol., vol. 3, no. 3, pp. 454–464, Jan. 2023, doi: 10.48175/IJARSCT-11900D.

[23] S. B. Karri, C. M. Penugonda, S. Karanam, M. Tajammul, S. Rayankula, and P. Vankadara, “Enhancing Cloud-Native Applications: A Comparative Study of Java-To-Go Micro Services Migration,” Int. Trans. Electr. Eng. Comput. Sci., vol. 4, no. 1, pp. 1–12, Apr. 2025, doi: 10.62760/iteecs.4.1.2025.127.

[24] A. Parupalli and H. Kali, “An In-Depth Review of Cost Optimization Tactics in Multi-Cloud Frameworks,” Int. J. Adv. Res. Sci. Commun. Technol., vol. 3, no. 5, pp. 1043–1052, Jun. 2023, doi: 10.48175/IJARSCT-11937Q.

[25] N. K. Prajapati, “Federated Learning for Privacy-Preserving Cybersecurity: A Review on Secure Threat Detection,” Int. J. Adv. Res. Sci. Commun. Technol., vol. 5, no. 4, pp. 520–528, Apr. 2025, doi: 10.48175/IJARSCT-25168.

[26] N. Mohamed, “Current trends in AI and ML for cybersecurity: A state-of-the-art survey,” Cogent Eng., vol. 10, no. 2, Dec. 2023, doi: 10.1080/23311916.2023.2272358.

[27] S. Kumara, “AI-Driven Threat Identification and Response: Implications for Secure and Scalable Telecom Infrastructure,” Int. J. Adv. Res. Sci. Commun. Technol., vol. 5, no. 4, p. 559, Dec. 2025, doi: 10.48175/IJARSCT-30567.

[28] R. Dattangire, R. Vaidya, D. Biradar, and A. Joon, “Exploring the Tangible Impact of Artificial Intelligence and Machine Learning: Bridging the Gap between Hype and Reality,” in 2024 1st International Conference on Advanced Computing and Emerging Technologies (ACET), IEEE, 2024, pp. 1–6. doi: 10.1109/ACET61898.2024.10730334.

[29] H. Liu and B. Lang, “Machine Learning and Deep Learning Methods for Intrusion Detection Systems: A Survey,” Appl. Sci., vol. 9, no. 20, p. 4396, Oct. 2019, doi: 10.3390/app9204396.

[30] G. Maddali, “Enhancing Database Architectures with Artificial Intelligence (AI),” Int. J. Sci. Res. Sci. Technol., vol. 12, no. 3, pp. 296–308, May 2025, doi: 10.32628/IJSRST2512331.

[31] A. R. Bilipelli, “AI-Driven Intrusion Detection Systems for Large- Scale Cybersecurity Networks Data Analysis : A Comparative Study,” TIJER – Int. Res. J., vol. 11, no. 12, pp. 922–928, 2024.

[32] G. Sarraf and V. Pal, “Adaptive Deep Learning for Identification of Real-Time Anomaly in Zero-Trust Cloud Networks,” vol. 4, no. 3, pp. 209–218, 2024, doi: 10.56472/25832646/JETA-V4I3P122.

[33] R. B and C. Meenakshi, “Zero Trust Network and Cloud Security Architecture: A New Wave of Access Control Techniques,” in 2025 3rd International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI), 2025, pp. 516–519. doi: 10.1109/ICoICI65217.2025.11253860.

[34] H. A. Igwe, “AI-Driven Framework for Automating Infrastructure Provisioning and Compliance Validation in Cloud-Native Environments,” in 2025 IEEE 14th International Conference on Consumer Electronics - Berlin (ICCE-Berlin), 2025, pp. 158–162. doi: 10.1109/ICCE-Berlin67488.2025.11277463.

[35] D. Chaudhary, S. K. Verma, V. Mohan Shrimal, R. Madala, R. Baliyan, and S. M, “AI-Based Methods to Detect and Counter Cyber Threats in Cloud Environments to Strengthen Cloud Security,” in 2024 International Conference on Electrical Electronics and Computing Technologies (ICEECT), 2024, pp. 1–6. doi: 10.1109/ICEECT61758.2024.10739173.

[36] D. Tocci, R. Zhou, and K. Zhang, “FPGA Accelerated Decentralized Reinforcement Learning for Anomaly Detection in UAV Networks,” in 2023 IEEE 16th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC), 2023, pp. 248–253. doi: 10.1109/MCSoC60832.2023.00044.

[37] M. Mehmood, R. Amin, M. M. A. Muslam, J. Xie, and H. Aldabbas, “Privilege Escalation Attack Detection and Mitigation in Cloud Using Machine Learning,” IEEE Access, vol. 11, pp. 46561–46576, 2023, doi: 10.1109/ACCESS.2023.3273895.

[38] E. Opara, H. Wimmer, and C. M. Rebman, “Auto-ML Cyber Security Data Analysis Using Google, Azure and IBM Cloud Platforms,” in 2022 International Conference on Electrical, Computer and Energy Technologies (ICECET), 2022, pp. 1–10. doi: 10.1109/ICECET55527.2022.9872782

Keywords:

Zero Trust Architecture, Cloud Security, Threat Detection, Machine Learning, Deep Learning, Reinforcement Learning, Cybersecurity.