Articles Open Access
Predictive Security and Cloud Infrastructure Optimization using Generative AI and Digital Twin Technology
Professor, Department of CSE, PSCMR College of Engineering and Technology, Vijayawada, India
Abstract
The rapid expansion of cloud computing has increased the complexity of enterprise infrastructure while creating new challenges in cybersecurity, resource management, service reliability, and operational efficiency. Conventional cloud monitoring approaches are predominantly reactive, identifying security incidents or performance problems after they occur. This research proposes an integrated approach to predictive security and cloud infrastructure optimization using Generative Artificial Intelligence (GenAI) and Digital Twin technology. A digital twin provides a virtual representation of cloud infrastructure that continuously reflects the state of physical and virtual resources, workloads, applications, and network interactions. Generative AI can analyze telemetry, simulate possible future conditions, generate threat scenarios, recommend corrective actions, and support automated decision-making. The proposed framework combines real-time infrastructure telemetry, digital twin modeling, anomaly detection, generative threat simulation, predictive analytics, and optimization algorithms. Historical and streaming data are used to construct behavioral baselines and identify deviations in security and resource utilization. The digital twin enables proposed changes to be tested in a simulated environment before being applied to production infrastructure, thereby reducing operational risk. Generative AI further supports what-if analysis by producing potential attack and workload scenarios. The study evaluates the framework using security detection accuracy, prediction performance, resource utilization, service availability, cost efficiency, response time, and optimization effectiveness. The proposed approach aims to establish a proactive cloud-management model capable of predicting security risks and optimizing infrastructure resources while improving resilience, scalability, and operational decision-making
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Publication Information
| Journal | International Research Journal of Innovative Engineering |
|---|---|
| Volume | 9 |
| Number | 6 |
| Pages | 17922-17933 |
| Published | 2025-12-10 |
| Issue | Vol. 9 No. 6 (2025): International Research Journal of Innovative Engineering (IRJIE) |
References
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34. Vani, M., & Dadlani, D. (2023). Smart home – “Aashraya” IoT automation system. International Journal of Computer Technology and Electronics Communication, 6(1), 6393–6403.
35. Karnam, V. S. (2024). Adaptive and federated test automation using AI in distributed multi-cloud and hybrid infrastructure environments. International Journal of Future Innovative Science and Technology (IJFIST), 7(4), 111–121.
36. Macha, Y. (2023). Cloud-Based CRM for Healthcare Data Management: A Review of HIPAA Compliance and Validation Rules. TIJER-Int. Res. J, 10(11), 118-123.
37. Gujarathi, M. (2023). Zero-Downtime Modernization of Enterprise Platforms: Migration Patterns and Operational Considerations. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(3), 8770-8774.
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2. Raja, G. V. (2020). Metadata gets a makeover: The machine learning approach. International Journal of Computer Technology and Electronics Communication, 3(6), 2900-2903.
3. Kundurthy, O. H., Kaata, S. K., Vikram, S., Somayajula, R., & Gangavarapu, R. (2025, September). A Framework for Lightweight Generative AI: Enabling Secure, Scalable, and Cloud-to-Edge Intelligence with MicroLLMs. In 2025 International Conference on Electronics and Computing, Communication Networking Automation Technologies (ICEC2NT) (pp. 1-8). IEEE.
4. Chaba, A. (2018). A platform-independent API integration architecture for scalable enterprise commerce solution. International Journal of Research Publication in Engineering, Technology and Management, 1(1), 9–13.
5. Anand, L. (2022). Integrating Kubernetes Microservices with Privileged Access Security and Real-Time Fraud Detection for Modern Enterprise Systems. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(5), 7453-7461.
6. Beeram, S. (2024). AI-driven intelligent traffic management in Microsoft Azure: Predictive routing for resilient cloud applications. International Journal of Advanced Engineering Science and Information Technology, 7(4), 14534–14538.
7. Mathew, A., & Romasco, L. (2024). Forensic Investigation of Artificial Intelligence Systems. Research Updates in Mathematics and Computer Science Vol. 4, 154-164.
8. Wadhwa, R. (2023). Ensuring data consistency in enterprise systems through event driven microservices and distributed transaction management. International Journal of Computer Science and Engineering Research and Development, 6(1), 24-51.
9. Tyagi, N. (2025). AI in education: Personalized learning through intelligent tutors. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 8(2), 11841-11848.
10. Pokala, H. K. (2022). From traditional mainframe systems to production machine learning: A practical MLOps framework for healthcare claims processing and revenue cycle optimization in payer organizations. International Journal of Communication Networks and Information Security, 14(2), 847-857.
11. Sudhan, S. K. H. H., & Kumar, S. S. (2015). An innovative proposal for secure cloud authentication using encrypted biometric authentication scheme. Indian journal of science and technology, 8(35), 1-5.
12. Singh, I. K. (2025). Intelligent software validation frameworks for mission-critical enterprise applications using AI and knowledge graphs. International Journal of Research and Applied Innovations, 8(4), 12723-12735.
