Abstract
The study aims to investigate how AI methods can be used to improve risk assessment in the field of physical security. A quantitative approach was applied in this study, where data was collected from 160 individuals working in this sector, and their responses were processed and evaluated using modern statistical analysis techniques. The results showed that AI-enhanced risk assessment plays a crucial role in reducing human errors, as AI contributes to eliminating biases and shortcomings caused by human factors, which enhances the accuracy of predictions and the effectiveness of decisions taken in the context of risk management. The results revealed that AI increases the response of security teams to risks, by providing a comprehensive and in-depth analysis that enables them to take quick and intelligent measures based on actual facts, which enhances the efficiency of security operations. It has also been proven that the application of AI contributes to the development of security sustainability, as it enables continuous analysis and adjustment of security methods to suit changing threats. The study emphasizes the need to integrate AI into risk management strategies, which enhances the ability of organizations to successfully solve security problems and enhances long-term sustainability. The results highlight the need to enhance training and development for workers in this sector to ensure the best use of contemporary technology, which contributes to increasing the effectiveness and sustainability of physical security.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: International Journal of Financial, Administrative, and Economic Sciences
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.