Securing Enterprise Data Pipelines through Generative AI Threat Detection and Cloud-Native Application Protection
DOI:
https://doi.org/10.15662/IJEETR.2025.0706051Keywords:
Generative AI, enterprise data security, data pipelines, threat detection, cloud-native security, application protection, cybersecurity, artificial intelligence, anomaly detection, cloud computing, DevSecOps, security automationAbstract
Enterprise data pipelines have become critical infrastructure for modern organizations because they continuously transfer, transform, and process information across databases, applications, cloud platforms, analytical systems, and artificial intelligence environments. As enterprises increasingly adopt cloud-native architectures, application programming interfaces, microservices, containers, serverless computing, and automated data workflows, the complexity and exposure of data pipelines have expanded considerably. Conventional cybersecurity mechanisms based primarily on predefined rules and known attack signatures may be insufficient for detecting sophisticated, rapidly changing, and previously unseen threats. Generative artificial intelligence (GenAI) offers new opportunities for strengthening enterprise security by analyzing large volumes of security events, identifying abnormal behavioral patterns, generating contextual threat intelligence, and assisting security teams in investigating incidents. When integrated with cloud-native application protection mechanisms, GenAI can contribute to continuous monitoring and adaptive threat detection across distributed environments. However, GenAI itself introduces security challenges, including model manipulation, prompt injection, sensitive-data exposure, hallucination, adversarial attacks, and unauthorized automated actions. This study examines how GenAI-based threat detection can be integrated with cloud-native application protection to secure enterprise data pipelines. It focuses on the relationship among AI-enabled detection, cloud infrastructure security, data governance, application protection, automated response, and human oversight. The proposed research methodology combines quantitative and qualitative approaches to evaluate detection effectiveness, operational performance, security readiness, and organizational challenges associated with implementing GenAI-driven cybersecurity mechanisms in enterprise cloud environments
References
1. Kaur, R., Gabrijelčič, D., & Klobučar, T. (2023). Artificial intelligence for cybersecurity: Literature review and future research directions. Information Fusion, 97, 101804. https://doi.org/10.1016/j.inffus.2023.101804
2. Karakondu, M., Jambagi, G., & Tatavarthi, S. (2025). Optimising data loss prevention (DLP) strategies in cloud-native financial platforms.
3. Mathew, A. (2021). Artificial intelligence and cognitive computing for 6G communications & networks. International Journal of Computer Science and Mobile Computing, 10(3), 26-31.
4. 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
5. Reddy, K. V. (2022). Power-aware verification closure using AI-enhanced UVM and dynamic voltage and frequency scaling models. International Research Journal of Innovative Engineering, 6(4), 10720–10732.
6. Gudipuri, N. (2024). Intent-Aware Enterprise Architecture Using Persistent AI Agents for Dynamic FinTech Service Composition. The Eastasouth Journal of Information System and Computer Science, 2(01), 151-161.
7. Venkatasalam, K., Rajendran, P., & Thangavel, M. (2019). Improving the accuracy of feature selection in big data mining using accelerated flower pollination (AFP) algorithm. Journal of medical systems, 43(4), 96.
8. Chundi, V. R. K. (2025). AI-Powered Sustainability Integration: Transforming Retail and Manufacturing Through Enterprise Resource Planning Solutions. Journal of Computer Science and Technology Studies, 7(5), 881-887.
9. Soundappan, S. J. (2021). Integrated Artificial Intelligence Framework for Enterprise Cloud Modernization and Intelligent Threat Management Systems. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 4(4), 5274-5284.
10. Puram, S. (2025). Decoding Native Application Architecture for Embedded Mobility Platforms. Journal Of Engineering And Computer Sciences, 4(8), 42-49.
11. Ahuja, D. (2023). Multi-Cloud Management Strategies: Tools, Challenges, and Implementation Methods. Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol, 9(10), 493-506.
12. Raja, G. V., & Mali, R. K. (2021). Federated learning frameworks for privacy-preserving artificial intelligence applications. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 4(3), 4946-4950.
13. Yadavalli, V. R. (2024). Hypercare signal fusion for early instability detection in SAP S/4HANA and EWM go-lives. International Research Journal of Innovative Engineering, 8(5), 15423–15437.
