ork traffic between end-users and operators to minimize the potential for data breaches and damage. As requirements and risks change, these techniques should be versatile enough to be gradually strengthened over time. The techniques also to be agile enough to prevent man-in-the-middle attacks where, for example, a hacker might leavesdrop on the communication between the service providers who were transferring data using 5G. 4.2.3 AI ORCHESTRATION Machine learning (ML) and artificial intelligence AI need to play key roles in clas- sifying and moderating various cyber risks and in producing a high-level of smart decision making to manage and eliminate network-security threats [27]. With the expansion of 5G in the healthcare sector, government regulators and policy makers need to create algorithms to guard the privacy of patients. The ML and AI algorithms may be used for the analysis of telecommunication network-traffic, data packet inspection, risk analysis, and the isolation of network attacks [28]. With the expansion of 5G in the healthcare sector, government regulators and policy makers have to make systems intelligent in order to protect the privacy of patients. These measures should be aimed at providing interoperability and extending telecommunications systems, applications, programs, and electronic system whilst safeguarding doctor to patient data security and the privacy of medical data. Doctor to patient medical equipment, medical data computation devices, and communication networks must have strict privacy to provide patient control over the medical data generation, store, and transmission by 5G-powered devices [29]. 5G onsite-enabled use situations in smart healthcare system and their capabilities are shown in Table 4.1. In order to test for cyber security threats and confidentiality weaknesses in real- world smart healthcare use cases, it is necessary to establish parameters for testing all new devices and software. In addition, data security testing and approval systems for technologies under developme