A data-sharing model to secure borders using an artificial intelligence based risk engine and big-data concepts

Purpose: The primary aim of this research is to propose a framework for the development of an AI based data management and sharing that will enable countries to share complex data about known and unknown high-risk passengers in order to streamline border-control security processes through the use of big data analytics and artificial intelligence (AI). In line with this aim, the following objectives were outlined: to understand and evaluate the current bordersecurity processes that are used to screen known and unknown risky passengers; to identify the challenges in data sharing for known and unknown risky passengers due to differing datasharing laws; and to propose a data-sharing model that mitigates common data-sharing problems, enabling border security to be strengthened and facilitating the identification both of known and unknown passengers. Methodology/approach: In order to achieve the purpose stated above, 15 semi-structured interviews were used to gather qualitative data. A thematic analysis approach was used to analyze the data; the interview data were coded using NVivo 11 qualitative-data-analysis software. Findings: A total of five aggregate dimensions were developed with a total of nine themes and nine sub-themes. These comprised 39 codes that emerged from the data. The following themes were identified: manual processes are conducted; biometrics; automated border control (ABC) gates are employed and are important; identification of known risky passengers; identification of unknown risky passengers; lack of data-sharing agreements between countries; no framework or model exists for sharing data; optimizing healthcare-information sharing; and potential for using big data and AI. The findings suggest that an AI risk engine is the answer to data-sharing problems, the strengthening of border security, simplifying passenger trips, and detecting unknown risky passengers. The inclusion of an AI-based risk engine on top of the Interpol platform is the most important component as it allows for data sharing while complying with privacy regulations. This may be accomplished by transferring healthcare data straight from the travelers’ nation of departure to the new platform. The utilization of contemporary technologies, such as big data and AI, will be critical in the future of border control. An AI-based risk engine could provide color-coded information without revealing specific details of the traveler. Airports and border-control authorities must work closely together to enhance border security. Overall, it was identified that we can enhance border- iv control security while simultaneously enhancing the passenger experience by using AI and big data. Research contributions: The current research offers many theoretical contributions. First, the study’s main contribution is that it has brought together the research fields of border security and modern technology. Common difficulties in border research and practice may therefore be minimized, and new research avenues can be highlighted as a result. More precisely, border security research has not yet incorporated AI and big-data concepts into the research reasoning. As a result, this study not only improves knowledge of border security but also provides greater insight into the potential for incorporating AI and big-data-related research ideas into bordersecurity research. Second, this study addresses a major theoretical gap; no previous research has established a framework or offered a solution to typical risk-management problems for border security and control. One of the main causes for this disparity is the data-sharing difficulties that commonly arise across nations, whereby one country may lack the legal framework to exchange data about its people without infringing their fundamental human right to privacy. Furthermore, the difficulties of data sharing are among some of the common theoretical concerns highlighted in previous studies that hinder the strengthening of borders across the globe. Third, this study improves knowledge of how boundaries are enacted across the globe by presenting a unified theoretical perspective of the commonly utilized security methods. A gap in the literature was identified since previous studies have not offered a clear perspective of what currently happens in terms of border security and what might be done to improve the process. Finally, this study examines border security and control as a whole, rather than focusing on a specific setting. Much previous research has been done in a contextual setting (e.g. the US or the UK), which restricts the debate to a certain geographical area, thus limiting theory development to a specific context. In contrast, this study not only examines the entire holistic environment, but also develops a generalized border-management framework that takes into account sea, land, and air borders. This is a unique addition of this study. The current research has broader management implications and contributions. First, the research develops a practical methodology for facilitating data exchange in a complicated legal context while ensuring that no privacy rules are broken. Second, another practical benefit of the study is the simplification of border operations. For example, by implementing an AI-based risk engine at the border, long procedures that certain crossings entail may be avoided, ensuring that passengers have a smoother trip. The consequences will be similar to those of preclearance, in which previously designated non-risky passengers or regular travelers are exempt v from rigorous border-security procedures. This may also have resource consequences, as additional resources might need to be allocated to security in order to identify unknown risky passengers. Finally, the development of an AI-based risk engine, based on the high-level proposal presented in this study, 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, minimizing border-related crime, and managing healthcare issues.