Cloud Native Machine Learning Framework for Automated Predictive Enterprise Risk Assessment and Security Intelligence

Sander Hoogendoorn; Sander Hoogendoorn

doi:10.15662/IJSRAT.2025.0806025

Cloud Native Machine Learning Framework for Automated Predictive Enterprise Risk Assessment and Security Intelligence

Sander Hoogendoorn

Software Architect & Independent Consultant, Amsterdam, Netherlands

Abstract

The rapid digital transformation of enterprises has generated increasingly complex technology ecosystems involving cloud platforms, microservices, containers, application programming interfaces, distributed databases, remote endpoints, and third-party services. This complexity creates new operational, financial, compliance, and cybersecurity risks that conventional risk-assessment approaches may not identify rapidly enough. This study proposes a cloud-native machine learning framework for automated predictive enterprise risk assessment and security intelligence. The framework integrates heterogeneous enterprise data, including security logs, operational events, user behavior, vulnerability information, financial indicators, compliance records, and threat-intelligence feeds, to develop a unified representation of organizational risk. Machine learning algorithms are employed to identify hidden patterns, detect anomalies, predict emerging risks, and classify security events according to their potential impact. A cloud-native architecture based on containerized services, scalable data pipelines, distributed processing, model-serving components, and continuous monitoring enables the framework to operate across dynamic enterprise environments. The proposed methodology incorporates data preprocessing, feature engineering, predictive model development, risk scoring, explainable artificial intelligence, automated alert prioritization, and continuous model updating. Model performance is evaluated using accuracy, precision, recall, F1-score, area under the ROC curve, false-positive rate, prediction latency, scalability, and resource utilization. The framework aims to transform enterprise risk management from periodic and reactive assessment into continuous, intelligent, predictive, and automated security intelligence capable of supporting timely organizational decision-making

Article Information

Journal	International Journal of Science, Research and Technology
Volume (Issue)	Vol. 8 No. 6 (2025): International Journal of Science, Research and Technology (IJSRAT)
DOI	https://doi.org/10.15662/IJSRAT.2025.0806025
Pages	15512-15523
Published	December 16, 2025
Copyright	All rights reserved
Open Access	This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite	Sander Hoogendoorn (%2025). Cloud Native Machine Learning Framework for Automated Predictive Enterprise Risk Assessment and Security Intelligence. International Journal of Science, Research and Technology , Vol. 8 No. 6 (2025): International Journal of Science, Research and Technology (IJSRAT) , pp. 15512-15523. https://doi.org/10.15662/IJSRAT.2025.0806025

