Architecting Trust: A Multi-Dimensional Analysis of Data Governance Frameworks for Secure Artificial Intelligence Adoption and Organizational Resilience
Keywords:
Data Governance, Artificial Intelligence Adoption, Digital Forensics, SME StrategyAbstract
Background: In an era characterized by the proliferation of Artificial Intelligence (AI) and complex multi-cloud environments, Data Governance (DG) has evolved from a compliance necessity to a strategic imperative. However, distinct gaps remain regarding the adaptability of traditional frameworks to modern security demands and the specific resource constraints of Small and Medium-sized Enterprises (SMEs).
Objectives: This study aims to synthesize current literature to construct a comprehensive understanding of how DG frameworks facilitate secure AI adoption, enhance digital forensic readiness, and support organizational resilience across varying operational scales.
Methods: A systematic literature review and bibliometric analysis were conducted, utilizing methodologies such as Methodi Ordinatio to qualitatively assess a bibliographic portfolio. The study examines the intersection of DG with blockchain technology, asset management, and public administration policy.
Results: Analysis reveals that while standard frameworks like DAMA-DMBOK provide foundational structure, they often lack the agility required for dynamic AI environments. The findings indicate a strong correlation between robust data quality monitoring and successful AI deployment. Furthermore, the "SME Quandary" persists, suggesting a need for scalable, modular governance architectures.
Conclusions: The paper proposes that modern DG must transition towards a "Governance as a Service" model, integrating forensic readiness and blockchain-enabled security. It concludes that effective governance is the primary determinant of trust in AI systems, requiring a shift from rigid control to dynamic, human-centric oversight.
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