AI-Driven Data Governance and Compliance: Building Trustworthy Enterprise Intelligence Systems
Keywords:
AI Governance, Data Lineage, Model Explainability, Bias Mitigation, Regulatory Compliance, Enterprise Trust ArchitectureAbstract
Enterprise AI adoption has grown faster than the systems designed to manage it. By early 2024, 65% of organizations were using generative AI in at least one core business function, yet only 18% had an enterprise-wide council with authority over AI risk decisions. This gap creates real exposure. An example of the quantification of AI risk within the economy includes the increase in public data breaches, liability lawsuits against chatbots, and regulatory fines. Based on findings from the 2024 IBM Cost of a Data Breach Report, the global average cost of a data breach stands at $4.88 million per data breach, although it can be reduced to $2.2 million through the use of AI technology. This article examines eight elements of a sound AI governance program: the structural governance gap, data lineage as the basis of accountability, explainability tools that make model outputs clear, bias detection across the full model lifecycle, AI-powered governance automation, compliance with the NIST AI RMF, ISO/IEC 42001, and the EU AI Act, a layered enterprise trust architecture, and the wider societal stakes of responsible AI deployment. The analysis draws on industry benchmarks, regulatory texts, and recent empirical research.
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