<![CDATA[Role-Based Access Control (RBAC) has become the main approach in improving data security in various information systems. This study analyzes the implementation of RBAC in the context of Enterprise Resource Planning (ERP) applications and cloud-based, mobile, and multi-domain systems. Using a systematic literature review (SLR) methodology, this study synthesizes findings from various studies to evaluate the effectiveness of RBAC in addressing challenges such as data privacy, regulatory compliance, and access policy complexity. The results show that the integration of intelligent technologies, such as machine learning (decision tree and random forest algorithms) for user behavior analysis, natural language processing for policy interpretation, and blockchain to record access activities with a security increase of up to 37%, can increase the flexibility and efficiency of RBAC, especially in detecting anomalies and managing dynamic policies. In addition, automation in RBAC deployments has been proven to reduce operational costs by 42% and management time by up to 65% compared to traditional manual approaches. However, RBAC implementation also faces significant challenges, including the need to adapt to complex regulations and the dynamics of a multi-domain environment. This research makes a theoretical contribution by expanding the understanding of the role of RBAC in modern data security management and offering practical recommendations for optimizing RBAC implementation. Thus, RBAC has proven to be a relevant and reliable model in answering data security needs in the digital era. ]]>
