Cloud Security Automation through Self-Healing AI Agents and Real-Time Enterprise Threat Intelligence Orchestration
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Abstract
The rapid adoption of cloud computing, microservices, containers, serverless platforms, and distributed enterprise applications has created increasingly dynamic security environments that are difficult to protect through conventional manual monitoring and response mechanisms. This study proposes a cloud security automation framework based on self-healing artificial intelligence agents and real-time enterprise threat intelligence orchestration. The proposed approach combines machine learning, autonomous security agents, threat intelligence feeds, continuous monitoring, automated decision-making, and policy-driven remediation to identify and respond to cybersecurity incidents with minimal human intervention. Self-healing AI agents continuously observe cloud workloads, network activity, identities, applications, and infrastructure configurations to detect deviations from established security baselines. Real-time threat intelligence is subsequently correlated with observed events to determine threat relevance, severity, and potential attack progression. Automated orchestration mechanisms can then initiate proportionate remediation actions, including credential restriction, workload isolation, configuration correction, malicious traffic blocking, and restoration of secure states. The research emphasizes explainability, human oversight, secure automation, and adaptive learning to reduce the risks associated with autonomous decision-making. The proposed framework aims to reduce detection and response latency, minimize operational disruption, improve resilience against evolving threats, and establish a continuously adaptive security architecture for modern enterprise cloud environments
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1. Driss, M., Almomani, I., Huma, Z. E., & Ahmad, J. (2022). A federated learning framework for cyberattack detection in vehicular sensor networks. Complex & Intelligent Systems, 8, 4221–4235.
2. Mohile, A., Yadav, A. L., Mukherjee, U., Kapoor, R., Attri, V., & Reddy, R. R. (2026, May). Ethical AI Framework for Protecting Human Rights in Digital Surveillance Systems. In 2026 International Conference on Computational Robotics, Testing and Engineering Evaluation (ICCRTEE) (pp. 1-6). IEEE.
3. Jayaraman, S., Rajendran, S., & P, S. P. (2019). Fuzzy c-means clustering and elliptic curve cryptography using privacy preserving in cloud. International Journal of Business Intelligence and Data Mining, 15(3), 273-287.
4. Padmanabham, S. (2025). An Empirical Study on Low-Code Platforms for Business Process Automation in Hybrid Cloud Environments. Journal Of Engineering And Computer Sciences, 4(7), 655-661.
5. Hasan, M., Rahman, S. A., Yasin, M., Gazi, M. S., Himeluzzaman, M., Alam, A., & Jakir, T. (2026). Heart disease prediction using artificial intelligence algorithms: A comparative study. Vascular and Endovascular Review, 9(1), 436–445.
6. Jain, R. (2019). The hidden tax: A production framework for cloud cost architecture in enterprise data workloads. International Journal of Science, Research and Technology (IJSRAT), 2(6), 2522–2530.
7. Patel, K. (2026). AI-Powered HACCP Risk Prediction System: Machine Learning Framework for Predictive Risk Assessment in HACCP-Based Food Safety Systems. Journal of Intelligent Decision Making and Information Science, 3(5s), 1956-1978.
8. Polamarasetty, V. K. (2021). Modernizing SAP sales and distribution systems through ABAP-based enterprise solutions. International Journal of Research and Applied Innovations, 4(2), 4925–4930.
9. Kargeti, H. (2026, February). Automating Enterprise Vulnerability Exposure: SCAP-Based Asset-CVE Linkage with Social Signal Triage for Cyber Defense. In 2026 International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA) (pp. 1-6). IEEE.
10. Tyagi, N. (2025). Explainability-Driven Differentiation: Responsible AI as a Trust Catalyst in Digital Banking Ecosystems. International Journal of Research and Applied Innovations, 8(3), 13043-13052.
11. Sudhan, S. K. H. H., & Kumar, S. S. (2016). Gallant Use of Cloud by a Novel Framework of Encrypted Biometric Authentication and Multi Level Data Protection. Indian Journal of Science and Technology, 9, 44.
12. Balaraman, N. K., Patel, K., Mahendran, P. K. R., & Kasarla, N. R. (2025, August). AI Driven Predictive Maintenance in Water and Wastewater Systems: Enhancing Efficiency, Reliability, and Sustainability. In 2025 IEEE 16th Control and System Graduate Research Colloquium (ICSGRC) (pp. 93-98). IEEE.
13. Nisar, K. (2024). Prompting, retrieval, and fine-tuning: Foundations of enterprise language model adaptation. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(6), 9310-9319.
14. Ravichandran, S., & Kandasamy, V. (2025). Optimized Attention Augmented Residual Convolutional Neural Network with Fa-Resnet for Fabric Defect Detection. Journal of Control Engineering and Applied Informatics, 27(4), 3-15.
15. Gopinathan, V. R. (2023). Intelligent Cloud Security through Continuous Threat Detection and Risk Assessment. International Research Journal of Innovative Engineering, 7(6), 13571-13581.
16. Pasupuleti, N. S., Kapoor, S., Vedula, J., Sati, M. M., Shamilevna, G. S., & Khurmat, E. (2025, July). Implementation of a Deep Learning Model for Real-time Detection of Diabetic Retinopathy in Primary Care Clinics. In 2025 International Conference on Information, Implementation, and Innovation in Technology (I2ITCON) (pp. 1-6). IEEE.
17. Vimal Raja, G. (2024). Intelligent data transition in automotive manufacturing systems using machine learning. International Journal of Multidisciplinary and Scientific Emerging Research, 12(2), 515-518.
