AI-Driven Vulnerability Prioritization and DevSecOps Automation Across SAP Cloud-Native Enterprise Platforms
DOI:
https://doi.org/10.15680/IJCTECE.2024.0706036Keywords:
Artificial Intelligence, Vulnerability Prioritization, DevSecOps Automation, SAP Cloud-Native Platforms, Machine Learning, Continuous Security Monitoring, Vulnerability Management, Risk-Based Remediation, Kubernetes Security, Enterprise Cloud SecurityAbstract
The increasing adoption of SAP cloud-native enterprise platforms has intensified the need for intelligent vulnerability management and automated security operations throughout the software development lifecycle. Traditional vulnerability assessment approaches frequently depend on static severity scores, manual investigations, and disconnected security tools, which may delay remediation and overlook business-critical risks. This research proposes an artificial intelligence (AI)-driven framework for vulnerability prioritization and DevSecOps automation across SAP cloud-native enterprise environments. The proposed framework integrates machine learning-based risk classification, contextual vulnerability assessment, continuous security monitoring, and automated remediation workflows within continuous integration and continuous delivery pipelines. Vulnerabilities are evaluated using multiple parameters, including exploitability, asset criticality, exposure level, dependency relationships, threat intelligence, and potential business impact. The framework supports integration with SAP S/4HANA, SAP Business Technology Platform, containerized workloads, Kubernetes environments, and cloud infrastructure services. Automated security gates, policy enforcement, and feedback mechanisms are incorporated to improve vulnerability detection and remediation efficiency. The methodology evaluates the framework using detection accuracy, prioritization precision, remediation time, pipeline execution overhead, and compliance coverage. Expected outcomes include improved identification of high-risk vulnerabilities, reduced manual intervention, enhanced security consistency, and stronger operational resilience. The proposed approach establishes a foundation for risk-aware, scalable, and adaptive security automation, enabling enterprise organizations to strengthen SAP application protection while maintaining development agility and supporting continuous compliance across distributed cloud-native environments.References
1. Khan, T., Tian, W., Zhou, G., & Buyya, R. (2022). Machine learning (ML)-centric resource management in cloud computing: A review and future directions. Journal of Network and Computer Applications, 204, Article 103405. https://doi.org/10.1016/j.jnca.2022.103405
2. Soundappan, S. J. (2022). Distributed Cloud Architecture for Intelligent Performance Optimization and Proactive Real-Time Threat Detection. International Research Journal of Innovative Engineering, 6(6), 11426–11432.
3. Kundavaram, R. M. R. (2022). Cloud-based data analysis identifying key financial influencers at scale. European Economic Letters (EEL), 12(2), 234–241.
4. Polamarasetty, V. K. (2021). Modernizing SAP sales and distribution systems through ABAP-based enterprise solutions. International Journal of Research and Applied Innovations, 4(2), 4925–4930.
5. 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.
6. Ramasamy, M. (2023). Cloud-native control plane design for modern network automation systems. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(4), 9082–9091.
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. Puram, S. (2024). AI-Enabled Software-Defined Vehicles: An Intelligent Cloud-Edge Architecture for Adaptive, Secure, and Resilient Automotive Software Systems. The USA Journals TAJAS, 6(03), 23–33.
9. Mathew, A. R. (2019). Cyber-infrastructure connections and smart gird security. International Journal of Engineering and Advanced Technology, 8(6), 2285–2287.
10. Basavala, S. R. (2024). Automated vulnerability intelligence and risk prioritization for secure CI/CD pipeline engineering. International Journal of Future Innovative Science and Technology (IJFIST), 7(1), 12057–12067.
11. 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.
12. Sahu, S. (2024). Digital Governance Framework for Salesforce Data Cloud in Healthcare Insurance Platforms. International Journal of Innovations in Science, Engineering And Management, 120–128.
13. Chinthamani, R. (2023). Engineering resilient microservices for real-time retail order and inventory synchronization. International Journal of Engineering & Extended Technologies Research, 5(6), 7774–7779.
14. Gopinathan, V. R. (2022). Advanced Predictive Decision Intelligence Using Machine Learning for Strategic Enterprise Management Platforms. International Journal of Research and Applied Innovations, 5(6), 8162–8169.