13. Challa, R. (2023). Engineering AI Factories: Scalable HPC Design Patterns for Next-Generation Foundation Model Infrastructure. International Journal of Computer Technology and Electronics Communication, 6(4), 7342-7351.
14. Bandhela, R. R., & Kundavaram, R. R. (2024). Leveraging machine learning algorithms for real-time health risk assessment and personalized treatment in the US healthcare system. South Eastern European Journal of Public Health, 24, 2218-2229.
15. Akiri, C. K., Jayabalan, K., Lopes, J., Kareem, S. A., & Tabbassum, A. (2025, March). Generative AI for real-time cloud security: Advanced anomaly detection using GPT models. In 2025 IEEE Conference on Computer Applications (ICCA) (pp. 1-6). IEEE.
16. 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.
17. 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.
18. Chevva, P. (2025, June). Balancing Accuracy and Efficiency in Distributed Machine Learning Systems: A Framework for Policy and Risk Management. In International Conference on Advances and Applications in Artificial Intelligence (ICAAAI 2025) (pp. 702-712). Atlantis Press.
19. Koganti, H. (2023). Architecting high-availability Java microservices for financial applications on AWS. International Journal of Science, Research and Technology, 6(3), 9934-9945.
20. Chandra Shekhar, P. (2021). Driving agile excellence in insurance development through shift-left testing. International Journal for Multidisciplinary Research, 3(6), 1-18.
21. Rajula, A. (2024). Drift-aligned personalization for privacy-preserving edge health monitoring. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(5), 8895–8906.
22. Pasumarthi, H. (2024). Engineering Large-Scale WMS Integrations: A Practical Guide to Implementing Blue Yonder with IBM ACE, Datapower, MQ, and SAP. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 7(2), 10008-10016.
23. Polamarasetty, V. K., Reddem, M. R., Ravi, D., & Madala, M. S. (2018). Attendance system based on face recognition. International Research Journal of Engineering and Technology, 5(4).
24. Soundappan, S. J. (2023). Designing Intelligent Enterprise Platforms Using Machine Learning Driven API Engineering and Cloud Native Security. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(4), 9074-9081.
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29. Awopejo, T. E., Adigun, P. O., & Oyekanmi, T. T. (2023). Emerging applications of artificial intelligence and machine learning in modern earthquake science. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(1), 8142–8154.
30. Bandaru, P. K. (2023). Validation methodologies for over-the-air software updates in modern automotive platforms. International Journal of Computer Technology and Electronics Communication (IJCTEC), 6(6), 8159–8165.
31. Bhagwat, V. B. (2024). A simplified transition from EBS Payroll to Cloud Payroll: Benefits and Drawbacks. Journal of Computational Analysis and Applications, 33(6).
32. Hoque, M. J., Hasan, M. M., Khatun, M. M., Akter, F., & Mohammad, A. R. (2021). Impact of COVID-19 on Consumer Buying Behavior During COVID-19 Pandemic Using Data Analytics. Journal of Business and Management Studies, 3(2), 296-307.
33. Bellundagi, M. (2022). Performance Optimization Techniques for Enterprise Java Applications Using Middleware and Messaging Systems. International Journal of Computer Technology and Electronics Communication, 5(3), 5158-5168.
34. Vani, M., & Dadlani, D. (2023). Smart home – “Aashraya” IoT automation system. International Journal of Computer Technology and Electronics Communication, 6(1), 6393–6403.
35. Karnam, V. S. (2024). Adaptive and federated test automation using AI in distributed multi-cloud and hybrid infrastructure environments. International Journal of Future Innovative Science and Technology (IJFIST), 7(4), 111–121.
36. Macha, Y. (2023). Cloud-Based CRM for Healthcare Data Management: A Review of HIPAA Compliance and Validation Rules. TIJER-Int. Res. J, 10(11), 118-123.
37. Gujarathi, M. (2023). Zero-Downtime Modernization of Enterprise Platforms: Migration Patterns and Operational Considerations. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(3), 8770-8774.
38. 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.
39. Abd-Rouf, M. S. K., Adigun, P. O., Alalade, E. O., Oyekanmi, T. T., Faniyi, A. J., Oladapo, B., Awopejo, T. E., Adegoke, O. S., Jamiu, A., Michael, O. B., Obisesan, A., Ajala, S., Adekanye, M. A., Yambali, P. M., & Abd-Rouf, A. B. (2024). From molecular profiling to predictive algorithms: A conceptual machine-learning framework for mechanism-informed therapy selection in multidrug-resistant cancer. International Journal of Science, Research and Technology (IJSRAT), 7(3), 12085–12101.
40. Chandra, G., Redd, P., & Kadali, R. S. (2022). Lightweight Linux–Powered Edge Systems: Facilitating Secure and Efficient Container Workloads at the Edge. J. Electrical Systems, 18(4), 151-161.
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