Scalable Governance Frameworks for Enterprise Architecture Supporting Oracle Cloud DBAs Serverless Data Pipelines and Proactive API Threat Mitigation
DOI:
https://doi.org/10.15662/IJEETR.2025.0706033Keywords:
Enterprise architecture, scalable governance frameworks, Oracle Cloud DBAs, serverless data pipelines, API threat mitigation, cloud-native systems, policy automation, role-based access control, proactive security, event-driven architecture, real-time monitoring, compliance, operational resilience, data security, auditabilityAbstract
Scalable governance frameworks are essential for modern enterprise architectures that support Oracle Cloud DBAs, serverless data pipelines, and proactive API threat mitigation. By combining cloud-native principles, role-based access control, automated policy enforcement, and continuous monitoring, organizations can ensure secure, compliant, and resilient operations across complex IT ecosystems. Serverless data pipelines enable efficient, event-driven data processing and transformation, while integrated analytics provide visibility into performance, compliance, and operational risks.
Proactive API threat mitigation, including automated anomaly detection, rate limiting, and security orchestration, protects sensitive enterprise data and maintains service continuity. The framework emphasizes standardization, automation, and auditability, enabling IT teams to scale governance practices without hindering innovation. Together, these strategies provide a robust foundation for secure, adaptive, and scalable enterprise systems in dynamic cloud environments.
References
1. Bathina, S. (2025). Atomic omnichannel: Reinventing retail personalization with generative-AI content factories. ISCSITR–International Journal of Computer Science and Engineering (ISCSITR-IJCSE), 6(4), 46–62.
2. Surisetty, L. S. (2023). Proactive threat mitigation in API ecosystems through AI-powered anomaly detection. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 6(1), 7633–7642.
3. Genne, S. (2024). Designing composable enterprise web architecture using headless CMS. International Journal of Future Innovative Science and Technology (IJFIST), 7(6), 13865–13875.
4. Devi, C., Siripuram, N. K., & Selvaraj, A. (2025). Serverless ETL orchestration with Apache Airflow and AWS Step Functions: A comparative study. European Journal of Quantum Computing and Intelligent Agents, 9, 15–52.
5. Rajasekharan, R. (2024). The evolving role of Oracle Cloud DBAs in the AI era. International Journal of Computer Technology and Electronics Communication (IJCTEC), 7(6), 9866–9879.
6. Kusumba, S. (2025). Empowering Federal Efficiency: Building an Integrated Maintenance Management System (Imms) Data Warehouse for Holistic Financial And Operational Intelligence. Journal Of Multidisciplinary, 5(7), 377-384.
7. Kamadi, S. (2024). Multi-cloud ETL automation and rollback strategies: An empirical study for distributed workload orchestration system. International Journal for Multidisciplinary Research, 6(2).
8. Mogili, V. B. Transforming Enterprises with Microsoft Technologies: Real-World Case Studies, Success Stories, and Insights from Failures. https://www.researchgate.net/profile/Ezekiel-Nyong/publication/400071341_Transforming_Enterprises_with_Microsoft_Technologies_Real-World_Case_Studies_Success_Stories_and_Insights_from_Failures/links/6976c9fbac604d40d0e5734e/Transforming-Enterprises-with-Microsoft-Technologies-Real-World-Case-Studies-Success-Stories-and-Insights-from-Failures.pdf.
9. Anumula, S. R. (2023). Enterprise architecture for real-time intelligence in distributed environments. International Journal of Computer Technology and Electronics Communication (IJCTEC), 6(4), 7301–7312.
10. Gurajapu, A., & Garimella, V. (2025). Edge-to-cloud workflows for low-latency telecom services: Optimizing offload decisions. International Journal of Research and Applied Innovations (IJRAI), 8(4), 12638–12641.
11. Panchakarla, S. K. (2025). Context-aware rule engines for pricing and claims processing in healthcare platforms. International Journal of Computer Technology and Electronics Communication, 8(4), 11087–11091.
12. Thakran, V. (2025, October). Intelligent modelling of pressure loss estimation in emulsion pipelines using machine learning techniques. In 2025 International Conference on Electrical, Electronics, and Computer Science with Advance Power Technologies – A Future Trends (ICE2CPT) (pp. 1–6). IEEE.
13. Gangina, P. (2025). The role of cloud-native architecture in enabling sustainable digital infrastructure. International Journal of Research and Applied Innovations (IJRAI), 8(5), 13046–13051.
14. Chennamsetty, C. S. (2023). Neural pipeline orchestration: Deep learning approaches to software development bottleneck elimination. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 6(4), 8674–8680.
15. Ramidi, M. (2024). Scalable mobile automation testing frameworks for government digital service platforms. International Journal of Advanced Engineering Science and Information Technology (IJAESIT), 7(4), 14455–14465.
16. Musunuru, M. V., Devi, C., & Sethuraman, S. (2025). Optimizing Hot Standby Redundancy Using AI for Network Traffic Balancing and Failover Management. Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online), 4(3), 14-26.
17. Alam, M. K., Mahmud, M. A., & Islam, M. S. (2024). The AI-powered treasury: A data-driven approach to managing America’s fiscal future. Journal of Computer Science and Technology Studies, 6(2), 236–256.
18. Gaddapuri, N. S. (2025). Scalable cloud-native governance systems for financial compliance and risk management. Power System Protection and Control, 53(2), 319–333.
19. Chivukula, V. (2024). The role of adstock and saturation curves in marketing mix models: Implications for accuracy and decision-making. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 7(2), 10002–10007.
20. M. I. Hossain, T. Akter, M. Yasin, and M. B. Rahman, "Zero‑ETL Analytics: Transforming operational data into actionable insights," 2025.
21. Vimal Raja, G. (2025). Context-aware demand forecasting in grocery retail using generative AI: A multivariate approach incorporating weather, local events, and consumer behaviour. International Journal of Innovative Research in Science Engineering and Technology (IJIRSET), 14(1), 743–746.





