Secure Data Access Framework Using Dynamic Data Masking and Auditing in Azure SQL for Regulatory Compliance

Background: The rapid adoption of cloud-based databases, organizations face increasing challenges in protecting sensitive data while complying with regulatory standards. Azure SQL provides built-in security features, yet improper configuration may lead to data exposure. Ensuring secure access without compromising usability remains a critical concern. Regulatory bodies demand strict control, visibility, and accountability over data usage. Hence, a structured security framework is essential. This study addresses these emerging security and compliance needs. Aim: The primary aim of this work is to design a secure data access framework using Dynamic Data Masking (DDM) and auditing features in Azure SQL. The framework focuses on minimizing unauthorized data exposure. It aims to support regulatory compliance such as GDPR and HIPAA. The goal is to balance data security with operational efficiency. Additionally, the framework enhances transparency in data access. It provides organizations with a compliance-ready architecture. Method: The proposed framework integrates role-based access control, Dynamic Data Masking, and Azure SQL auditing. Sensitive attributes are masked dynamically based on user roles. Auditing logs are enabled to track database activities in real time. The system architecture is implemented using native Azure SQL features. Controlled experiments are conducted to evaluate access behavior. Compliance alignment is validated through policy mapping. Results: The results show a significant reduction in unauthorized data visibility. Masked users could only view obfuscated data, while privileged users retained full access. Audit logs successfully captured all access attempts and query executions. Performance overhead introduced by masking and auditing remained minimal. The framework improved compliance traceability. Overall system security posture was strengthened. Conclusion: This study demonstrates that combining Dynamic Data Masking with auditing provides an effective security solution for Azure SQL. The framework ensures regulatory compliance without degrading database performance. It offers scalable and flexible security controls. Organizations can adopt this approach to enhance data governance. Future enhancements may include automation and AI-based threat detection. The framework serves as a practical reference for secure cloud data access.