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Digital transformation has redefined business operations, driving efficiency, innovation, and competitiveness through artificial intelligence (AI) and advanced analytics. However, the rapid adoption of AI-driven processes introduces significant regulatory and security challenges, necessitating robust data governance frameworks to ensure compliance, mitigate risks, and protect sensitive information. This study explores the intersection of digital transformation and data governance, highlighting strategies for regulatory compliance and secure AI-driven business operations. The paper first examines the evolving landscape of AI regulation, emphasizing global frameworks such as the General Data Protection Regulation (GDPR), the California Consumer Privacy Act (CCPA), and emerging AI governance policies. It underscores the critical role of compliance in mitigating data privacy concerns, ensuring transparency, and fostering ethical AI implementation. Next, the study explores data governance strategies essential for AI-driven enterprises. These strategies include data classification, access control mechanisms, encryption protocols, and real-time auditing to enhance data integrity and security. The importance of explainable AI (XAI) is also discussed, demonstrating how organizations can achieve regulatory alignment while maintaining AI model interpretability. Furthermore, the research highlights best practices for integrating digital transformation initiatives with data governance frameworks. It presents case studies on AI-driven businesses that have successfully implemented compliance-driven operational models, showcasing how enterprises can balance innovation with regulatory adherence. Key elements such as risk-based approaches, third-party data audits, and compliance automation tools are analyzed. Finally, the paper provides insights into future trends in AI governance, predicting the increasing convergence of digital transformation, AI ethics, and regulatory policies. As AI adoption accelerates, enterprises must adopt proactive data governance frameworks to address security vulnerabilities, regulatory obligations, and ethical considerations. This study serves as a comprehensive guide for organizations navigating the complexities of digital transformation while ensuring data security, regulatory compliance, and responsible AI implementation. By integrating strategic data governance practices, businesses can unlock AI's full potential while safeguarding consumer trust and regulatory alignment.
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