International Journal of Science, Engineering and TechnologyAuthors: Associate Professor Grace Cooper, Ella Edwards, Lily Campbell, Chaitanya Srinivas, Yashwanth kumar
Abstract: The rapid adoption of artificial intelligence (AI) across enterprise data environments has created significant opportunities for automation, predictive analytics, decision support, and intelligent data management, while simultaneously introducing challenges related to transparency, accountability, fairness, privacy, security, explainability, and regulatory compliance. This paper presents an evidence-mapping framework for responsible AI governance in enterprise data systems, designed to systematically organize and evaluate governance strategies that support trustworthy and accountable AI deployment. The proposed framework integrates evidence mapping with key responsible AI principles, including fairness, transparency, explainability, accountability, privacy protection, security, human oversight, and regulatory compliance. It examines how governance mechanisms can be aligned with enterprise data lifecycle activities, including data acquisition, integration, processing, modeling, storage, sharing, and decision-making. The framework further categorizes available evidence according to governance dimensions, organizational responsibilities, risk-management practices, technical controls, and compliance requirements, enabling organizations to identify evidence gaps and prioritize governance interventions. By connecting responsible AI principles with enterprise data governance practices, the proposed approach provides a structured foundation for assessing AI-related risks and improving organizational accountability. The study demonstrates that evidence-driven governance can strengthen AI transparency, improve data quality and traceability, support regulatory readiness, and facilitate more consistent implementation of responsible AI practices across complex enterprise environments. The proposed framework can serve as a practical and research-oriented foundation for organizations seeking to develop scalable, auditable, and trustworthy AI governance strategies.
