Secure AI and Machine Learning Integration in SAP Cloud Platforms: An Ethical Automation Framework for Risk Governance
DOI:
https://doi.org/10.15662/IJARCST.2023.0605006Keywords:
SAP BTP, AI governance, ethical AI, machine learning lifecycle, risk management, secure automation, data governance, model monitoring, complianceAbstract
Enterprise organizations increasingly embed machine learning (ML) and AI capabilities within cloud-native enterprise resource planning (ERP) environments to automate processes, improve decision-support, and extract operational value. SAP Business Technology Platform (BTP) and related SAP cloud services provide tightly integrated AI/ML primitives that enable predictive analytics, process automation, and generative assistance across business processes. However, embedding AI into mission-critical SAP landscapes raises governance, security, privacy, and ethical risks — including model bias, data leakage, unauthorized decision automation, and compliance gaps — that demand a structured risk-governance approach. This paper presents an Ethical Automation Framework for Risk Governance (EAF-RG) tailored to SAP cloud platforms, combining technical controls, process-level governance, and ethical guardrails. The EAF-RG prescribes layered controls: (1) data governance and provenance (model input lineage, data minimization, and consent-aware ingestion), (2) model lifecycle governance (transparent model cards, versioning, validation, and continuous monitoring), (3) platform security (identity & access management, encryption in transit/at rest, secure model deployment pipelines) and (4) organizational oversight (roles, audit trails, risk registers, and human-in-the-loop escalation). The framework aligns with recognized standards and guidance — notably the EU “Trustworthy AI” principles and risk-management guidance — while adapting to the operational specifics of SAP BTP and enterprise SAP landscapes. We outline a research methodology for validating the framework via scenario-driven threat modeling, prototype implementation on SAP BTP, red-team exercises, and stakeholder impact assessments. Findings indicate that integrated governance (technical + process + ethical review) reduces model-related risk and increases compliance readiness, but requires organizational investment, cross-functional teams, and continual monitoring. The paper concludes with practical recommendations for SAP customers and platform teams, and a roadmap for future empirical evaluation. (Digital Strategy)
References
1. Ponnoju, S. C., Kotapati, V. B. R., & Mani, K. (2022). Enhancing Cloud Deployment Efficiency: A Novel Kubernetes-Starling Hybrid Model for Financial Applications. American Journal of Autonomous Systems and Robotics Engineering, 2, 203-240.
2. 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.
3. 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.
4. Gonepally, S., Amuda, K. K., Kumbum, P. K., Adari, V. K., & Chunduru, V. K. (2022). Teaching software engineering by means of computer game development: Challenges and opportunities using the PROMETHEE method. SOJ Materials Science & Engineering, 9(1), 1–9.
5. 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.
6. 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.
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. AKTER, S., ISLAM, M., FERDOUS, J., HASSAN, M. M., & JABED, M. M. I. (2023). Synergizing Theoretical Foundations and Intelligent Systems: A Unified Approach Through Machine Learning and Artificial Intelligence.
9. Barocas, S., & Selbst, A. D. (2016). Big data’s disparate impact. California Law Review, 104(3), 671–732.
10. Floridi, L., Cowls, J., King, T., & Taddeo, M. (2018). How to design AI for social good: Seven essential factors. Science and Engineering Ethics, 24, 597–614.
11. Diakopoulos, N. (2016). Accountability in algorithmic decision-making. Communications of the ACM, 59(2), 56–62.
12. Sridhar Kakulavaram. (2022). Life Insurance Customer Prediction and Sustainbility Analysis Using Machine Learning Techniques. International Journal of Intelligent Systems and Applications in Engineering, 10(3s), 390 –.Retrieved from https://ijisae.org/index.php/IJISAE/article/view/7649
13. 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.
14. 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
15. Kesavan, E. (2023). Codeless Automation Versus Scripting: A Case Study on Selenium-Based JavaScript Testing Tools. International Journal of Scientific Research and Modern Technology, 2(5), 7-14. https://ideas.repec.org/a/daw/ijsrmt/v2y2023i5p7-14id843.html
16. Chunduru, V. K., Gonepally, S., Amuda, K. K., Kumbum, P. K., & Adari, V. K. (2022). Evaluation of human information processing: An overview for human-computer interaction using the EDAS method. SOJ Materials Science & Engineering, 9(1), 1–9.
17. Kesavan, E. (2022). Real-Time Adaptive Framework for Behavioural Malware Detection in Evolving Threat Environments. International Journal of Scientific Research and Modern Technology, 1(3), 32-39. https://ideas.repec.org/a/daw/ijsrmt/v1y2022i3p32-39id842.html
18. 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
19. 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.
20. 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.
21. 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.
22. Arul Raj .A.M and Sugumar R.,” Monitoring of the social Distance between Passengers in Real-time through video Analytics and Deep learning in Railway stations for Developing highest Efficiency” , March 2023 International Conference on Data Science, Agents and Artificial Intelligence, ICDSAAI 2022, ISBN 979- 835033384-8, March 2023, Chennai , India ., DOI 10.1109/ICDSAAI55433.2022.10028930.
23. 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
