Enterprise-Scale AI Architecture for Secure Mobile Platforms with Governance-Driven Automation Large-Scale Data Warehousing and Machine Learning

Abstract

Enterprise-scale adoption of artificial intelligence requires architectures that are secure, scalable, and governed across heterogeneous digital ecosystems. This paper presents an enterprise-scale AI architecture designed for secure mobile platforms that integrates governance-driven automation, large-scale data warehousing, and machine learning capabilities. The proposed architecture enables cross-domain intelligence by unifying data ingestion, storage, processing, and analytics while enforcing security, privacy, and compliance policies throughout the AI lifecycle. Large-scale data warehousing serves as the foundation for managing structured and unstructured data, supporting real-time and batch analytics for machine learning model training and inference. Governance-driven automation ensures transparency, auditability, and ethical AI operations through policy enforcement, access control, and continuous monitoring. The architecture supports scalable deployment across enterprise environments, enhances decision intelligence, and enables secure, data-driven automation for modern mobile platforms. This work demonstrates how integrated AI, data, and governance frameworks can address operational complexity and regulatory requirements in enterprise-scale systems.