Scalable Cloud Automation for SAP Ecosystems Ethical AI and Predictive Risk Management using Machine Learning
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Abstract
The growing adoption of cloud computing and automation within SAP ecosystems has enabled enterprises to achieve operational scalability, real-time analytics, and intelligent decision-making. However, as automation becomes increasingly driven by Artificial Intelligence (AI) and Machine Learning (ML), it raises critical challenges in ethical governance, predictive risk management, and data security. This paper presents a Scalable Cloud Automation Framework for SAP ecosystems that leverages machine learning to manage operational and compliance risks while maintaining ethical and transparent automation. The framework integrates ethical AI principles with predictive analytics to identify emerging risks across financial, procurement, and human resource workflows.
The proposed architecture encompasses four major layers: (1) Cloud Infrastructure and Integration Layer, enabling dynamic SAP deployments with containerized services and secure APIs; (2) Machine Learning and Predictive Risk Layer, which uses anomaly detection and probabilistic models for early risk alerts; (3) Ethical AI and Governance Layer, embedding fairness, accountability, and explainability into automated decision systems; and (4) Continuous Compliance Layer, automating security monitoring, access audits, and SoD enforcement using AI-driven insights.
A prototype implementation on SAP Business Technology Platform (BTP) demonstrates improvements in decision accuracy and compliance management. By combining predictive ML models with ethical oversight, the framework ensures automation integrity, reduces bias, and strengthens trust in AI-enabled business processes. Experimental results highlight a 70% reduction in risk incidents and enhanced audit traceability. The study underscores the need for responsible automation in cloud-native SAP ecosystems, proposing a roadmap for aligning enterprise automation strategies with transparency, accountability, and regulatory compliance.
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1. Amuda, K. K., Kumbum, P. K., Adari, V. K., Chunduru, V. K., & Gonepally, S. (2021). Performance evaluation of wireless sensor networks using the wireless power management method. Journal of Computer Science Applications and Information Technology, 6(1), 1–9.
2. Sugumar, R. (2023). A Deep Learning Framework for COVID-19 Detection in X-Ray Images with Global Thresholding. IEEE 1 (2):1-6.
3. Anand, L., & Neelanarayanan, V. (2019). Feature Selection for Liver Disease using Particle Swarm Optimization Algorithm. International Journal of Recent Technology and Engineering (IJRTE), 8(3), 6434-6439.
4. Kakulavaram, S. R. (2023). Performance Measurement of Test Management Roles in ‘A’ Group through the TOPSIS Strategy. International Journal of Artificial intelligence and Machine Learning, 1(3), 276. https://doi.org/10.55124/jaim.v1i3.276NIST. (2013). Security and Privacy Controls for Federal Information Systems and Organizations (SP 800-53 Rev.4).
5. Anbalagan, B. (2023). Proactive Failover and Automation Frameworks for Mission-Critical Workloads: Lessons from Manufacturing Industry. International Journal of Research and Applied Innovations, 6(1), 8279-8296.
6. Mohammed, A. A., Akash, T. R., Zubair, K. M., & Khan, A. (2020). AI-driven Automation of Business rules: Implications on both Analysis and Design Processes. Journal of Computer Science and Technology Studies, 2(2), 53-74.
7. Kandula N (2023). Gray Relational Analysis of Tuberculosis Drug Interactions A Multi-Parameter Evaluation of Treatment Efficacy. J Comp Sci Appl Inform Technol. 8(2): 1-10.
8. Chiranjeevi, K. G., Latha, R., & Kumar, S. S. (2016). Enlarge Storing Concept in an Efficient Handoff Allocation during Travel by Time Based Algorithm. Indian Journal of Science and Technology, 9, 40.
9. Sivaraju, P. S. (2023). Global Network Migrations & IPv4 Externalization: Balancing Scalability, Security, and Risk in Large-Scale Deployments. ISCSITR-INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS (ISCSITR-IJCA), 4(1), 7-34.
10. West, S. M., Whittaker, M., & Crawford, K. (2019). Discriminating systems: Gender, race, and power in AI. AI Now Institute.
11. IEEE. (2019). Ethically Aligned Design: A Vision for Prioritizing Human Well-being with Autonomous and Intelligent Systems. IEEE Global Initiative.
12. Cherukuri, B. R. (2020). Quantum machine learning: Transforming cloud-based AI solutions. https://www.researchgate.net/profile/Bangar-Raju-Cherukuri/publication/388617417_Quantum_machine_learning_Transforming_cloud-based_AI_solutions/links/67a33efb645ef274a46db8cf/Quantum-machine-learning-Transforming-cloud-based-AI-solutions.pdf
13. Mathur, T., Kotapati, V. B. R., & Das, D. (2020). Agentic Negotiation Framework for Strategic Vendor Management. Journal of Artificial Intelligence & Machine Learning Studies, 4, 143-177.
14. Kumar, R., Al-Turjman, F., Anand, L., Kumar, A., Magesh, S., Vengatesan, K., ... & Rajesh, M. (2021). Genomic sequence analysis of lung infections using artificial intelligence technique. Interdisciplinary Sciences: Computational Life Sciences, 13(2), 192-200.
15. Anbalagan, B., & Pasumarthi, A. (2022). Building Enterprise Resilience through Preventive Failover: A Real-World Case Study in Sustaining Critical Sap Workloads. International Journal of Computer Technology and Electronics Communication, 5(4), 5423-5441.
16. Sudhan, S. K. H. H., & Kumar, S. S. (2015). An innovative proposal for secure cloud authentication using encrypted biometric authentication scheme. Indian journal of science and technology, 8(35), 1-5.
17. Thambireddy, S., Bussu, V. R. R., & Joyce, S. (2023). Strategic Frameworks for Migrating Sap S/4HANA To Azure: Addressing Hostname Constraints, Infrastructure Diversity, And Deployment Scenarios Across Hybrid and Multi-Architecture Landscapes. Journal ID, 9471, 1297. https://www.researchgate.net/publication/396446597_Strategic_Frameworks_for_Migrating_Sap_S4HANA_To_Azure_Addressing_Hostname_Constraints_Infrastructure_Diversity_And_Deployment_Scenarios_Across_Hybrid_and_Multi-Architecture_Landscapes
18. Sugumar, R. (2023). Enhancing COVID-19 Diagnosis with Automated Reporting Using Preprocessed Chest X-Ray Image Analysis based on CNN (2nd edition). International Conference on Applied Artificial Intelligence and Computing 2 (2):35-40.
19. Gonepally, S., Amuda, K. K., Kumbum, P. K., Adari, V. K., & Chunduru, V. K. (2021). The evolution of software maintenance. Journal of Computer Science Applications and Information Technology, 6(1), 1–8. https://doi.org/10.15226/2474-9257/6/1/00150
20. European Commission. (2020). Ethics Guidelines for Trustworthy AI. High-Level Expert Group on Artificial Intelligence.