AI-Assisted Legacy Transformation: Modernizing Mission-Critical .NET Applications Through Cloud Integration and API Governance

Abstract

Mission-critical .NET Framework applications remain deeply embedded in enterprise and public-sector operations years after their originating platform stopped receiving new features, and the reasons they persist are the same reasons they resist modernization: undocumented business logic accumulated over decades, tightly coupled monolithic designs, and the operational risk of touching a system that cannot fail. Two developments have changed what a modernization program can attempt. First, cloud-native infrastructure gives legacy .NET estates a concrete migration target containerized runtimes, managed platform services, and governed API boundaries that was not available when many of these systems were built. Second, large-language-model-based coding assistants can now read, summarize, and transform undocumented legacy code far faster than a human engineer, but the same literature that documents their productivity gains also documents that their suggestions can be insecure, architecturally inconsistent, or simply wrong, and existing modernization guidance was written before such tools existed. This paper addresses the resulting gap by developing AI-Assisted Legacy Transformation (ALT): a reference architecture that uses large-language-model assistance for code comprehension, dependency mapping, and code transformation at each stage of a .NET-to-cloud modernization program, but routes every AI-generated artifact through a risk-tiered human review gate and an API governance layer before it reaches a mission-critical system. Because no production deployment of this specific architecture exists to evaluate, the paper adopts a design-science approach: the framework is built from and evaluated against peer-reviewed findings in legacy modernization, cloud migration, API governance, and AI-assisted software engineering, and every quantitative comparison presented two tables and four illustrative figures spanning capability coverage, migration velocity, the velocity-defect trade-off, and risk reduction through governance is explicitly labeled as a literature-grounded illustrative estimate rather than a measured result. The paper’s contribution is a synthesis of four otherwise separate literatures into one modernization pipeline, an explicit mapping of modernization objectives to specific AI-assisted techniques and governance controls, and a candid account of where AI assistance helps a .NET modernization program and where it introduces risk that only human governance can absorb.