# Analysis of Azure Zero Trust Architecture Implementation for Mid-Size Organizations
## 2 Related Work
A subsequent study ought to investigate the application of ML models in enhancing Azure's Conditional Access, particularly with anomaly detection and policy enforcement. Investigations into trust evaluation frameworks inside community cloud settings provide an understanding of dynamic trust administration. A ZTA-based approach for business data sharing in community clouds, highlighting the necessity of continual trust recalibration to safeguard critical information, was also researched [17]. Although these notions are beneficial, the scalability of these models still needs to be better comprehended, and empirical validation needs to be more frequent. Azure ZTA encounters comparable difficulties in overseeing continual trust recalibration, particularly as its PIM and JIT Access mechanisms depend on dynamic trust updates. Mitigating scalability and performance limitations is crucial to guaranteeing that trust recalibration does not adversely affect cloud operations. The security issues presented by wearable IoT devices have resulted in the creation of lightweight, real-time access management systems grounded in ZTA principles [18]. This method prioritizes resource-efficient design and compatibility with devices possessing constrained computing capabilities. Real-time access control and monitoring are essential for IoT and pertinent to Azure ZTA concepts. Azure Conditional Access policies are engineered to react instantaneously to alterations in device compliance, user risk, and geographical location. The scalability issues identified in IoT-based ZTA research are similarly evident in Azure ZTA, necessitating the management of network traffic surges without compromising performance. The incorporation of AI into ZTA frameworks has been analyzed for the automation of trust assessments, improvement of anomaly detection, and optimization of security protocols [19]. AI-driven automation facilitates adaptive risk evaluations and policy formulation, which is crucial for Azure's Conditional Access and Just-in-Time Access. Integrating AI models into IT infrastructure presents hurdles, especially in hybrid cloud systems. The possibility of algorithmic bias in anomaly detection algorithms is a significant worry, as it may result in false positives and negatives. The research findings are relevant to Azure ZTA, where impartial AI-driven anomaly detection may enhance the precision of Conditional Access determinations and policy implementation. Investigations on ZTA for cloud-native architectures underscore the necessity for secure communication and adaptive load balancing in microservices-based systems [20]. These systems must accommodate various network conditions while guaranteeing secure communication among microservices. The difficulties of system bottlenecks during peak traffic times are especially pertinent to Azure ZTA, where Conditional Access and JIT Access policies must be implemented effectively, even under maximum load conditions. Future studies may investigate applying ML models and automated resource allocation to Azure ZTA to enhance system performance while ensuring robust security. The SysFlow framework provides a system-level application of Zero Trust principles, emphasizing internal systems over network-level access control [21]. SysFlow facilitates the customization of ZTA policies for organizations by offering programmable security rules tailored to their specific requirements. The intricacy of programming these regulations presents an obstacle to widespread implementation. Azure mitigates this difficulty via its RBAC (Role-Based Access Control) and Conditional Access, enabling administrators to establish policies through graphical user interfaces (GUIs) rather than coding. SysFlow's focus on programmability suggests that Azure ZTA may be improved by facilitating more intuitive policy customization, possibly via low-code interfaces. RBAC is frequently deemed adequate for numerous ZTA implementations.