AI-Driven Enterprise Application Modernization: Integrating Cloud-Native Microservices, Intelligent Data Pipelines, and Secure Infrastructure

Abstract

Large enterprises continue to operate substantial estates of monolithic, tightly coupled applications even as cloud-native microservices, intelligent data pipelines, and zero-trust security models have become the accepted architectural baseline for new systems. Modernization efforts that address these three concerns separately decomposing a monolith into services, rebuilding its data flows, and retrofitting its security posture tend to produce a system that is cloud-native in form but still fragile in practice, because the boundaries chosen for the service decomposition rarely align with the boundaries a well-designed data pipeline or a zero-trust access model would require. This paper proposes TRIMOD (Tri-Layer Intelligent Modernization), an architecture in which an AI-assisted decomposition layer, an intelligent data-pipeline layer, and a zero-trust secure-infrastructure layer are designed jointly rather than sequentially, coordinated by a central modernization orchestration engine that uses the same service-boundary analysis to inform microservice decomposition, pipeline topology, and access-policy scoping at once. Following a design-science research approach, the architecture is illustrated, not empirically validated, through a comparative capability table, two illustrative figures, and a short case scenario, with every quantitative value explicitly labeled as an illustrative estimate grounded in the cited literature’s directional findings rather than as measured data from a production system. The paper situates TRIMOD against the literature on microservice decomposition, AI-assisted software modernization, intelligent data-pipeline architecture, and zero-trust infrastructure, and discusses the framework’s practical implications, assumptions, and limitations.