AI-Enhanced Integrations: Secure API Management for Multi-Cloud ERP Environments
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V6I3P115Keywords:
Artificial Intelligence, Api Management, Multi-Cloud Computing, Erp Systems, Cloud Security, Machine Learning, Zero Trust Architecture, Enterprise IntegrationAbstract
The rapid adoption of multi-cloud strategies and enterprise resource planning (ERP) platforms has significantly transformed digital enterprise ecosystems. Organizations increasingly rely on application programming interfaces (APIs) to enable seamless integration across heterogeneous cloud infrastructures, third-party services, and internal enterprise applications. However, the exponential growth of API-driven interactions introduces substantial challenges related to security, scalability, performance optimization, governance, and real-time threat detection. Traditional API management approaches, largely rule-based and reactive, are insufficient to address the dynamic, distributed, and high-velocity nature of modern multi-cloud ERP environments. This paper presents a comprehensive study on AI-enhanced secure API management frameworks tailored for multi-cloud ERP ecosystems. By leveraging artificial intelligence (AI) and machine learning (ML) techniques including anomaly detection, behavioral analytics, reinforcement learning, and predictive modeling—the proposed framework enables proactive security enforcement, adaptive traffic management, intelligent policy orchestration, and continuous compliance monitoring. The research integrates AI models with API gateways, identity and access management (IAM), and cloud-native security services to enhance resilience against evolving cyber threats such as API abuse, credential stuffing, distributed denial-of-service (DDoS) attacks, and data exfiltration. The methodology involves designing an AI-driven API security architecture, implementing it across simulated multi-cloud ERP environments, and evaluating performance using key metrics such as latency, threat detection accuracy, scalability, and fault tolerance. Experimental results demonstrate that AI-enhanced API management significantly outperforms traditional approaches by reducing security incidents, improving system availability, and optimizing cross-cloud data flows. The findings establish AI-driven secure API management as a critical enabler for next-generation ERP integrations in multi-cloud environments
Downloads
References
[1] Kanagasabapathi, K., Mahajan, K., Ahamad, S., Soumya, E., & Barthwal, S. (2023, December). AI-enhanced multi-cloud security management: Ensuring robust cybersecurity in hybrid cloud environments. In 2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES) (pp. 1-6). IEEE.
[2] Hussain, F., Noye, B., & Sharieh, S. (2022). Current state of API security and machine learning. IEEE Technology Policy and Ethics, 4(2), 1-5.
[3] Raj, P., & Raman, A. (2018). Multi-cloud management: Technologies, tools, and techniques. In Software-defined cloud centers: Operational and management technologies and tools (pp. 219-240). Cham: Springer International Publishing.
[4] Abd Elmonem, M. A., Nasr, E. S., & Geith, M. H. (2016). Benefits and challenges of cloud ERP systems–A systematic literature review. Future Computing and Informatics Journal, 1(1-2), 1-9.
[5] Alzoubi, Y. I., Mishra, A., & Topcu, A. E. (2024). Research trends in deep learning and machine learning for cloud computing security. Artificial intelligence review, 57(5), 132.
[6] Vashishth, T. K., Sharma, V., Sharma, K. K., Kumar, B., Chaudhary, S., & Panwar, R. (2024). Enhancing cloud security: The role of artificial intelligence and machine learning. In Improving security, privacy, and trust in cloud computing (pp. 85-112). IGI Global Scientific Publishing.
[7] Papazoglou, M. P., & Van Den Heuvel, W. J. (2007). Service oriented architectures: approaches, technologies and research issues. The VLDB journal, 16(3), 389-415.
[8] Pautasso, C., Zimmermann, O., Amundsen, M., Lewis, J., & Josuttis, N. (2017). Microservices in practice, part 1: Reality check and service design. IEEE software, 34(01), 91-98.
[9] Ghazawneh, A., & Henfridsson, O. (2013). Balancing platform control and external contribution in third‐party development: the boundary resources model. Information systems journal, 23(2), 173-192.
[10] Bass, L., Weber, I., & Zhu, L. (2015). DevOps: A software architect's perspective. Addison-Wesley Professional.
[11] Pahl, C. (2015). Containerization and the paas cloud. IEEE Cloud Computing, 2(3), 24-31.
[12] Petcu, D. (2013, April). Multi-cloud: expectations and current approaches. In Proceedings of the 2013 international workshop on Multi-cloud applications and federated clouds (pp. 1-6).
[13] Buczak, A. L., & Guven, E. (2015). A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Communications surveys & tutorials, 18(2), 1153-1176.
[14] Kim, G., Lee, S., & Kim, S. (2014). A novel hybrid intrusion detection method integrating anomaly detection with misuse detection. Expert Systems with Applications, 41(4), 1690-1700.
[15] Shone, N., Ngoc, T. N., Phai, V. D., & Shi, Q. (2018). A deep learning approach to network intrusion detection. IEEE transactions on emerging topics in computational intelligence, 2(1), 41-50.
[16] Hwang, R. H., Peng, M. C., Huang, C. W., Lin, P. C., & Nguyen, V. L. (2020). An unsupervised deep learning model for early network traffic anomaly detection. IEEE Access, 8, 30387-30399.
