A Cloud Security Hyper-Automation Model for Financial Markets and ERP Healthcare AI-Driven Anomaly Detection, Multivariate Risk Inference, and Continuous DevSecOps Assurance

Article Sidebar

PDF
Published: 2022-09-05
DOI: https://doi.org/10.15662/IJRPETM.2022.0505004
Keywords:
Cloud Security, Hyper-Automation, AI-Driven Anomaly Detection, Multivariate Risk Inference, DevSecOps, Financial Markets, Healthcare ERP, Continuous Monitoring, Threat Detection, Risk Mitigation, Regulatory Compliance, Cloud-Native Architecture

Main Article Content

Jonas Kristoffer Björnsson Ekström
Cloud Security Engineer, Sweden

Abstract

The increasing digitization of financial markets and healthcare ERP systems exposes organizations to complex security threats and operational risks. This study proposes a Cloud Security Hyper-Automation Model designed to enhance threat detection, risk assessment, and compliance in real-time. Leveraging AI-driven anomaly detection, the model identifies deviations in transactional and operational data, enabling proactive intervention. Multivariate risk inference techniques are employed to quantify and prioritize threats across diverse financial and healthcare ERP datasets, ensuring a comprehensive risk management framework. Continuous DevSecOps assurance integrates security into the software development lifecycle, automating monitoring, vulnerability assessment, and remediation. The framework is scalable, cloud-native, and capable of handling high-velocity data streams while maintaining regulatory compliance. Experimental results demonstrate significant improvements in threat detection accuracy, operational resilience, and risk mitigation compared to traditional approaches. By combining AI, hyper-automation, and DevSecOps practices, this model provides organizations in finance and healthcare with a robust, adaptive, and continuous security strategy, minimizing financial losses and ensuring data integrity.

Article Details

Issue

Vol. 5 No. 5 (2022): The International Journal of Research Publications in Engineering, Technology and Management

Section

Articles

How to Cite

A Cloud Security Hyper-Automation Model for Financial Markets and ERP Healthcare AI-Driven Anomaly Detection, Multivariate Risk Inference, and Continuous DevSecOps Assurance. (2022). International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(5), 7429-7436. https://doi.org/10.15662/IJRPETM.2022.0505004

References

1. Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM Computing Surveys, 41(3), 1–58.

2. 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.

3. Anand, L., & Neelanarayanan, V. (2019). Liver disease classification using deep learning algorithm. BEIESP, 8(12), 5105–5111.

4. Vankayala, S. C. (2017). Embedding Quality Intelligence in API-First Architectures: Assurance Frameworks for Real-Time Financial Transactions. Journal of Scientific and Engineering Research, 4(6), 227-241.

5. Adari, V. K. (2021). Building trust in AI-first banking: Ethical models, explainability, and responsible governance. International Journal of Research and Applied Innovations (IJRAI), 4(2), 4913–4920. https://doi.org/10.15662/IJRAI.2021.0402004

6. 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.

7. Mohile, A. (2021). Performance Optimization in Global Content Delivery Networks using Intelligent Caching and Routing Algorithms. International Journal of Research and Applied Innovations, 4(2), 4904-4912.

8. Ravipudi, S., Thangavelu, K., & Ramalingam, S. (2021). Automating Enterprise Security: Integrating DevSecOps into CI/CD Pipelines. American Journal of Data Science and Artificial Intelligence Innovations, 1, 31-68.

9. Singh, H. (2020). Evaluating AI-enabled fraud detection systems for protecting businesses from financial losses and scams. The Research Journal (TRJ), 6(4).

10. Yamsani, N. (2021). Governance by design: Secure role delegation and approval structures in enterprise master data systems. International Journal of Science, Engineering and Technology, 9(2). https://doi.org/10.5281/zenodo.18296977

11. Amutha, M., & Sugumar, R. (2015). A survey on dynamic data replication system in cloud computing. International Journal of Innovative Research in Science, Engineering and Technology, 4(4), 1454-1467.

12. Zimek, A., Schubert, E., & Kriegel, H.-P. (2012). A survey on unsupervised outlier detection in high-dimensional numerical data. Statistical Analysis and Data Mining, 5(5), 363–387.

13. Humble, J., & Farley, D. (2010). Continuous Delivery: Reliable Software Releases through Build, Test, and Deployment Automation. Addison-Wesley.

14. Prasad, P. K. (2021). Kubernetes everywhere: Operating hybrid and multi-cloud infrastructure at scale. International Journal of Engineering & Extended Technologies Research, 3(4), 3393–3401.

15. Humble, J., Kim, G., Debois, P., & Willis, J. (2016). The DevOps Handbook: How to Create World-Class Agility, Reliability, & Security in Technology Organizations. IT Revolution Press.

16. Subashini, S., & Kavitha, V. (2011). A survey on security issues in service delivery models of cloud computing. Journal of Network and Computer Applications, 34(1), 1–11.

17. Balamuralidhar Sarabu, V. (2020). Scalable data processing patterns for national retail platforms: An enterprise architecture for high-volume transaction systems. International Journal of Computer Technology and Electronics Communication (IJCTEC), 3(3), 1–14.

