Enhancing Enterprise Cyber Resilience through Intelligent API Security and Cloud Native Infrastructure Optimization

Authors

  • Dr.B.Murugeshwari Professor & HOD, Department of Computer Science and Engineering, Velammal Engineering College, Chennai, India Author

DOI:

https://doi.org/10.15662/IJEETR.2021.0302006

Keywords:

Enterprise cyber resilience, Intelligent API security, Cloud-native infrastructure, DevSecOps, Zero Trust Architecture, Artificial Intelligence, Machine Learning, API protection, Kubernetes security, Cloud security, Threat detection, Infrastructure optimization, Security automation, Digital transformation, Cybersecurity resilience

Abstract

Enterprise digital transformation has accelerated the adoption of cloud-native architectures, microservices, and Application Programming Interfaces (APIs), enabling organizations to achieve greater scalability, flexibility, and operational efficiency. However, this transformation has also expanded the cyber threat landscape, exposing enterprises to increasingly sophisticated attacks targeting APIs, containers, Kubernetes environments, and cloud infrastructures. Traditional perimeter-based security approaches are insufficient in addressing these evolving risks because cloud-native ecosystems are dynamic, distributed, and highly interconnected. This study examines how intelligent API security combined with cloud-native infrastructure optimization enhances enterprise cyber resilience by improving threat detection, reducing attack surfaces, and ensuring continuous business operations. The research explores the integration of artificial intelligence, machine learning, behavioral analytics, zero trust architecture, automated security monitoring, and infrastructure optimization strategies to strengthen organizational cybersecurity capabilities. It further investigates the role of DevSecOps practices, security automation, and continuous compliance in protecting modern enterprise environments while maintaining operational performance. Using a qualitative review of recent scholarly literature and industry best practices, the study identifies critical success factors for implementing resilient cloud-native security frameworks. The findings indicate that intelligent API protection and optimized cloud-native infrastructure significantly improve enterprise resilience by enabling proactive threat prevention, rapid incident response, regulatory compliance, and sustainable digital innovation in increasingly complex technological ecosystems

References

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

2. Mahendran, M., Sugumar, R., Anbazhagan, K., & Natarajan, R. (2012). An efficient algorithm for privacy preserving data mining using heuristic approach. International Journal of Advanced Research in Computer and Communication Engineering, 1(9), 737-744.

3. Vayyasi, N. K. (2020). Decoding token volatility patterns with generative models deployed on cloud-native Java environments. International Journal of Engineering & Extended Technologies Research (IJEETR), 2(4), 1552-1565.

4. 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.

5. Veershetty, G. (2019). From Legacy Back Office to Intelligent Utility Enterprise a Practitioner Case Study of SAP Cloud Transformation and Utility IT Landscape Modernization. American International Journal of Computer Science and Technology, 1(1), 23-27.

6. Mathew, A. R. (2019). Airport cyber security and cyber resilience controls. arXiv preprint arXiv:1908.09894.

7. Umasankar, P., & Saravanan, K. (2016). Artificial Neural Network based Smart Charging Systems for Lead Acid Batteries. Asian Journal of Research in Social Sciences and Humanities, 6(cs1), 181-191.

8. Yamsani, N. (2018). Operationalizing regulatory governance through enterprise master data design: A practical examination of OFAC, KYC, and GDPR controls at Elavon. International Journal of Scientific Research & Engineering Trends, 4(6). https://doi.org/10.5281/zenodo.18196005

9. Konakalla, K. (2020). Automated commission calculation and sales quota management in Salesforce: A code-driven approach for sales efficiency. International Journal, 7, 125-127.

10. Alex Roney Mathew. (2019). Malware analysis of API calls using FPGA hardware level security. International Journal for Research in Applied Science & Engineering Technology, 7(3), 898–900.

11. Sugumar, R., & Murugeshwari, B. (2016). An Efficient MChord based Authentication for Vehicular Ad-Hoc Networks.

12. Seetala, S. R. (2016). Strategic architecture patterns and design principles for enterprise-grade data integration in large-scale, multi-source and distributed platform environments. European Journal of Advances in Engineering and Technology, 3(8), 125-135.

13. Watham, S. D., & Vimal, V. R. (2013). Design and Implementation of Data Sanitization Technique For Effective Filtering With Enhanced Medical Support System in Cloud Architecture Diagram. International Journal of Emerging Technology and Advanced Engineering, 3(12), 471-473.

14. Parasa, M. (2020). Designing future ready compensation systems with data driven fairness and performance alignment in SAP SuccessFactors. International Journal of Scientific Research and Engineering Trends, 6(4), 10-5281.

15. Gummadi, V. P. K. (2020). API design and implementation: RAML and OpenAPI specification. Journal of Electrical Systems, 16(4).

16. Rajendran, S. (2023). Privacy preserving data mining using hiding maximum utility item first algorithm by means of grey wolf optimisation algorithm.

17. Juvvadi, R. R. (2019). Smart contracts in supply chain finance: Automating accounts payable and the three-way match. Journal of Information Systems Engineering and Management, 4(1), 1–12.

18. Vankayala, S. C. (2018). Engineering elastic performance testing frameworks for cloud native applications: A scalable design perspective. Journal of Scientific and Engineering Research, 5(8), 301–315. https://doi.org/10.5281/zenodo.17839723

19. Anand, L., & Neelanarayanan, V. (2020, October). Enchanced multiclass intrusion detection using supervised learning methods. In AIP conference proceedings (Vol. 2282, No. 1, p. 020044). AIP Publishing LLC.

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

21. Sojol, J. I., Alam, M. S., Hossain, N., & Motahar, T. (2019, February). Smart School Bus: Ensuring Safety On The Road For School Going Children. In 2019 21st International Conference on Advanced Communication Technology (ICACT) (pp. 734-739). IEEE.

22. 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.

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Published

2021-04-20

How to Cite

Enhancing Enterprise Cyber Resilience through Intelligent API Security and Cloud Native Infrastructure Optimization. (2021). International Journal of Engineering & Extended Technologies Research (IJEETR), 3(2), 2796-2805. https://doi.org/10.15662/IJEETR.2021.0302006