[17] Fan, C. I., Hsiao, H. W., Chou, C. H., & Tseng, Y. F. (2015, July). Malware detection systems based on API log data mining. In 2015 IEEE 39th annual computer software and applications conference (Vol. 3, pp. 255-260). IEEE.
[18] Jangam, S. K., Karri, N., & Muntala, P. S. R. P. (2022). Advanced API Security Techniques and Service Management. International Journal of Emerging Research in Engineering and Technology, 3(4), 63-74.
[19] Vennala, B. L., Suresh, D., Samiya, P., Suresh, M., & Vamsi, B. (2025, March). Integrating Artificial Intelligence with Cloud Platforms to Optimize Performance, Scalability, and Reliability in Distributed Computing Systems. In 2025 International Conference on Data Science, Agents & Artificial Intelligence (ICDSAAI) (pp. 1-6). IEEE.
[20] Sharma, S., Kumar, N., Dash, Y., Dubey, A., & Devi, K. (2024, September). Intelligent multi-cloud orchestration for AI workloads: enhancing performance and reliability. In 2024 7th International Conference on Contemporary Computing and Informatics (IC3I) (Vol. 7, pp. 1421-1426). IEEE.
[21] Nangi, P. R., & Reddy Nala Obannagari, C. K. (2024). High-Performance Distributed Database Partitioning Using Machine Learning-Driven Workload Forecasting and Query Optimization. American International Journal of Computer Science and Technology, 6(2), 11-21. https://doi.org/10.63282/3117-5481/AIJCST-V6I2P102
[22] Sundar, D., & Jayaram, Y. (2022). Composable Digital Experience: Unifying ECM, WCM, and DXP through Headless Architecture. International Journal of Emerging Research in Engineering and Technology, 3(1), 127-135. https://doi.org/10.63282/3050-922X.IJERET-V3I1P113
[23] Jayaram, Y. (2024). Private LLMs for Higher Education: Secure GenAI for Academic & Administrative Content. American International Journal of Computer Science and Technology, 6(4), 28-38. https://doi.org/10.63282/3117-5481/AIJCST-V6I4P103
[24] Sundar, D. (2023). Machine Learning Frameworks for Media Consumption Intelligence across OTT and Television Ecosystems. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 4(2), 124-134. https://doi.org/10.63282/3050-9262.IJAIDSML-V4I2P114
[25] Nangi, P. R., Reddy Nala Obannagari, C. K., & Settipi, S. (2024). A Federated Zero-Trust Security Framework for Multi-Cloud Environments Using Predictive Analytics and AI-Driven Access Control Models. International Journal of Emerging Research in Engineering and Technology, 5(2), 95-107. https://doi.org/10.63282/3050-922X.IJERET-V5I2P110
[26] Jayaram, Y., & Sundar, D. (2022). Enhanced Predictive Decision Models for Academia and Operations through Advanced Analytical Methodologies. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 3(4), 113-122. https://doi.org/10.63282/3050-9262.IJAIDSML-V3I4P113
[27] Sundar, D. (2022). Architectural Advancements for AI/ML-Driven TV Audience Analytics and Intelligent Viewership Characterization. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 3(1), 124-132. https://doi.org/10.63282/3050-9262.IJAIDSML-V3I1P113
[28] Nangi, P. R., Obannagari, C. K. R. N., & Settipi, S. (2022). Enhanced Serverless Micro-Reactivity Model for High-Velocity Event Streams within Scalable Cloud-Native Architectures. International Journal of Emerging Research in Engineering and Technology, 3(3), 127-135. https://doi.org/10.63282/3050-922X.IJERET-V3I3P113
[29] Jayaram, Y., Sundar, D., & Bhat, J. (2024). Generative AI Governance & Secure Content Automation in Higher Education. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 5(4), 163-174. https://doi.org/10.63282/3050-9262.IJAIDSML-V5I4P116
[30] Sundar, D., & Bhat, J. (2023). AI-Based Fraud Detection Employing Graph Structures and Advanced Anomaly Modeling Techniques. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 4(3), 103-111. https://doi.org/10.63282/3050-9262.IJAIDSML-V4I3P112
[31] Nangi, P. R., Reddy Nala Obannagari, C. K., & Settipi, S. (2023). A Multi-Layered Zero-Trust Security Framework for Cloud-Native and Distributed Enterprise Systems Using AI-Driven Identity and Access Intelligence. International Journal of Emerging Trends in Computer Science and Information Technology, 4(3), 144-153. https://doi.org/10.63282/3050-9246.IJETCSIT-V4I3P115
[32] Jayaram, Y. (2023). Cloud-First Content Modernization: Migrating Legacy ECM to Secure, Scalable Cloud Platforms. International Journal of Emerging Research in Engineering and Technology, 4(3), 130-139. https://doi.org/10.63282/3050-922X.IJERET-V4I3P114
[33] Sundar, D. (2024). Streaming Analytics Architectures for Live TV Evaluation and Ad Performance Optimization. American International Journal of Computer Science and Technology, 6(5), 25-36. https://doi.org/10.63282/3117-5481/AIJCST-V6I5P103
