The rapid integration of artificial intelligence into enterprise information systems has intensified the need for structured data governance and operationalized ethical controls. While existing research provides normative AI ethics principles and conceptual data governance models, limited work integrates these domains into a unified enterprise level architecture with measurable performance outcomes. This study proposes and empirically evaluates an Intelligent Data Governance and Ethical AI Framework designed to embed fairness, transparency, accountability, and risk monitoring directly into the AI lifecycle within enterprise environments. Using a design science research methodology, the study develops a multi layer governance architecture that integrates data ownership controls, ethical constraint modeling, lifecycle monitoring, and enterprise risk management mechanisms. The framework is evaluated through a comparative experimental design contrasting a baseline AI system with a governance integrated system across predictive accuracy, fairness metrics, enterprise risk exposure, and robustness under distribution shift. Statistical analysis demonstrates significant improvements in predictive reliability, substantial reductions in fairness disparities, and meaningful declines in composite enterprise risk indicators following framework implementation. The findings indicate that ethical governance, when embedded architecturally rather than applied as external oversight, enhances both technical performance and organizational accountability. By operationalizing ethical principles through intelligent data governance mechanisms, the proposed framework advances the state of the art in enterprise AI management and provides a structured pathway for trustworthy, resilient, and high performing AI systems.
Nahar et al. (2026) studied this question.