18. Mather, T., Kumaraswamy, S., & Latif, S. (2009). Cloud Security and Privacy: An Enterprise Perspective on Risks and Compliance. O’Reilly Media.

19. Goodfellow, I., Shlens, J., & Szegedy, C. (2014). Explaining and harnessing adversarial examples. arXiv:1412.6572.

20. Chiranjeevi, K. G., Latha, R., & Kumar, S. S. (2016). Enlarge Storing Concept in an Efficient Handoff Allocation during Travel by Time Based Algorithm. Indian Journal of Science and Technology, 9, 40.

21. Sculley, D., Holt, G., Golovin, D., et al. (2015). Hidden technical debt in machine learning systems. Proceedings of the 28th International Conference on Neural Information Processing Systems (NIPS) — Workshop/Industry track (discussion of maintainability and operational debt in ML systems).

22. Adepu, R. (2021). Modernizing legacy data centers through virtualization and software-defined infrastructure. International Journal of Research and Applied Innovations (IJRAI), 4(4), 17–36.

23. Kapadia, V., Jensen, J., McBride, G., Sundaramoothy, J., Deshmukh, R., Sacheti, P., & Althati, C. (2015). U.S. Patent No. 8,965,820. Washington, DC: U.S. Patent and Trademark Office.

24. Mani, K., Pichaimani, T., & Siripuram, N. K. (2021). RiskPredict360: Leveraging Explainable AI for Comprehensive Risk Management in Insurance and Investment Banking. Newark Journal of Human-Centric AI and Robotics Interaction, 1, 34-70.

25. Parasa, M. (2021). Encryption-aware data integrity and quality controls in SAP SuccessFactors integrations using machine learning and cryptographic hash chains for tamper detection. International Journal of Computer Technology and Electronics Communication, 4(6), 4304–4316. https://doi.org/10.15680/IJCTECE.2021.0406014

26. Vijayaboopathy, V., Kalyanasundaram, P. D., & Surampudi, Y. (2022). Optimizing Cloud Resources through Automated Frameworks: Impact on Large-Scale Technology Projects. Los Angeles Journal of Intelligent Systems and Pattern Recognition, 2, 168-203.

27. Amuda, K. K., Kumbum, P. K., Adari, V. K., Chunduru, V. K., & Gonepally, S. (2021). Performance evaluation of wireless sensor networks using the wireless power management method. Journal of Computer Science Applications and Information Technology, 6(1), 1–9.

28. Namdeo, A. (2022). Graph neural networks for real-time supply chain risk. International Journal of Humanities and Information Technology, 4(1–3), 175–192.

29. Subramanyam, S. P. (2022). Kubernetes-oriented continuous deployment architecture for .NET microservices. International Journal of Future Innovative Science and Technology (IJFIST), 5(3), 8482–8490. https://doi.org/10.15662/IJFIST.2022.0503002

30. Sivaraju, P. S. (2021). 10x Faster Real-World Results from Flash Storage Implementation (Or) Accelerating IO Performance A Comprehensive Guide to Migrating From HDD to Flash Storage. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 4(5), 5575-5587.

31. Navandar, P. (2021). Fortifying cybersecurity in Healthcare ERP systems: unveiling challenges, proposing solutions, and envisioning future perspectives. Int J Sci Res, 10(5), 1322-1325.

32. Arora, Anuj. "Challenges of Integrating Artificial Intelligence in Legacy Systems and Potential Solutions for Seamless Integration." The Research Journal (TRJ), vol. 6, no. 6, Nov.–Dec. 2020, pp. 44–51. ISSN 2454-7301 (Print), 2454-4930 (Online).

33. Joyce, S. (2021). Beyond migration: Designing resilient SAP workloads for the next generation of cloud infrastructure. International Journal of Engineering & Extended Technologies Research (IJEETR), 3(2), 2779–2788. https://doi.org/10.15662/IJEETR.2021.0302004

34. Adepu, G. (2021). AI-enabled digital identity verification framework for government self-service platforms using secure API and cloud integration. International Journal of Research Publications in Engineering, Technology and Management, 4(1), 160–176.

35. Kumar, R., Al-Turjman, F., Anand, L., Kumar, A., Magesh, S., Vengatesan, K., ... & Rajesh, M. (2021). Genomic sequence analysis of lung infections using artificial intelligence technique. Interdisciplinary Sciences: Computational Life Sciences, 13(2), 192-200.

36. Kasireddy, J. R. (2022). From Raw Trades to Audit-Ready Insights Designing Regulator-Grade Market Surveillance Pipelines. International Journal of Engineering & Extended Technologies Research (IJEETR), 4(2), 4609-4616.

37. Nagarajan, G. (2022). Optimizing project resource allocation through a caching-enhanced cloud AI decision support system. International Journal of Computer Technology and Electronics Communication, 5(2), 4812–4820. https://doi.org/10.15680/IJCTECE.2022.0502003

38. Fung, J., & Panyala, V. R. (2020). Automating multi-region scalable CI/CD framework for managing AWS CloudWatch alerts. International Journal of Engineering & Extended Technologies Research, 2(5), 1854–1858.

39. Srikumar, S., & Roth, D. (2011). Modeling source code and configuration artifacts for security analysis. Proceedings — relevant conference paper exploring code/configuration analysis in automated pipelines.