18. Mole, M. (2025). Human-AI interaction in public safety: Preventing crime and improving policing. Journal of Multidisciplinary, 5(7), 169–176.
19. Badam, L. R. (2022). Machine learning-based catastrophic loss prediction for climate-related insurance risk. International Journal of Future Innovative Science and Technology (IJFIST), 5(1), 7797–7807.
20. Alvi, Y. M., Kumar, A., & Goel, S. (2026, April). Optimal Clustering with Deep Reinforcement Learning for Supply Chain Management. In 2026 International Conference on Frontiers of Engineering and Emerging Technologies (FET) (pp. 1-5). IEEE.
21. Bandaru, P. K. (2025). Achieving production readiness in software-defined vehicle platforms through comprehensive verification. International Journal of Research Publications in Engineering, Technology and Management, 8(2), 11789–11793.
22. Sugumar, R. (2026). Modernizing Healthcare Software Delivery through Predictive AI Decision Support Cybersecurity and Real-Time Threat Detection. International Journal of Emerging Trends in Engineering and Management Research, 11(2), 19651.
23. Jayabalan, K., & Radhakrishnan, S. (2025, December). C-STEN Contrastive Spatial-Temporal Embedding Network for Robust Credit Card Fraud Detection. In 2025 IEEE 1st International Conference on Recent Trends in Computing and Smart Mobility (RCSM) (pp. 1-7). IEEE.
24. Vemireddy, S. (2024). Secure and scalable intelligent service architectures for next-generation enterprise applications. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(4), 8177–8182.
25. Anand, L. (2025). Modernizing Enterprise Systems through Generative AI Autonomous Operations and Cloud-Native Engineering. International Journal of Humanities and Information Technology, 7(02), 54-69.
26. Punithavathi, R., Selvi, R. T., Latha, R., Kadiravan, G., Srikanth, V., & Shukla, N. K. (2022). Robust node localization with intrusion detection for wireless sensor networks. Intelligent Automation and Soft Computing, 33(1), 143-156.
27. Kumar, R., Upadhyay, H., Pandey, C. P., & Kumar, P. R. (2026, April). Quantum Computing as a Service (QCaaS): Architecture, Orchestration, and Performance Tradeoffs. In 2026 International Conference on Computing Theory and Wireless Communications (ICCTWC) (pp. 1-11). IEEE.
28. Chundi, V. R. K. (2025). AI-based Sustainable Vehicle Monitoring System for Existing Internal Combustion Vehicles. London Journal of Research In Computer Science and Technology, 25(3), 1-7.
29. Awopejo, T. E., Adigun, P. O., Oyekanmi, T. T., Azeez, N. A. A., Adekanye, M. A., & Obisesan, A. (2025). Machine learning-based prediction of magnetic properties from hysteresis curves: A comparative study of Random Forest, Gradient Boosting, XGBoost, LightGBM and Support Vector Regressions. International Journal of Research Publications in Engineering, Technology and Management, 8(6), 13456–13479.
30. Raja, G. V. (2023). AI Driven Secure Intelligent Framework for Fraud Detection Cybersecurity and Cloud Based Enterprise Systems. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 6(5), 9068-9076.
31. Pothuri, M. K. (2025). Designing a metadata-driven framework for automated data profiling, data analysis, data management, integration at scale in Medicaid healthcare ecosystems. International Journal of Multidisciplinary Research and Growth Evaluation, 6(4), 1413-1418.
32. Mudunuri, L. N. R., Maroju, P. K., & Aragani, V. M. (2025, January). Leveraging nlp-driven sentiment analysis for enhancing decision-making in supply chain management. In 2025 Fifth International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT) (pp. 1-6). IEEE.
33. Mathew, A. (2023). Sentinel AI: An Investigation into Robust Threat Mitigation Strategies for Artificial Intelligence. Educational Research (IJMCER), 5(5), 108-111.
34. Himeluzzaman, M., Alam, A., Gazi, M. S., Abdullah, S. M., Chy, M. S. K., Onik, T. A., ... & Shakil, S. M. (2025). Countering AI-Generated Disinformation: A Novel Detection Model to Safeguard National Security. International Journal of Computer Technology and Electronics Communication, 8(4), 11192-11203.
35. Chaturvedi, V., Narra, R., & Chintagunta, S. K. (2026). Applied AI engineering for developers: Building intelligent applications at scale. Wissira Press.
https://doi.org/10.63345/WP-978-93-7559-963-0
36. Mohan, A. (2025). Causal Inference for Real-Time Decision Systems: Bandits, Interleaved Experiments, and the Bias-Speed Tradeoff. Interleaved Experiments, and the Bias-Speed Tradeoff (December 01, 2025).
37. Mali, R. K. (2026, July). AI-Driven Cloud-Native Banking Platforms: A Scalable Architecture for Real-Time Financial Services. In 2026 International Conference on Intelligent and Sustainable AI Systems (ICOSAAS) (pp. 749-756). IEEE.
38. Tarakampet, S., Puvvula, G., Begum, S., Ali, S., Tatavarthi, S., & Bikkavolu, V. (2025). The power of interoperability: Designing custom applications for seamless integration. International Journal of Emerging Information Technology, 1(2), 7–13.
https://doi.org/10.5281/zenodo.20371782
39. Sarhan, M., Layeghy, S., Moustafa, N., & Portmann, M. (2023). Cyber threat intelligence sharing scheme based on federated learning for network intrusion detection. Journal of Network and Systems Management, 31, Article 3.