A Governance Framework For Trustworthy AI-Generated Data Engineering Artifacts In Cloud Environments

Abstract

AI systems are increasingly capable of generating data engineering artifacts, transformation logic, orchestration workflows, quality rules, and documentation at speeds that outpace traditional review processes. This productivity gain introduces risks related to semantic correctness, policy compliance, provenance, security, and regulatory accountability that existing controls for the software development lifecycle were not designed to address. This article proposes a governance framework for trustworthy AI-generated data engineering artifacts in cloud environments. The framework defines six control domains: provenance tracking, policy-aware generation, technical validation, human review, lifecycle management, and audit reporting. Together these domains ensure that AI-assisted engineering accelerates delivery without compromising enterprise trust or compliance obligations. The paper presents a governance problem analysis identifying four distinct risk categories, describes the framework architecture and implementation strategy, defines evaluation criteria, and outlines enterprise adoption implications. The central argument is that governance is not a constraint on AI adoption; it is the mechanism that makes AI adoption sustainable at the artifact volumes, organizational scales, and regulatory expectations that meaningful productivity gains require.