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All Journal The Eastasouth Journal of Information System and Computer Science
Rajendra Asuri
Senior SAP BTP Consultant
Computer Science & IT

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Architecting Responsible AI Governance on SAP BTP for Intelligent Enterprise Transformation through Explainable Systems, Adaptive Compliance Controls, and Trusted Automation Engineering Rajendra Asuri
The Eastasouth Journal of Information System and Computer Science Vol. 3 No. 02 (2025): The Eastasouth Journal of Information System and Computer Science (ESISCS)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esiscs.v3i02.1247

Abstract

Rapid enterprise adoption of artificial intelligence within SAP Business Technology Platform environments has introduced critical challenges associated with governance transparency, ethical automation, regulatory compliance, and operational accountability. Existing enterprise governance models often lack integrated mechanisms capable of addressing explainability, adaptive policy enforcement, and intelligent risk monitoring across complex AI driven ecosystems. This study proposes a comprehensive Responsible AI Governance framework for SAP BTP deployments designed to strengthen trusted enterprise transformation through explainable systems, adaptive compliance controls, and secure automation engineering. The research applies a mixed methodological approach that combines qualitative analysis of enterprise governance practices with quantitative evaluation of governance effectiveness across AI lifecycle management processes. The framework integrates explainable intelligence models, automated compliance orchestration, policy driven governance workflows, and continuous risk assessment mechanisms to improve transparency, operational resilience, and decision accountability within enterprise platforms. Findings demonstrate that the proposed architecture significantly enhances governance efficiency, reduces compliance complexity, improves trust in AI enabled business operations, and supports scalable enterprise innovation without compromising security or ethical standards. The study contributes strategically by establishing a governance blueprint that aligns enterprise AI adoption with organizational accountability and regulatory readiness, while academically extending existing research in responsible AI governance and intelligent enterprise systems. The proposed framework provides a scalable foundation for organizations seeking to operationalize trustworthy AI capabilities within modern SAP driven digital transformation environments.
A Privacy-Preserving Federated Learning Framework on SAP Business Technology Platform for Secure, Scalable, and Collaborative Predictive Analytics in Distributed Enterprise Environments Rajendra Asuri
The Eastasouth Journal of Information System and Computer Science Vol. 2 No. 01 (2024): The Eastasouth Journal of Information System and Computer Science (ESISCS)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esiscs.v2i01.1248

Abstract

Enterprise organizations increasingly rely on predictive analytics while facing strict data privacy requirements and fragmented data landscapes that limit centralized model training. This study addresses the challenge of enabling accurate and scalable predictive intelligence without exposing sensitive enterprise data by proposing a privacy-preserving federated learning framework implemented on SAP Business Technology Platform. The research problem focuses on balancing model performance, data security, and interoperability across distributed environments. A mixed-method approach is adopted, combining quantitative evaluation of model accuracy, convergence behavior, and communication efficiency with qualitative assessment of governance, trust, and deployment feasibility. The proposed framework integrates secure aggregation, decentralized training orchestration, and policy-driven data access controls to ensure privacy while maintaining analytical performance. Experimental results demonstrate that federated models achieve comparable accuracy to centralized approaches while significantly reducing data exposure risks and improving compliance alignment. The study introduces an architectural blueprint that enhances scalability, resilience, and cross-organizational collaboration. Findings contribute to both academic discourse on distributed machine learning and industry practices by establishing a structured approach to privacy-aware predictive analytics. The research concludes that federated learning on SAP BTP enables sustainable, secure, and enterprise-ready AI adoption, offering strategic value for organizations seeking to unlock insights from sensitive and distributed data ecosystems.
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