Authors : Evans Momanyi Getembe

Volume/Issue : Volume 10 - 2025, Issue 3 - March

Google Scholar : https://tinyurl.com/2eas6x6x

Scribd : https://tinyurl.com/2ebj95y3

DOI : https://doi.org/10.38124/ijisrt/25mar574

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Abstract : Border porosity remains a significant challenge in Kenya, particularly along the Kenya-Uganda borderline, leading to revenue leakages, illegal trade, and security threats. Traditional border management approaches have proven inadequate in curbing smuggling and enhancing revenue collection. This study explores how digital surveillance technologies can strengthen border control, increase revenue collection, and promote sustainable economic growth. The research examines key digital surveillance tools, including sensor-based detection, drone surveillance, and computer vision systems, which enable real-time monitoring and rapid response to illegal crossings. These technologies improve the enforcement of customs regulations, ensuring that tax revenues from legitimate trade are maximized. Additionally, securing border trade routes fosters economic stability by creating a favorable environment for businesses and employment opportunities in border communities. Despite the benefits, implementing digital surveillance raises concerns about data privacy, high infrastructure costs, and potential misuse of surveillance systems. Striking a balance between national security and civil liberties remains a critical challenge. The study recommends the adoption of integrated surveillance systems, government investment in border technology, and policy frameworks that ensure ethical and effective use of digital monitoring tools. By leveraging advanced surveillance technologies, Kenya can significantly reduce border porosity, enhance revenue collection, and promote long-term economic growth while maintaining a secure and regulated trade environment.

References :

1. Andreas, P. (2003). Redefining Border Security in the Age of Globalization. International Security, 28(2), 78-111.
2. Chene, M. (2018). Use of Technology in Border Control. Transparency International.
3. Gonzalez, M. (2021). EU Smart Borders Initiative: A Model for Digital Border Management? European Journal of Security Studies, 14(2), 65-87.
4. Gonzalez, M. (2021). EU Smart Borders Initiative: A Model for Digital Border Management? European Journal of Security Studies, 14(2), 65-87.
5. Jones, R., & Sullivan, P. (2020). AI and Surveillance in US Border Security Policies. American Security Review, 12(3), 98-120.
6. Kimutai, P. (2021). Cross-border Trade and Revenue Collection in East Africa. Journal of African Trade, 8(2), 45-60.
7. Korkmaz, M. (2024). Digital Surveillance and Revenue Collection in Emerging Economies: A Case Study Approach. Journal of Economic Policy, 17(1), 55-72.
8. Korkmaz, T. (2024). Enhancing Border Security Through Digital Innovation: Economic Implications and Policy Recommendations. Journal of Trade and Security, 20(1), 55-73.
9. Marino, J. (2021). Automated Border Control: Enhancing Security and Efficiency. Journal of Border Security Studies, 15(2), 45-60.
10. Marino, J. (2021). Privacy in the Age of Digital SSurveillance: Balancing Security and Rights. Cybersecurity Journal, 9(3), 112-129.
11. Mutabazi, C. (2019). Blockchain and Trade Facilitation in Rwanda: A Border Management Approach. African Journal of Trade, 7(1), 45-66.
12. Njuguna, F., & Wekesa, C. (2020). Border Security and Economic Development in Kenya. African Journal of Policy Studies, 12(1), 112-130.
13. Sharma, P., Gupta, R., & Patel, T. (2021). Drones and Sensor-Based Monitoring in Border Security. International Journal of Security Studies, 19(2), 75-93.
14. Smith, J., Brown, T., & Patel, R. (2022). The Role of AI in Border Management. International Security Review, 15(3), 78-95.
15. Tanzi, V., & Zee, H. (2000). Tax Policy for Emerging Economies: Enhancing Revenue Collection and Economic Growth. IMF Working Paper Series.
16. The Digital Border. (2022). AI and Machine Learning in Border Security: Trends and Challenges. Border Security Review, 18(1), 88-102.
17. Weeraratne, S. (2020). Porous Borders and Illicit Trade: Challenges in Border Control. Global Security Journal, 22(4), 67-81.
18. Wilson, J., Mann, C., & Otsuki, T. (2005). Trade Facilitation and Economic Development: A Global Perspective. World Bank Publications.
19. World Bank. (2023). Leveraging Digital Technologies for Trade Facilitation in Africa. Washington, DC: World Bank Publications.
20. Z, A., Lee, B., & Chan, M. (2020). Computer Vision for Automated Border Surveillance: AI in Action. International Journal of AI and Security, 14(4), 145-163.
21. Z, X., Y, B., & W, C. (2020). Drones and Sensor Networks for Border Surveillance: A Case Study on Emerging Technologies. Technology and Security Journal, 12(5), 33-49.