Secure and Causality-Aware AI Cloud Architectures for Enterprise Automation across Mobile Healthcare and Omnichannel Retail Media Systems

Abstract

The convergence of artificial intelligence, cloud-native software engineering, and enterprise automation is transforming decision-making across regulated and data-intensive sectors such as mobile healthcare and omnichannel retail media. However, existing AI-driven cloud platforms often prioritize predictive accuracy and scalability while insufficiently addressing security, causal validity, and regulatory accountability. This paper proposes a secure and causality-aware AI cloud architecture designed to support enterprise automation across heterogeneous domains by integrating causal incrementality analysis and bandit-based optimization within a cloud-native framework.
The proposed architecture combines zero-trust security principles, privacy-preserving data pipelines, and compliance-aware governance layers with causal inference engines that distinguish correlation from true intervention impact. In mobile healthcare systems, this enables safe and explainable automation for clinical decision support, patient engagement, and remote monitoring while ensuring data confidentiality and regulatory compliance. In omnichannel retail media environments, the architecture supports adaptive customer acquisition and budget optimization through contextual multi-armed bandit models informed by causal attribution and real-time feedback.
By unifying secure AI engineering, causal intelligence, and cloud-native orchestration, the architecture enhances trustworthiness, operational efficiency, and cross-domain generalizability of enterprise automation platforms. The paper discusses design principles, system components, and deployment considerations, and highlights how causality-aware optimization improves decision reliability compared to purely predictive approaches. The proposed framework provides a foundation for building ethically aligned, secure, and scalable AI systems capable of supporting high-stakes automated decision-making in both healthcare and retail media ecosystems.