AI Powered Holistic Cognitive Framework for Intelligent Cloud Network Security Self Healing Enterprise Infrastructure and Digital Trust Systems

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Published: 2025-10-15
DOI: https://doi.org/10.15662/IJRPETM.2025.0805031
Keywords:
Artificial Intelligence, Cognitive Framework, Cloud Security, Self-Healing Systems, Enterprise Infrastructure, Digital Trust, Machine Learning, Cybersecurity, Predictive Analytics, Autonomous Systems, Data Security, Intelligent Systems

Main Article Content

Dr. T. Nalini
Professor, Department of CSE, Saveetha School of Engineering, SIMATS, Chennai, India

Abstract

The increasing complexity of modern digital ecosystems demands intelligent, adaptive, and resilient systems capable of ensuring security, operational continuity, and trust. This paper presents an AI-powered holistic cognitive framework designed to integrate intelligent cloud network security, self-healing enterprise infrastructure, and robust digital trust systems. The proposed framework leverages advanced machine learning, cognitive computing, and real-time analytics to create a unified, adaptive architecture capable of detecting, analyzing, and responding to dynamic threats and system anomalies. A key feature of the framework is its self-healing capability, which enables automated detection and recovery from failures, minimizing downtime and improving system resilience. In cloud network security, the framework employs anomaly detection, predictive threat intelligence, and automated response strategies to enhance protection against evolving cyber threats. Within enterprise infrastructure, it supports intelligent monitoring, predictive maintenance, and resource optimization. Additionally, digital trust is reinforced through explainable AI, encryption, and decentralized identity mechanisms, ensuring transparency, accountability, and data integrity. The proposed framework demonstrates improved performance in threat detection, system recovery, and trust assurance compared to traditional approaches. It provides a scalable and adaptive solution for organizations seeking to secure and optimize their digital environments while fostering trust in AI-driven systems.

Article Details

Issue

Vol. 8 No. 5 (2025): International Journal of Research Publications in Engineering, Technology and Management

Section

Articles

How to Cite

AI Powered Holistic Cognitive Framework for Intelligent Cloud Network Security Self Healing Enterprise Infrastructure and Digital Trust Systems. (2025). International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 8(5), 12929-12938. https://doi.org/10.15662/IJRPETM.2025.0805031

References

1. Rajasekar, M. (2024). Real-time predictive DevOps intelligence for risk-aware digital business processes in cloud and SAP ecosystems. International Journal of Advanced Research in Computer Science & Technology, 7(4), 10713–10718.

2. Potel, R. (2020). AI-enabled post-quantum solutions for anti-counterfeiting and digital trust in global supply chains. International Journal of Computer Technology and Electronics Communication, 3(6), 2937–2944.

3. Sengupta, J., & Alzbutas, R. (2024). Deep learning-based intracranial hemorrhage detection in 3D computed tomography images. In International Conference on WorldS4 (pp. 219–226). Springer.

4. Dave, B. L. (2024). Harnessing artificial intelligence for Salesforce metadata advanced migration strategies and strategic business benefits. International Journal of Advanced Research in Computer Science & Technology, 7(6), 11398–11408.

5. Niture, N., & Abdellatif, I. (2025). A systematic review of factors data sources and prediction techniques for earlier prediction of traffic collision using AI and machine learning. Multimedia Tools and Applications, 84(18), 19009–19037.

6. Kunadi, S. K. (2023). Entity resolution at scale advanced fuzzy matching techniques for company and project data. International Journal of Research Publications in Engineering Technology and Management, 6(1), 8014–8022.

7. Chachra, B. (2023). Strengthening national digital infrastructure privacy focused data pipelines for ethical behavioral analytics. International Journal of Computer Technology and Electronics Communication, 6(4), 7331–7340.

8. Kale, A. (2025). The virtual CFO leading dispersed financial groups using asynchronous technologies. International Journal of Accounting and Management Sciences, 4(4).

9. Gopinathan, V. R. (2023). Cloud-first AI security architecture for protecting enterprise digital ecosystems and financial networks. International Journal of Research and Applied Innovations, 6(6), 10031–10039.

10. Murugeshwari, B., et al. (2020). SAFE secure authentication in federated environment using CEG key code.

11. Mathew, A. (2024). AI TRiSM trust risk and security management in cybersecurity. Cybersecurity, 4(3), 84–90.

12. Vayyasi, N. K. (2023). Optimizing factory maintenance and downtime prediction through Java-driven AI pipelines. International Journal of Research and Applied Innovations, 6(3).

13. Balaji, K. V., & Sugumar, R. (2023). Harnessing the power of machine learning for diabetes risk assessment. In ICDSAAI (pp. 1–6). IEEE.

14. Rajasekar, M., Nahar, G., Jagatheeswaran, S., Chinthamani, S. A. M., Mohammed, S. H., & Al-Hilali, A. (2024, May). The Roadmap to Classify Malware Using ML Algo Through IOT Based SN. In 2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) (pp. 127-130). IEEE.

