ISSN (Online): 2321-3418
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Engineering and Computer Science
Open Access

Bridging Borders with AI: Enhancing Global Cybersecurity Through Intelligent Threat Detection

DOI: 10.18535/ijsrm/v13i06.ec11· Pages: 2352-2363· Vol. 13, No. 06, (2025)· Published: June 28, 2025
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Abstract

With increasing global interconnectedness and digitization of the world, which is affecting everything from critical infrastructure to personal communication, cybersecurity has become a critical issue for humanity. A significant increase in the intelligence and the number of cyber incidents - that could be from state-sponsored agents or from various criminal groups - requires a fast and united global reaction. Old-fashioned security frameworks usually let down under the conditions of a large scale and complexity of modern cyber threats.

In the current situation, AI is the leading entity capable of revolutionizing cybersecurity worldwide by clever threat detection systems. With the help of machine learning, natural language processing, and predictive analytics, AI-powered platforms can detect deviations, analyze the potential threat, and trigger immediate responses to a much greater extent than human analysts can. Moreover, cybersecurity is a borderless thing, hence due to the continuous nature of cybercrime, this necessitates cross-national collaboration—a process where AI can also be of help by providing common intelligence, matching defense protocols, and joint reactions.

Firstly, the paper discusses the topic of the AI of raising global cybersecurity due to its smart threat detection capacity. This research outlines the various implementations, global cooperation models, and AI-assisted cybersecurity projects showhow AI incites not only operational efficiency but also international trust and interoperability. Secondly, it explores the challenges of ethics, data privacy, and the need for transparent algorithmic governance. It ends with the provisions for progressing AI integration in the global cybersecurity ecosphere. Hence, it illustrates the significance of AI as a means of bridging national borders to form a cyber-secure world for everyone.

Keywords

Artificial IntelligenceCybersecurityGlobal CollaborationThreat DetectionMachine LearningCybercrimeCross-Border SecurityIntelligent SystemsData AnalyticsCyber Defense

References

  1. Anderson, R. (2022). Security Engineering: A Guide to Building Dependable Distributed Systems (3rd ed.). Wiley.Google Scholar ↗
  2. IBM Security. (2023). AI and Cybersecurity: Building a Smarter Defense. Retrieved from https://www.ibm.com/securityGoogle Scholar ↗
  3. EUROPOL. (2023). Internet Organised Crime Threat Assessment (IOCTA). Retrieved from https://www.europol.europa.euGoogle Scholar ↗
  4. Microsoft. (2023). Introducing Microsoft Security Copilot. Retrieved from https://www.microsoft.com/securityGoogle Scholar ↗
  5. INTERPOL. (2024). AI in Cybercrime Investigations. Retrieved from https://www.interpol.intGoogle Scholar ↗
  6. OECD. (2021). OECD Principles on Artificial Intelligence. Retrieved from https://www.oecd.org/ai/Google Scholar ↗
  7. Darktrace. (2024). Enterprise Immune System: AI for Cyber Defense. Retrieved from https://www.darktrace.comGoogle Scholar ↗
  8. United Nations Office on Drugs and Crime (UNODC). (2022). The Use of AI in Fighting Cybercrime. Retrieved from https://www.unodc.orgGoogle Scholar ↗
  9. National Institute of Standards and Technology (NIST). (2023). Artificial Intelligence Risk Management Framework (AI RMF 1.0). Retrieved from https://www.nist.govGoogle Scholar ↗
  10. Zeguro. (2023). How AI Enhances Cybersecurity Threat Detection. Retrieved from https://www.zeguro.comGoogle Scholar ↗
  11. Bostrom, N., & Yudkowsky, E. (2014). The Ethics of Artificial Intelligence. In K. Frankish & W. Ramsey (Eds.), The Cambridge Handbook of Artificial Intelligence. Cambridge University Press.Google Scholar ↗
  12. Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach (4th ed.). Pearson.Google Scholar ↗
  13. Symantec. (2023). Internet Security Threat Report. Retrieved from https://www.broadcom.com/company/newsroom/press-releasesGoogle Scholar ↗
  14. McKinsey & Company. (2023). The State of AI in 2023: Cybersecurity Trends. Retrieved from https://www.mckinsey.comGoogle Scholar ↗
  15. Google Cloud. (2024). Securing the Cloud with AI and ML. Retrieved from https://cloud.google.com/securityGoogle Scholar ↗
  16. World Economic Forum. (2023). Global Cybersecurity Outlook. Retrieved from https://www.weforum.orgGoogle Scholar ↗
  17. Capgemini Research Institute. (2020). Reinventing Cybersecurity with Artificial Intelligence. Retrieved from https://www.capgemini.comGoogle Scholar ↗
  18. Kaspersky Lab. (2023). AI in Cybersecurity: Trends and Challenges. Retrieved from https://www.kaspersky.comGoogle Scholar ↗
  19. Chio, C., & Freeman, D. (2018). Machine Learning and Security: Protecting Systems with Data and Algorithms. O’Reilly Media.Google Scholar ↗
  20. MIT Technology Review. (2023). How AI is Shaping the Future of Cybersecurity. Retrieved from https://www.technologyreview.comGoogle Scholar ↗
  21. Gartner. (2024). AI and Cybersecurity: Hype vs. Reality. Retrieved from https://www.gartner.comGoogle Scholar ↗
  22. ENISA (European Union Agency for Cybersecurity). (2023). Artificial Intelligence Threat Landscape. Retrieved from https://www.enisa.europa.euGoogle Scholar ↗
  23. IEEE. (2021). Artificial Intelligence for Cybersecurity Applications: Challenges and Opportunities. IEEE Access, 9, 150241–150267.Google Scholar ↗
  24. Palanichamy, Y., & Tiwari, A. (2022). AI-Driven Threat Intelligence: A Review. Journal of Cyber Security Technology, 6(1), 45–62.Google Scholar ↗
  25. Future of Life Institute. (2023). Policy Considerations for Global AI Collaboration in Security. Retrieved from https://futureoflife.orgGoogle Scholar ↗
Author details
Goutham Sunkara
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