Edge data analytics refers to processing near data sources at the edge of the network to reduce delays in data transmission and, consequently, enable real-time interactions. However, data analytics at the edge introduces numerous security risks that can impact the data being processed. Thus, safeguarding sensitive data from being exposed to illegitimate users is crucial to avoiding uncertainties and maintaining the overall quality of the service offered. Most existing edge security models have considered attacks during data analysis as an afterthought. In this paper, an overview of edge data analytics in healthcare, traffic management, and smart city use cases is provided, including the possible attacks and their impacts on edge data analytics. Further, existing models are investigated to understand how these attacks are handled and research gaps are identified. Finally, research directions to enhance data analytics at the edge are presented.

The increase in social-engineering threats within the Saudi public sector has changed awareness and training methods. However, due to employees' lack of awareness, social engineering could lead to a breach whereby attackers identify vulnerabilities and subsequently launch their attacks. A social-engineering attack is a high risk to the Saudi public sector and may significantly affect its security measures. Thus, the benefits of adopting awareness-enhancement tools in the public sector are undeniable. This study proposes a conceptual awareness model designed to enhance employee awareness in the Saudi public sector to address this issue. This study reviews seven main factors of social engineering risk: phishing, baiting, pretexting, quid pro quo, tailgating, related security policies, and the ability to identify attacks and respond to threats. Additionally, this research examines one public sector actor in Saudi Arabia as a case study. The findings led to a model creation comprising of five components: a situation-awareness model for phishing, an information-security awareness tool, a power-knowledge-practice triangle, Saudi public sector follow-up metrics, and implementation phases. As a result, an a priori model was successfully developed, tested, and applied in the subsequent stage by the case study participants, the employees.