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As the global trade environment becomes more interconnected and the movement of people and goods across borders increases in complexity, detecting threats to national security becomes more difficult. A central goal of every border security agency’s modernization agenda is to use innovation and technology to improve the ability to protect society. The obligation to modernize is partially driven by the rapidly evolving nature of the threat landscape – use of new technology is critical to ensure agencies are ahead of, or at least keeping up with, their adversaries’ adaptability – but it can also reflect political and budgetary change, meaning the perennial pressures to do more with less.
With the push to innovate and streamline, it is no surprise there is keen interest in the potential of Artificial Intelligence (AI) to enhance scanning and threat detection. On paper, the automation of decisions would enhance both mission effectiveness and governance.
But if AI is the future, the path to get there rests heavily on each agency’s starting point. AI’s success should be built upon a solid foundation of appropriate inputs, including a significant body of scanned images and the appropriate algorithms to guide machine learning towards reasonable, accurate outcomes.
A key factor is developing a clear operational direction. While many border security agencies share a common overarching mission, the specifics will be impacted by political, financial and physical influences unique to each. Furthermore, they face adaptable adversaries, constantly developing methods to exploit weaknesses.