Self-Healing Cyber Resilience through Advanced Agentic AI in Distributed Hybrid Cloud Enterprises
DOI:
https://doi.org/10.15680/q66g6y07Keywords:
Agentic AI, Self-Healing Cybersecurity, Cyber Resilience, Hybrid Cloud, Autonomous Threat Detection, Intelligent Security Orchestration, Distributed Enterprise SecurityAbstract
Distributed hybrid cloud enterprises face increasingly sophisticated cyber threats that exploit complex infrastructures spanning private data centers, public clouds, edge environments, APIs, containers, virtual machines, and interconnected enterprise applications. Conventional cybersecurity mechanisms often depend on predefined rules, centralized monitoring, and human intervention, limiting their ability to respond rapidly to dynamic and coordinated attacks. This study proposes a self-healing cyber resilience framework based on advanced Agentic Artificial Intelligence (AI), enabling autonomous detection, decision-making, response, recovery, and continuous adaptation across distributed hybrid cloud environments. The proposed framework integrates agentic AI, machine learning, behavioral analytics, threat intelligence, Zero Trust principles, automated orchestration, predictive risk analysis, and policy-driven remediation. Intelligent agents continuously observe infrastructure telemetry, security events, identity activities, network flows, application behavior, and system configurations to identify anomalous conditions and determine appropriate corrective actions. A closed-loop architecture enables the system to isolate compromised resources, modify security policies, redirect workloads, restore trusted configurations, and validate recovery with minimal human intervention. The research methodology combines architecture design, simulated hybrid-cloud experimentation, threat modeling, machine-learning evaluation, and resilience assessment using detection accuracy, response time, recovery time, false-positive rate, service availability, and remediation effectiveness. The proposed approach aims to improve enterprise cyber resilience by transforming security operations from reactive incident handling toward autonomous, adaptive, and continuously self-healing protection.
References
1. Kollu, R. K. (2025). Unlocking Sales Cloud: A Guide to Smarter Selling in Salesforce. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 8(5), 12891-12899.
2. Bhati, R., Ingale, K., Turakne, S., Yadav, L. N., Khetani, V., & Hire, D. (2026). The zero-touch data center: Lights-out operations at scale. International Journal of Computer Information Systems and Industrial Management Applications, 18(1s), 1–8. https://doi.org/10.70917/ijcisim-2026-2036
3. Gaddam, M. K., Kapoor, S., Vedula, J., Manu, M., Usmani, M. A., & Polvanov, S. (2025, July). LiDAR Sensor Simulation in Unity: Real-Time Performance for Autonomous Systems and Virtual Environments. In 2025 International Conference on Information, Implementation, and Innovation in Technology (I2ITCON) (pp. 1-6). IEEE.
4. Soundappan, S. J. (2023). Generative AI-Driven Intelligent Cybersecurity Framework for Secure Hybrid Cloud Enterprise Systems. International Journal of Emerging Trends in Engineering and Management Research, 8(6), 14728.
5. Mahajan, A., Uddandarao, D. P., & Konatham, M. R. (2026). Impact of statistically driven AI forecasting of energy demand under dynamic market conditions. Scientific Culture, 12(2, Part 1), 112–123.
6. Pothuri, M. K. Building a Seamless Healthcare Data Fabric: Zero-Touch Integration and Scalable Mapping Across Provider, Claims, Recipient, and Pharmacy Source Systems for State Medicaid. IJLRP-International Journal of Leading Research Publication, 6(8).
7. Ahuja, D. (2025). DevOps and Ethical AI: Ensuring Responsible Deployment. Journal Of Multidisciplinary, 5(6), 1-14.
8. 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.
9. Bellundagi, M. (2023). Design of an Intelligent Clinical Decision Support System Using Machine Learning Techniques. International Journal of Research and Applied Innovations, 6(6), 10075-10081.
10. Ramasamy, M. (2025). The synergy of human and AI collaboration in modern network management. Journal of Computer Science and Technology Studies, 7(4), 174-180.
11. Valarmathi, P., Maroju, P. K., Mudunuri, L. N. R., Kommineni, M., Aragani, V. M., & Kolasani, S. (2024, November). Implementing Blockchain for Advanced Supply Chain Data Sharing with Practical Byzantine Fault Tolerance (PBFT) Algorithm. In 2024 International Conference on Advances in Computing, Communication and Materials (ICACCM) (pp. 1-6). IEEE.
12. Panda, M. R. (2025). Real-time preemptive fraud detection using adaptive agentic AI for financial transaction risk intelligence. International Journal of Advanced Engineering Science and Information Technology (IJAESIT), 8(5), 17324–17334.