15. Kollu, R. K. (2024). Architecting intelligent sales cloud solutions: A unified framework for scalable Salesforce implementations. International Journal of Research and Applied Innovations, 7(3), 10843–104849.
16. Bitragunta, S. L. V. (2022). High level modeling of high-voltage gallium nitride (GaN) power devices for sophisticated power electronics applications. Journal of Artificial Intelligence, Machine Learning and Data Science, 1(1), 2011–2015.
17. Selvarajan, K. (2024). Modernizing legacy big data platforms using cloud-native lakehouse architectures. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(2), 10377–10385.
18. Shaik, N. (2022). Beyond vendor lock-in: Building intelligent multi-cloud Kubernetes orchestration. International Research Journal of Innovative Engineering, 6(2), 9985–9996.
19. Mysula, S. (2023). Performance optimization of distributed enterprise integration workflows across heterogeneous applications. International Journal of Engineering & Extended Technologies Research, 5(1), 6003–6008.
20. Abdullah, S. M., Chy, M. S. K., Gazi, M. S., Alam, A., Himeluzzaman, M., Onik, T. A., & Jakir, T. (2023). Quantum-Enhanced Machine Learning for Early Disease Diagnosis: A Systematic Review and Public Health Innovation Perspective. International Journal of Science, Research and Technology, 6(4), 10384–10392.
21. 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.
22. 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.
23. Tarigoppula, S. (2022). Zero-trust security architecture for hybrid enterprise cloud environments using AWS native services. International Research Journal of Innovative Engineering, 6(6), 11433–11444.
24. Raja, G. V. (2020). Metadata gets a makeover: The machine learning approach. International Journal of Computer Technology and Electronics Communication, 3(6), 2900–2903.
25. Koganti, H. (2021). Machine learning-driven performance anomaly detection and auto-tuning in distributed Java full-stack systems: A comprehensive review. International Journal of Research Publications in Engineering, Technology and Management, 4(3), 4977–4986.
26. Padmanabham, S. (2024). AI assisted fraud investigation architecture patterns for technology controls in financial institutions. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(3), 10587–10592.
27. 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.
28. Chandran, A. S. (2023). A unified Spark ETL stack across Cloudera CDP, Azure HDInsight, and Databricks. International Journal of Future Innovative Science and Technology, 6(6), 11769–11779.
29. Yadavalli, V. R. (2023). Cutover intelligence mesh: A dependency-aware architecture for enterprise cloud ERP migration. International Journal of Future Innovative Science and Technology, 6(4), 11015–11031.
30. Gunasekaran, R. M. N. (2024). From Data to Decisions: How Quality Drives Machine Learning Success. The Eastasouth Journal of Information System and Computer Science, 2(01), 120–130.
31. Shaik, N. (2022). Engineering global resilience: Multi-cluster Istio for zero-downtime traffic management. International Journal of Science, Research and Technology, 5(5), 8522–8534.
32. 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.
33. Bandari, U., & Thilagaraj, S. V. (2023). Defensive and offensive data strategy in manufacturing M&A: A comparative evaluation of data lake, data fabric, and data mesh integration patterns. International Journal of Research and Applied Innovations, 6(1), 8403–8413.
34. Reddy, K. V. (2023). Reconfigurable FPGA-based acceleration of RTL simulation and co-simulation for cloud datacenter workloads. International Journal of Emerging Trends in Engineering and Management Research, 8(2), 13265.
35. Kalabhavi, V. (2023). Automated quote generation using AI for product configuration, pricing and external order integration in SAP CPQ. International Research Journal of Innovative Engineering, 7(3), 12503–12515.
36. Gadige, C. D. (2023). Secure CRM modernization: Architectural patterns for migrating enterprise customer data from legacy CRM platforms to Salesforce. International Journal of Science, Research and Technology, 6(3), 9946–9956.
37. 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.
38. Parast, F. K., Sindhav, C., Nikam, S., Yekta, H. I., Kent, K. B., & Hakak, S. (2022). Cloud computing security: A survey of service-based models. Computers & Security, 114, Article 102580. https://doi.org/10.1016/j.cose.2021.102580
