Intelligent Cyber Defense and Secure API Lifecycle Management Using Generative AI in Cloud Environments

Article Sidebar

PDF
Published: 2025-12-20
DOI: https://doi.org/10.15662/IJRPETM.2025.0806046
Keywords:
Generative Artificial Intelligence, Cyber Defense, API Security, API Lifecycle Management, Cloud Computing, Machine Learning, Cloud Security, Secure APIs, Threat Detection, Automated Security, Zero Trust, DevSecOps

Main Article Content

Rajakumar Sampathkumar
Principal Technical Account Manager, Amazon Web Services (AWS), New York, United States

Abstract

The rapid adoption of cloud computing, microservices, serverless architectures, and application programming interfaces (APIs) has created highly distributed enterprise environments with increasingly complex cybersecurity requirements. APIs have become essential for communication among applications, cloud services, databases, mobile platforms, and third-party systems, while simultaneously becoming attractive targets for attackers. Traditional security mechanisms based on static rules, signatures, and manual analysis are often unable to respond effectively to rapidly evolving threats and continuously changing API ecosystems. This research proposes an intelligent cyber defense and secure API lifecycle management framework using Generative Artificial Intelligence (GenAI) in cloud environments. The proposed framework integrates generative AI with API discovery, secure API design, automated security testing, vulnerability analysis, behavioral monitoring, threat intelligence, identity and access management, anomaly detection, and automated incident response. GenAI is used to assist security teams in generating security policies, analyzing API specifications, identifying vulnerabilities, producing test cases, correlating security events, and recommending remediation strategies. The framework covers the complete API lifecycle, including planning, design, development, testing, deployment, monitoring, versioning, and retirement. A design-science research methodology is adopted to develop and experimentally evaluate the proposed architecture using simulated cloud workloads and API attack scenarios. The expected outcome is improved threat detection, reduced security response time, stronger API governance, and more efficient security operations while maintaining scalability and reliability.

Article Details

Issue

Vol. 8 No. 6 (2025): International Journal of Research Publications in Engineering, Technology and Management

Section

Articles

How to Cite

Intelligent Cyber Defense and Secure API Lifecycle Management Using Generative AI in Cloud Environments. (2025). International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 8(6), 13430-13442. https://doi.org/10.15662/IJRPETM.2025.0806046

References

1. Kumar, S., Dwivedi, M., Kumar, M., & Gill, S. S. (2024). A comprehensive review of vulnerabilities and AI-enabled defense against DDoS attacks for securing cloud services. Computer Science Review, 53, 100661. https://doi.org/10.1016/j.cosrev.2024.100661

2. Kumar, A., Wadhwa, M., Kalla, D., Konduru, S. C., Nandawat, C., & Sharma, M. (2025, October). Benchmarking the Trade-Offs in Object Detection: Accuracy, Speed, and Energy Efficiency. In International Conference on Artificial Intelligence and Networking (pp. 410-422). Cham: Springer Nature Switzerland.

3. Gummadi, V. P. K. (2021). Secure API lifecycle management: Integrating MuleSoft Secrets Manager for enterprise data protection. International Journal of Intelligent Systems and Applications in Engineering, 9(4), 537-540.

4. Soundappan, S. J. (2023). Machine Learning Based Predictive Models for Secure Financial Transactions and Cyber Threat Detection. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(1), 5966-5975.

5. Narra, S. L. (2025). Human-AI Collaboration in Identity Security: When Should AI Decide?. Journal of Computer Science and Technology Studies, 7(7), 191-197.

6. Challa, R. (2024). High-Availability HPC Cluster Design for Mission-Critical Public Infrastructure: Lessons from Energy Grid and Government. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(6), 9305-9309.

7. 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.

8. Karan Nisar. (2025). Why enterprise agent deployments fail: A field taxonomy. International Journal of Computer Technology and Electronics Communication (IJCTEC), 8(5), 11592–11605.

9. 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.

10. Anbazhagan, R. S. K. (2016). A Proficient Two Level Security Contrivances for Storing Data in Cloud.

11. Tyagi, N. (2025). The Compliance Horizon: Anticipating Regulatory Change in Financial Services and Artificial Intelligence. International Journal of Research and Applied Innovations, 8(2), 11227-11232.

12. Chandra Shekhar, P. (2021). Driving agile excellence in insurance development through shift-left testing. International Journal for Multidisciplinary Research, 3(6), 1-18.

