Sarcouncil Journal of Engineering and Computer Sciences

Abstract: This article presents a comprehensive blueprint for integrating data governance and AI governance frameworks to create more trustworthy artificial intelligence systems. As AI increasingly permeates critical business and societal functions, traditional data governance approaches prove insufficient for addressing the unique challenges of algorithmic systems. The convergence of these governance domains represents an essential evolution, treating data and AI assets as components within a continuous lifecycle rather than discrete entities. By extending metadata frameworks, implementing unified policies, and establishing cross-functional governance structures, organizations can achieve consistent oversight across the entire AI value chain. The blueprint outlines architectural components for implementation, including integrated technology stacks and risk-based control frameworks, alongside operational strategies for bias management, transparency practices, and compliance readiness. This converged governance architecture enables enhanced traceability, consistent policy enforcement, and more efficient regulatory compliance, ultimately leading to AI systems that are performant, fair, and aligned with societal expectations.