13. Duggineni, K. K., & Muppalla, L. K. (2024). Secure semantic web service architectures: A machine learning framework for adaptive threat detection. International Journal of Advances in Signal and Image Sciences, 40–51.
14. 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
15. Narra, S. L. (2025). Cybersecurity and Digital Equity: Why Strong Identity Management is a Public Good. Journal Of Engineering And Computer Sciences, 4(7), 308-314.
16. Matrouk, K., V, S., Kumar, S., Bhadla, M. K., Sabirov, M., & Saadh, M. J. (2023). Deep Learning–based Dynamic User Alignment in Social Networks. ACM Journal of Data and Information Quality, 15(3), 1-26.
17. Gopinathan, V. R. (2025). Hybrid Artificial Intelligence Framework for Real-Time Cloud Financial Fraud Detection and Adaptive Risk Mitigation. International Journal of Emerging Trends in Engineering and Management Research, 10(6), 18967.
18. Jain, R. (2025). Operationalizing responsible AI and data governance in enterprise modernization. International Journal of Engineering & Extended Technologies Research, 7(6), 11306–11311.
19. 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.
20. Selvarajan, K. (2025). AI-Driven Enterprise Supply Chain Intelligence: A Technical Deep Dive. Journal of Computer Science and Technology Studies, 7(2), 612-617.
21. Vineetha, B., Surendran, R., & Madhusundar, N. (2024, November). Enhancing accuracy in obesity prediction and nutrition guidance through KNN and decision tree models. In 2024 5th International Conference on Data Intelligence and Cognitive Informatics (ICDICI) (pp. 757-762). IEEE.
22. Jayabalan, K., Paramsivan, S., & Radhakrishnan, S. (2025, December). Orchestrating AI Microservices for Adaptive Fraud Detection and Compliance in Modern Financial Systems. In International Conference on Deep Learning and Visual Artificial Intelligence (pp. 618-629). Cham: Springer Nature Switzerland.
23. Venkatasalam, K., Rajendran, P., & Thangavel, M. (2019). Improving the accuracy of feature selection in big data mining using accelerated flower pollination (AFP) algorithm. Journal of medical systems, 43(4), 96.
24. Padmanabham, S. (2025). AI-Augmented Business Process Automation: Architecture and Implementation in Regulated Industries. Journal Of Multidisciplinary, 5(7), 983-991.
25. 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.
26. Patel, K., Patel, K., & Thakare, S. B. (2025, October). Adaptive multi-sensor photometric and domain-adaptive fusion based industrial decision support system for intelligent manufacturing operations. In 2025 2nd International Conference on Software, Systems and Information Technology (SSITCON) (pp. 1-6). IEEE.
27. Agarwal, S. (2025). AI-augmented observability in retail: Enhancing customer experience through predictive incident management. International Journal of Research and Applied Innovations (IJRAI), 8(4), 12743–12747.
28. Mohile, A., Yadav, A. L., Pandey, P. K., & Reddy, R. R. (2026, May). Automated Detection of Human Trafficking Networks Using Multimodal Data Analytics. In 2026 International Conference on Computational Robotics, Testing and Engineering Evaluation (ICCRTEE) (pp. 1-6). IEEE.
29. Anand, L. (2023). Distributed Multi-Cloud Data Lake and Edge Computing Architecture for Intelligent SAP Enterprise Data Integration. International Journal of Computer Technology and Electronics Communication, 6(5), 7636-7344.
30. 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.
31. Selvarajan, K. (2022). Architecting scalable self-service data platforms for enterprise analytics. International Journal of Science, Research and Technology (IJSRAT), 5(2), 7427–7436.
32. 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.
33. Bandaru, P. K. (2022). Hardware-in-the-loop testing for connected vehicles: Enhancing software reliability through continuous validation. International Journal of Engineering & Extended Technologies Research (IJEETR), 4(2), 4645–4651.
34. 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.
35. Pothuri, M. K. Building a Seamless Healthcare Data Fabric: Zero-Touch Integration and Scalable Mapping Across Provider, Claims, Recipient, and Pharmacy Source Systems for State Medicaid. IJLRP-International Journal of Leading Research Publication, 6(8).
36. Mathew, A. (2023). Sentinel AI: An Investigation into Robust Threat Mitigation Strategies for Artificial Intelligence. Educational Research (IJMCER), 5(5), 108-111.
37. Karakondu, M., Jambagi, G., & Tatavarthi, S. (2025). Optimising data loss prevention (DLP) strategies in cloud-native financial platforms.
38. Hashmi, H., & Srikanth, V. (2023, November). Combining Neural Networks to Recognize Offline Digits & Words by Training the Model Using Keras. In 2023 3rd International Conference on Advancement in Electronics & Communication Engineering (AECE) (pp. 694-697). IEEE.