13. Rella, B. P. R., Chakraborty, A., Shah, S. B., Ghosh, H., Gautam, S., Dhirwani, B. A., ... & Mutyam, N. (2024). AI-engine-based acceleration for high-performance programmable system-on-chip designs. Journal of Computational Analysis and Applications, 32(1).

14. Raja, G. V. (2020). Metadata gets a makeover: The machine learning approach. International Journal of Computer Technology and Electronics Communication, 3(6), 2900-2903.

15. 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.

16. Karan Nisar. (2025). Why enterprise agent deployments fail: A field taxonomy. International Journal of Computer Technology and Electronics Communication (IJCTEC), 8(5), 11592–11605.

17. 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.

18. 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 (IJRPETM), 7(Special Issue 1), 5-12.

19. Juvvadi, R. R. (2017). From record-to-report to insight-to-action: Re-engineering the finance operating model for the cloud ERP era. International Research Journal of Innovative Engineering (IRJIE), 1(2), 371–376.

20. Pokala, H. K. (2025). AIR-DEA: A multi-criterion mathematical model for evaluating artificial intelligence systems. International Journal of Applied Mathematics, 38(8s), 4818–4830.

21. 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.

22. Narapareddy, V. S. R. (2022). Strategies for Integrating Services with External Systems Via Rest & Soap. Universal Library of Engineering Technology, (Issue).

23. Ganapathy, S. K. (2024). Threat Modeling for Federated SSO and MFA Systems: STRIDE-Based Analysis of Attack Vectors. American Academic Journal, 55-73.

24. Bajarang Bhagwat, V. (2023). Optimizing payroll to general ledger reconciliation: Identifying discrepancies and enhancing financial accuracy. Journal of Advance and Future Research, 1(4).

25. 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.

26. Suddala, V. R. A. K. (2024). Machine learning for operational excellence: Real-world applications. International Journal of Future Innovative Science and Technology (IJFIST), 7(6), 13917.

27. Kundurthy, O. H., Ghadiyaram, R., & Vanam, L. (2025, September). Global AI Regulation Review: Comparative Insights from EU, US and APAC. In International Conference on Intelligent Computing and Communication (pp. 422-434). Cham: Springer Nature Switzerland.

28. Alex Mathew. (2023). Threat defense through cyber fusion. International Journal of Computer Science and Mobile Computing, 12(1), 24–27. https://doi.org/10.47760/ijcsmc.2022.v12i01.003

29. Onteddu, A. R., Bandhela, R. R., & Kundavaram, R. R. (2024). Enhancing E-Commerce Product Recommendations through Data Engineering and Machine Learning. Economic Sciences, 20(1), 171-183.

30. 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.

31. Macha, Y., & Pulichikkunnu, S. K. (2024). A Data-Driven Framework for Medical Insurance Cost Prediction Using Efficient AI Approaches. IJRAR, 11(4), 887-893.

32. 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.

33. Kumar, A., Wadhwa, M., Kalla, D., Konduru, S. C., Nandawat, C., & Sharma, M. (2025, October). Benchmarking the Trade-Offs in Object Detection: Accuracy, Speed, and Energy Efficiency. In International Conference on Artificial Intelligence and Networking (pp. 410-422). Cham: Springer Nature Switzerland.

34. Pasumarthi, H. (2024). AI-driven forecasting and optimization in distributed systems: Lessons from retail, lending, and healthcare platforms. International Journal of Research and Applied Innovations, 7(3), 10786-10790.

35. 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.

36. 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.

37. Soundappan, S. J. (2025). Federated Multi-Cloud Data Governance for Secure and Compliant Enterprise Analytics Systems Framework. International Journal of Research and Applied Innovations, 8(2), 11251-11261.

38. 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.

39. Vani, M., & Dadlani, D. (2023). Smart home – “Aashraya” IoT automation system. International Journal of Computer Technology and Electronics Communication, 6(1), 6393–6403.

40. Hoque, M. J., Akter, F., & Mohammad, A. R. (2019). Data-Driven Decision Support System for Restaurant Business Optimization. American Journal of Economics and Business Management, 2(2).

41. Kumar, A., Wadhwa, M., Kalla, D., Konduru, S. C., Nandawat, C., & Sharma, M. (2025, October). Benchmarking the Trade-Offs in Object Detection: Accuracy, Speed, and Energy Efficiency. In International Conference on Artificial Intelligence and Networking (pp. 410-422). Cham: Springer Nature Switzerland.