A multi-layer governance architecture for enterprise generative AI systems

Generative AI (GenAI)systems are increasingly embedded in enterprise workflows, yet existing governance approaches remain fragmented across policy, development, evaluation, and operational monitoring. GenAI systems produce unpredictable results because they generate output through probabilistic processes rather than deterministic logic, and large language models and foundation models have accelerated enterprise adoption across knowledge work, decision support, and content generation use cases. At the same time, generative systems introduce governance concerns related to hallucinations, output reliability, and accountability, which make enterprise deployment materially different from traditional software systems. Organizations commonly struggle to develop effective governance systems that oversee all stages of GenAI system development because these systems operate across multiple teams, technologies, data sources, and regulatory frameworks.
The study presents the EAGLE (Enterprise AI Governance and Lifecycle Execution) Framework, which functions as a multi-layer governance framework that assists organizations in establishing responsible and large-scale Generative AI deployment. The framework includes four key layers: Governance, which sets policies for responsible AI use, compliance, and data management; Program Orchestration, which coordinates collaboration between engineering, infrastructure, security, legal, and product teams; Evaluation, which validates model performance, output reliability, and business impact; and Operational Monitoring, which continuously tracks system performance, detects model drift, and manages incidents. The research team used Design Science Research to create and test the EAGLE framework which functions as an organized governance system for enterprise AI implementation. The research results demonstrate that EAGLE multi-layer governance systems enable organizations to prepare for extensive AI implementation through improved risk management capabilities and better accountability systems and enhanced interdepartmental cooperation. The system provides organizations with an operational guide that enables them to implement Generative AI systems in a dependable and regulation-compliant manner.