References

1. Himeluzzaman, M., Alam, A., Gazi, M. S., Abdullah, S. M., Chy, M. S. K., Onik, T. A., Nabil, M. A., & Shakil, S. M. (2025). Countering AI-generated disinformation: A novel detection model to safeguard national security. International Journal of Computer Technology and Electronics Communication, 8(4), 11192–11203.
2. Ravichandran, S., & Kandasamy, V. (2025). Optimized Attention Augmented Residual Convolutional Neural Network with Fa-Resnet for Fabric Defect Detection. Journal of Control Engineering and Applied Informatics, 27(4), 3-15.
3. Jain, R. (2019). The hidden tax: A production framework for cloud cost architecture in enterprise data workloads. International Journal of Science, Research and Technology (IJSRAT), 2(6), 2522–2530.
4. Selvarajan, K. (2025). AI-Driven Enterprise Supply Chain Intelligence: A Technical Deep Dive. Journal of Computer Science and Technology Studies, 7(2), 612-617.
5. Himeluzzaman, M., Alam, A., Gazi, M. S., Abdullah, S. M., Chy, M. S. K., Onik, T. A., ... & Shakil, S. M. (2025). Countering AI-Generated Disinformation: A Novel Detection Model to Safeguard National Security. International Journal of Computer Technology and Electronics Communication, 8(4), 11192-11203.
6. Kundavaram, R. R., Onteddu, A. R., & Kishore, K. K. (2024). Smart banking: The impact of artificial intelligence on financial services. Economic Sciences, 20(1), 193–202. https://doi.org/10.69889/ht8j4n02
7. Balaraman, N. K., Hariharan, A., Patel, K., & Sonachalam, A. (2025). Wastewater Industry with Artificial Intelligence. Advances in Water Resources Science, 97.
8. Padmanabham, S. (2023). Building resilient banking platforms using event-driven microseconds and cloud-native architecture. International Journal of Research and Applied Innovations, 6(4), 9284–9290.
9. Patel, K. (2024). Human–machine collaboration in microbiological laboratories: A posthuman perspective on automation, control, and scientific agency. Journal of Posthumanism, 4(3), 2346–2355. https://doi.org/10.63332/joph.v4i3.4186
10. Koganti, H. (2024). The computational complexity of hybrid algorithms: When branch-and-bound meets machine learning heuristics. International Journal of Research and Applied Innovations (IJRAI), 7(4), 11184–11190.
11. Omi, M. S. H., Ara, J., Ali, M. M., Hoque, M. R., Ferdausi, S., Fatema, K., ... & Bijoy, M. H. I. (2025, July). Integrating Deep Neural Networks with Explainable AI for Precise Brain Tumor Detection and Classification. In 2025 International Conference on Quantum Photonics, Artificial Intelligence, and Networking (QPAIN) (pp. 1-6). IEEE.
12. Vemireddy, S. (2023). Building resilient enterprise platforms using event-driven and distributed computing models. International Journal of Research and Applied Innovations (IJRAI), 6(6), 10106–10112.
13. 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.
14. Bandaru, P. K. (2021). Leveraging hardware-in-the-loop simulation for continuous automotive software testing. International Journal of Engineering & Extended Technologies Research (IJEETR), 3(5), 3730–3735.
15. Sugumar, R. (2023). Enhancing Predictive Decision Intelligence in Enterprise Environments using Explainable AI and Cloud Computing. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 6(6), 9592-9602.
16. Polamarasetty, V. K. (2022). Enterprise SAP application modernization for post-acquisition business transformation. International Journal of Science, Research and Technology (IJSRAT), 5(3), 7777–7783. https://www.ijsrat.com/index.php/ijsrat/issue/view/23
17. Seetharaman, K. M. R. (2025, May). Predicting Cryptocurrency Price Movements Using Leveraging Machine Learning Algorithms. In 2025 International Conference on Networks and Cryptology (NETCRYPT) (pp. 1497-1502). IEEE.
18. Narra, R. (2024). A survey on scalable feature engineering techniques for cloud-native machine learning workflows. International Journal of Advanced Research in Science, Communication and Technology, 4(4), 664–677.
19. Mohan, A. (2025). Breaking down attribution modeling in predictive analytics. World Journal of Advanced Engineering Technology and Sciences, 15(02), 2851-2859.
20. Mathew, A. (2021). Artificial intelligence and cognitive computing for 6G communications & networks. International Journal of Computer Science and Mobile Computing, 10(3), 26-31.
21. Chundi, V. R. K. (2025). AI-based Sustainable Vehicle Monitoring System for Existing Internal Combustion Vehicles. London Journal of Research In Computer Science and Technology, 25(3), 1-7.
22. Agarwal, S. (2024). Signal Overload in Cloud-Native Observability Platforms: A Scalable Framework for Telemetry Correlation. International Journal of Research and Applied Innovations, 7(2), 10466-10473.
23. 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.
24. Gujarathi, M. (2024). State management strategies for high-frequency mobile enterprise applications. International Journal of Science, Research and Technology, 7(5), 12869-12877.
25. Nisar, K. (2024). Prompting, retrieval, and fine-tuning: Foundations of enterprise language model adaptation. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(6), 9310-9319.
26. Sasikumar, B., Venkatasalam, K., & Rajendran, P. (2024). Induction motor fault detection and classification using rcnn and surf based machine learning algorithms and infrared thermography. Scientia Iranica.
27. Tarakampet, S., Puvvula, G., Begum, S., Ali, S., Tatavarthi, S., & Bikkavolu, V. (2025). The power of interoperability: Designing custom applications for seamless integration. International Journal of Emerging Information Technology, 1(2), 7–13. https://doi.org/10.5281/zenodo.20371782
28. Vimal Raja, G. (2024). Intelligent data transition in automotive manufacturing systems using machine learning. International Journal of Multidisciplinary and Scientific Emerging Research, 12(2), 515-518.
29. Pasupuleti, N. S., Kapoor, S., Vedula, J., Sati, M. M., Shamilevna, G. S., & Khurmat, E. (2025, July). Implementation of a Deep Learning Model for Real-time Detection of Diabetic Retinopathy in Primary Care Clinics. In 2025 International Conference on Information, Implementation, and Innovation in Technology (I2ITCON) (pp. 1-6). IEEE.
30. Narra, S. L. (2025). Demystifying Zero Trust Architecture: Why It's Not Just a Buzzword. International Journal of Computing and Engineering, 7(10), 1-16.
31. Patel, C. (2024). AI-driven recommendation systems for improving online customer journey. International Journal of Current Engineering and Technology, 14(6), 549–556. https://doi.org/10.14741/ijcet/v.14.6.18
32. Ramasamy, M. (2025). The synergy of human and AI collaboration in modern network management. Journal of Computer Science and Technology Studies, 7(4), 174-180.
33. Mole, M. (2025). Artificial intelligence in public safety: Enhancing prevention and response. Sarcouncil Journal of Engineering and Computer Sciences, 4(7), 211–217. https://doi.org/10.5281/zenodo.15818598
34. Praneeth, P. (2022). Prediction of Cost Overruns in Solar EPC Projects Using Machine Learning Techniques: A Data-Driven Study in India. International Journal of Engineering Science & Humanities, 12(2), 71-85.
35. Kollu, R. K. (2025). Unlocking Sales Cloud: A Guide to Smarter Selling in Salesforce. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 8(5), 12891-12899.
36. Badam, L. R. (2023). Explainable AI framework for real-time financial fraud detection in banking systems. International Journal of Emerging Trends in Engineering and Management Research, 8(2), 13241–13251.
37. Matrouk, K., V, S., Kumar, S., Bhadla, M. K., Sabirov, M., & Saadh, M. J. (2023). Deep Learning–based Dynamic User Alignment in Social Networks. ACM Journal of Data and Information Quality, 15(3), 1-26.
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. Pothuri, M. K. (2025). From legacy systems to scalable cloud platforms: Building modern data pipelines with data engineering strategies, scaling trust, compliance, and performance in public health. International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences, 13(5). https://doi.org/10.37082/IJIRMPS.v13.i5.232724
40. Hashmi, H., & Srikanth, V. (2023, November). Combining Neural Networks to Recognize Offline Digits & Words by Training the Model Using Keras. In 2023 3rd International Conference on Advancement in Electronics & Communication Engineering (AECE) (pp. 694-697). IEEE.