Ethical AI-Driven Automation Framework for Secure SAP Cloud Environments: Managing Risk in Large-Scale Enterprise Systems
DOI:
https://doi.org/10.15662/IJEETR.2023.0506003Keywords:
SAP Cloud, AI automation, ethical AI, security, governance, identity and access management, data privacy, human-in-the-loop, compliance, risk managementAbstract
Enterprises increasingly adopt AI-driven automation within SAP cloud landscapes to accelerate business processes, reduce human error, and improve scalability. However, integrating automation and AI into mission-critical SAP systems introduces unique security, compliance, and ethical risks—ranging from data leakage and privileged-access abuse to algorithmic bias and opaque decision-making that can affect financial reporting and regulatory standing. This paper presents an Ethical AI-Driven Automation Framework tailored for large-scale SAP Cloud environments that combines technical safeguards, governance controls, and ethical design principles to manage risk end-to-end. The framework integrates (1) a threat-aware architecture for automation components (infrastructure, integration middleware, and AI services); (2) identity, access, and secrets management aligned to least-privilege and separation-of-duties (SoD) within SAP roles; (3) data governance and privacy-preserving techniques (data minimization, tokenization, differential privacy patterns) for training and inference; (4) algorithmic transparency and human-in-the-loop (HITL) checkpoints for high-impact transactions; and (5) continuous assurance through automated compliance checks, audit trails, and behavioral anomaly detection. We describe design patterns and implementation practices that map to SAP Cloud Platform capabilities, common integration topologies (API, IDoc, RFC), and enterprise CI/CD pipelines. A risk taxonomy and decision matrix is provided to prioritize controls by impact and likelihood. We evaluate the framework with a realistic enterprise scenario (automated invoice posting and vendor master changes) using threat modelling, control mapping, and tabletop risk exercises; this evaluation demonstrates measurable risk reduction in access violations, data exposure vectors, and reduction in high-impact false positives through HITL policies. The framework strikes a balance between operational automation benefits and ethical responsibility, providing enterprises a practical roadmap to deploy AI automation in SAP Cloud environments while meeting security, compliance, and stakeholder-trust requirements.
References
1. Floridi, L., & Sanders, J. W. (2004). On the morality of artificial agents. Minds and Machines, 14(3), 349–379.
2. Gosangi, S. R. (2023). AI AND THE FUTURE OF PUBLIC SECTOR ERP: INTELLIGENT AUTOMATION BEYOND DATA ANALYTICS. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(4), 8991-8995.
3. 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.
4. 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
5. 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
6. Kandula, N. (2023). Evaluating Social Media Platforms A Comprehensive Analysis of Their Influence on Travel Decision-Making. J Comp Sci Appl Inform Technol, 8(2), 1-9. https://www.researchgate.net/profile/Nagababu-Kandula/publication/393673311_Evaluating_Social_Media_Platforms_A_Comprehensive_Analysis_of_Their_Influence_on_Travel_Decision-Making/links/68753bd33c12dc437a3eaf1c/Evaluating-Social-Media-Platforms-A-Comprehensive-Analysis-of-Their-Influence-on-Travel-Decision-Making.pdf
7. Vengathattil, S. (2019). Ethical Artificial Intelligence - Does it exist? International Journal for Multidisciplinary Research, 1(3). https://doi.org/10.36948/ijfmr.2019.v01i03.37443
8. 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.
9. Selvi, R., Saravan Kumar, S., & Suresh, A. (2014). An intelligent intrusion detection system using average manhattan distance-based decision tree. In Artificial Intelligence and Evolutionary Algorithms in Engineering Systems: Proceedings of ICAEES 2014, Volume 1 (pp. 205-212). New Delhi: Springer India.
10. Peddamukkula, P. K. How Technology is Making Life Insurance Smarter and Faster: The Role of Cloud and Automation. https://www.researchgate.net/profile/Praveen-Peddamukkula/publication/397017728_How_Technology_is_Making_Life_Insurance_Smarter_and_Faster_The_Role_of_Cloud_and_Automation/links/69023a0cc900be105cbd89d5/How-Technology-is-Making-Life-Insurance-Smarter-and-Faster-The-Role-of-Cloud-and-Automation.pdf
11. 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.
12. 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.
13. Dong Wang, Lihua Dai (2022). Vibration signal diagnosis and conditional health monitoring of motor used in biomedical applications using Internet of Things environment. Journal of Engineering 5 (6):1-9.
14. 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.
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. van den Hoven, J., Vermaas, P. E., & van de Poel, I. (Eds.). (2015). Handbook of Ethics, Values, and Technological Design: Sources, Theory, Values and Application Domains. Springer.
17. Srinivas Chippagiri, Savan Kumar, Olivia R Liu Sheng,‖ Advanced Natural Language Processing (NLP) Techniques for Text-Data Based Sentiment Analysis on Social Media‖, Journal of Artificial Intelligence and Big Data(jaibd),1(1),11-20,2016.
18. 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
19. 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.
20. Sarker, I. H., Sheng, Q. Z., Kayes, A. S. M., & Badsha, S. (2020). A survey of machine learning models for cloud intrusion detection. ACM Computing Surveys, 53(1), Article 9.
21. 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.
22. Jeetha Lakshmi, P. S., Saravan Kumar, S., & Suresh, A. (2014). Intelligent Medical Diagnosis System Using Weighted Genetic and New Weighted Fuzzy C-Means Clustering Algorithm. In Artificial Intelligence and Evolutionary Algorithms in Engineering Systems: Proceedings of ICAEES 2014, Volume 1 (pp. 213-220). New Delhi: Springer India.
23. Sugumar, R. (2022). Estimation of Social Distance for COVID19 Prevention using K-Nearest Neighbor Algorithm through deep learning. IEEE 2 (2):1-6.
24. 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.
25. 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.
26. Pasumarthi, A. (2022). Architecting Resilient SAP Hana Systems: A Framework for Implementation, Performance Optimization, and Lifecycle Maintenance. International Journal of Research and Applied Innovations, 5(6), 7994-8003.
27. SANS Institute. (2016). Critical Controls for Effective Cyber Defense (Version 6). SANS/CIS Controls.