[34] Nangi, P. R., Obannagari, C. K. R. N., & Settipi, S. (2022). Predictive SQL Query Tuning Using Sequence Modeling of Query Plans for Performance Optimization. International Journal of AI, BigData, Computational and Management Studies, 3(2), 104-113. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V3I2P111
[35] Jayaram, Y., & Bhat, J. (2022). Intelligent Forms Automation for Higher Ed: Streamlining Student Onboarding and Administrative Workflows. International Journal of Emerging Trends in Computer Science and Information Technology, 3(4), 100-111. https://doi.org/10.63282/3050-9246.IJETCSIT-V3I4P110
[36] Sundar, D. (2023). Serverless Cloud Engineering Methodologies for Scalable and Efficient Data Pipeline Architectures. International Journal of Emerging Trends in Computer Science and Information Technology, 4(2), 182-192. https://doi.org/10.63282/3050-9246.IJETCSIT-V4I2P118
[37] Jayaram, Y., Sundar, D., & Bhat, J. (2022). AI-Driven Content Intelligence in Higher Education: Transforming Institutional Knowledge Management. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 3(2), 132-142. https://doi.org/10.63282/3050-9262.IJAIDSML-V3I2P115
[38] Nangi, P. R. (2022). Multi-Cloud Resource Stability Forecasting Using Temporal Fusion Transformers. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 3(3), 123-135. https://doi.org/10.63282/3050-9262.IJAIDSML-V3I3P113
[39] Sundar, D., Jayaram, Y., & Bhat, J. (2024). Generative AI Frameworks for Digital Academic Advising and Intelligent Student Supporst Systems. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 5(3), 128-138. https://doi.org/10.63282/3050-9262.IJAIDSML-V5I3P114
[40] Nangi, P. R., & Settipi, S. (2023). A Cloud-Native Serverless Architecture for Event-Driven, Low-Latency, and AI-Enabled Distributed Systems. International Journal of Emerging Research in Engineering and Technology, 4(4), 128-136. https://doi.org/10.63282/3050-922X.IJERET-V4I4P113
[41] Jayaram, Y., & Sundar, D. (2023). AI-Powered Student Success Ecosystems: Integrating ECM, DXP, and Predictive Analytics. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 4(1), 109-119. https://doi.org/10.63282/3050-9262.IJAIDSML-V4I1P113
[42] Sundar, D., Jayaram, Y., & Bhat, J. (2022). A Comprehensive Cloud Data Lakehouse Adoption Strategy for Scalable Enterprise Analytics. International Journal of Emerging Research in Engineering and Technology, 3(4), 92-103. https://doi.org/10.63282/3050-922X.IJERET-V3I4P111
[43] Nangi, P. R., & Reddy Nala Obannagari, C. K. (2024). A Multi-Layered Zero-Trust–Driven Cybersecurity Framework Integrating Deep Learning and Automated Compliance for Heterogeneous Enterprise Clouds. American International Journal of Computer Science and Technology, 6(4), 14-27. https://doi.org/10.63282/3117-5481/AIJCST-V6I4P102
[44] Sundar, D. (2024). Enterprise Data Mesh Architectures for Scalable and Distributed Analytics. American International Journal of Computer Science and Technology, 6(3), 24-35. https://doi.org/10.63282/3117-5481/AIJCST-V6I3P103
[45] Nangi, P. R., Reddy Nala Obannagari, C. K., & Settipi, S. (2024). Serverless Computing Optimization Strategies Using ML-Based Auto-Scaling and Event-Stream Intelligence for Low-Latency Enterprise Workloads. International Journal of Emerging Trends in Computer Science and Information Technology, 5(3), 131-142. https://doi.org/10.63282/3050-9246.IJETCSIT-V5I3P113
[46] Jayaram, Y. (2024). AI-Driven Personalization 2.0: Hyper-Personalized Journeys for Every Student Type. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 5(1), 149-159. https://doi.org/10.63282/3050-9262.IJAIDSML-V5I1P114
[47] Reddy Nangi, P., & Reddy Nala Obannagari, C. K. (2023). Scalable End-to-End Encryption Management Using Quantum-Resistant Cryptographic Protocols for Cloud-Native Microservices Ecosystems. International Journal of Emerging Trends in Computer Science and Information Technology, 4(1), 142-153. https://doi.org/10.63282/3050-9246.IJETCSIT-V4I1P116
[48] Jayaram, Y. (2023). Data Governance and Content Lifecycle Automation in the Cloud for Secure, Compliance-Oriented Data Operations. International Journal of AI, BigData, Computational and Management Studies, 4(3), 124-133. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V4I3P113
[49] Nangi, P. R., Obannagari, C. K. R. N., & Settipi, S. (2022). Self-Auditing Deep Learning Pipelines for Automated Compliance Validation with Explainability, Traceability, and Regulatory Assurance. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 3(1), 133-142. https://doi.org/10.63282/3050-9262.IJAIDSML-V3I1P114