15. Boddupally, H. L. (2022). Designing intelligent support bot frameworks for scalable enterprise production systems. Journal of Scientific and Engineering Research, 9(10), 108–115.

16. Soundappan, S. J. (2024). AI-driven customer intelligence in enterprise lakehouse systems sentiment mining governance-aware analytics and real-time data synchronization. International Journal of Advanced Engineering Science and Information Technology, 7(5).

17. Singh, A. (2024). Network performance in autonomous vehicle communication. International Journal of Advanced Research in Computer Science & Technology, 7(1), 9712–9717.

18. Anbazhagan, K. (2025). AI driven zero trust security model for enterprise data protection and intelligent infrastructure management. International Journal of Technology Management and Humanities, 11(03), 101–107.

19. Varma, K. K., & Anand, L. (2025). Deep learning driven proactive auto scaler for high-quality cloud services. In International Conference on Computing and Communication Systems (pp. 329–338). Springer.

20. Guda, D. P. (2024). Cyber insurance for DevSecOps risks pricing models and coverage gaps. Journal of Information Systems Engineering and Management, 9(3).

21. Nallamothu, T. K. (2024). The age of smart living how AI is shaping our daily lives in real time. International Journal of Research and Applied Innovations, 7(5), 11456–11468.

22. Anand, L. (2024). AI-powered cloud cybersecurity architecture for risk prediction and threat mitigation in healthcare and finance. International Journal of Research Publications in Engineering Technology and Management, 7(Special Issue 1), 5–12.

23. Loganayagi, S., Balakrishnan, T. S., Vimal, V. R., & Thangam, S. A. (2024, November). Assessing the Efficacy of ML Techniques for Forecasting Healthcare Consumer Readmission: A Comparative Analysis of Risk Factors and Healthcare Interventions. In 2024 International Conference on Smart Technologies for Sustainable Development Goals (ICSTSDG) (pp. 1-7). IEEE.

24. Kaliappan, S., Ragunthar, T., Ali, M., & Murugeshwari, B. (2024). Implementation of Virtual High Speed Data Transfer in Satellite Communication Systems Using PLC and Cloud Computing. In AI Approaches to Smart and Sustainable Power Systems (pp. 274-286). IGI Global Scientific Publishing.

25. Hossain, M. S., Hossain, M. S., Ali, M., & Rahman, M. W. (2025). Data-Driven Strategies for Predicting and Enhancing Rural Business Growth in the United States. Data-Driven Strategies for Predicting and Enhancing Rural Business Growth in the United States, 1(7), 121-146.

26. Selvi, G. V., et al. (2023). Application oriented integrated unequal clustering algorithm for wireless sensor network. In Machine Learning Techniques (pp. 140–154). CRC Press.

27. Gentyala, R. (2024). From bronze to broken a grounded theory study of anti-patterns and accruing data debt in medallion lakehouse deployments. European Journal of Advances in Engineering and Technology, 11(1), 90–100.

28. Chaturvedi, V. (2025). Disease diagnostic systems based on AI applications in healthcare. International Journal of Emerging Research in Engineering and Technology, 6(4), 207–217.

29. Boddupally, H. L. (2022). Toward self-optimizing enterprise applications AI-guided profiling and performance optimization for C# and SQL-based systems. SSRN.

30. Raj, A. M. A., Rajendran, S., & Vimal, G. S. A. G. (2024). Enhanced convolutional neural network enabled optimized diagnostic model for COVID-19 detection. Bulletin of Electrical Engineering and Informatics, 13(3), 1935–1942.

31. Anbazhagan, K. (2024). Trustworthy and Adaptive AI Systems for Enterprise Analytics Cybersecurity and Decision Optimization Using API-First and Cloud-Native Architectures. International Journal of Technology, Management and Humanities, 10(03), 65-74.

32. Gupta, S. Digital Twins for Circular Economy Optimization: A Framework for Sustainable Engineering Systems. Proceedings 2025, 121, 4. [CrossRef]

33. Barigidad, S. (2025). Edge-optimized facial emotion recognition a hybrid Mobilenetv2-ViT model. International Journal of AI BigData Computational and Management Studies, 6(2), 1–10.

34. Mudunuri, P. R. (2023). Governance-aware infrastructure-as-code for regulated research environments. International Journal of Research Publications in Engineering Technology and Management, 6(4), 9017–9027.

35. Vani, S., Malathi, P., Ramya, V. J., Sriman, B., Saravanan, M., & Srivel, R. (2024). An efficient black widow optimization-based faster R-CNN for classification of COVID-19 from CT images. Multimedia Systems, 30(2), 108.

36. Katta, T. B. (2023). Towards unified enterprise integration leveraging hybrid integration platforms. International Journal of Computer Technology and Electronics Communication, 6(5), 7354–7365.