Adaptive Authorization through Transformer-Based Learning

Access control governs how entities interact with protected resources and remains a central pillar of modern cybersecurity. Classical authorization frameworks such as DAC, MAC, RBAC, and ABAC provide structured and interpretable policy foundations but depend heavily on manual policy and attribute engineering, making them increasingly difficult to maintain in large, dynamic environments. As organizational roles, contextual attributes, and system conditions evolve, policy drift, privilege accumulation, and configuration inconsistencies emerge, weakening security posture. Recent advances in machine learning (ML) offer the ability to automate authorization by learning predictive relationships from user, resource, and contextual metadata. However, progress is constrained by the scarcity of real-world datasets, inconsistent evaluation methodologies, and challenges associated with heterogeneous or incomplete attribute sets.Motivated by these limitations, this work focuses on a synthetic data generation and evaluation framework for access-control models. Leveraging domain-informed synthetic datasets that emulate realistic healthcare authorization conditions including role hierarchies, permission distributions, and anomaly patterns, we systematically evaluate transformer models against traditional machine-learning approaches. Results show that while tree-based ensembles perform strongly in low-noise, low-data environments, transformer-based and other neural architectures scale more robustly as data scale and complexity grows. These findings reinforce emerging evidence that modern attention-based models offer promising advantages for adaptive, data-driven authorization in real-world environments.

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