14. Punithavathi, R., Selvi, R. T., Latha, R., Kadiravan, G., Srikanth, V., & Shukla, N. K. (2022). Robust node localization with intrusion detection for wireless sensor networks. Intelligent Automation and Soft Computing, 33(1), 143-156.
15. Bandari, U., & Subramani, S. K. (2025). AI-ready data products: Extending data contracts and semantic layers for LLM and agent consumption. International Journal of Computer Technology and Electronics Communication, 8(4), 11214–11224.
16. Polamarasetty, V. K. (2022). Enterprise SAP application modernization for post-acquisition business transformation. International Journal of Science, Research and Technology, 5(3), 7777-7783.
17. Karrothu, A. (2024). A cloud-native architecture for scalable real-time security telemetry ingestion and threat signal processing. Journal of Computational Analysis and Applications (JoCAAA), 33(8), 8137–8154. https://eudoxuspress.com/index.php/pub/article/view/5220
18. Sugumar, R. (2021). Generative AI Pipelines for Safety Validation of Autonomous Driving Models. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 4(5), 5466-5469.
19. Chinnam, N. B. (2022). Engineering real-time supply chain visibility and ETA intelligence platforms for large-scale retail distribution networks. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(6), 7918–7922.
20. Mishra, D., Behera, D., Challa, A., & Vemu, C. (2021). PDCloudEX software defined network. In Integrated emerging methods of artificial intelligence & cloud computing (pp. 66–70). Springer International Publishing.
21. Vimal Raja, G. (2022). Leveraging machine learning for real-time short-term snowfall forecasting using multisource atmospheric and terrain data integration. International Journal of Multidisciplinary Research in Science, Engineering and Technology, 5(8), 1336-1339.
22. Selvarajan, K. (2025). AI-driven analytics architectures for enterprise security and operational intelligence. International Journal of Advanced Engineering Science and Information Technology, 8(2), 16093–16102.
23. Neela, S. (2025). Middleware-Driven Hybrid Integration: Bridging the Gap in Modern Enterprise Architecture. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 11(1), 3527-3536.
24. 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.
25. Tarigoppula, S. (2023). Cloud-native Oracle database modernization using intelligent migration frameworks and automated recovery architectures. International Journal of Science, Research and Technology, 6(6), 11132–11144.
26. Srikanth, V., Aswini, P., Chandrashekar, R., Sirisha, N., Kumar, M., & Adnan, K. (2024, November). Machine learning-based analogue circuit design for stage categorization and evolutionary optimization. In 2024 Second International Conference Computational and Characterization Techniques in Engineering & Sciences (IC3TES) (pp. 1-6). IEEE.
27. Gajjela, H., & Kari, M. (2024). Secure Multi-Tenant AI Architecture Enhances Enterprise AI Adoption Through Tenant Trust Using PLS-SEM in India’s Salesforce Cloud Industry. International Journal of Engineering Science & Humanities, 14(2), 253-271.
28. 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.
29. Ramasamy, M. (2025). Transforming industries: The impact of AI-driven network engineering and cloud infrastructure. International Journal of Research and Applied Innovations, 8(3), 13114–13118.
30. Bitragunta, S. L. V. (2025). Optimizing hybrid energy storage in EVs using AI enhanced fuzzy logic control systems. International Journal of Science, Research and Technology, 8(1), 13525–13533.
31. Basavala, S. R. (2021). Threat modeling and security requirements specifications for enterprise application development. International Research Journal of Innovative Engineering, 5(1), 7498–7508.
32. Mudunuri, L. N. R., Aragani, V. M., & Maroju, P. K. (2024, December). Development of an AI-Powered Garbage Detection System for Environmental Sustainability Via YOLOv5. In International Conference on Information and Communication Technology for Competitive Strategies (pp. 317-327). Singapore: Springer Nature Singapore.
33. Shaik, N. (2025). Dynamic healthcare intelligence: Integrating AI predictive analytics with Kubernetes scaling for enhanced patient outcomes. International Journal of Engineering & Extended Technologies Research, 7(2), 9715–9720.
34. 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.
35. 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.
36. Yang, X., Ma, M., & Tang, X. (2024). An efficient privacy-preserving and verifiable scheme for federated learning. Future Generation Computer Systems, 160, 238–250. https://doi.org/10.1016/j.future.2024.06.002
