A Data-Sharing Model to Secure Borders Using an Artificial-Intelligence-Based Risk Engine and Big-Data Concepts

Abstract

The primary aim of this research is to develop a framework for data management and sharing that will enable countries to share complex data about known and unknown high-risk passengers to streamline border-control security processes through the use of big data analytics and Artificial Intelligence (AI). A total of 15 semi-structured interviews were used to gather qualitative data. A thematic analysis approach was used to analyze the data and the interview data were coded using NVivo 11 qualitative-data-analysis software. Five aggregate dimensions were developed, comprising nine themes and nine sub-themes, based on 39 codes that emerged from the data. This research has several theoretical and practical contributions. Primarily, the development of an AI-based risk engine will not only improve how borders are enforced but will also lead to the integration of new technology for border control, thus boosting securitization, decreasing human factors/error, and minimizing border-related crime, and helping to manage healthcare issues.