1 Department of Computer Science, Faculty of Physical Sciences, University of Calabar, Cross-River State, Nigeria.
2 Department of Computer Science, Federal School of Statistics, Amechi Uno, Awkunanaw, Enugu, Enugu State, Nigeria.
3 Department of Computer Science, Federal School of Statistics, Sasha Ajibode Road, Ibadan, Oyo State, Nigeria.
International Journal of Science and Research Archive, 2025, 14(01), 890-897
Article DOI: 10.30574/ijsra.2025.14.1.2650
Received on 03 December 2024; revised on 11 January 2025; accepted on 13 January 2025
In the evolving landscape of cyber threats, traditional threat intelligence methods are increasingly inadequate for addressing the complexity and speed of modern attacks. This paper explores the transformative impact of Artificial Intelligence (AI) on enhancing cyber security threat intelligence and situational awareness. By leveraging advanced AI technologies—such as machine learning, natural language processing, and data analytics—organizations can significantly improve their ability to detect, analyze, and respond to threats. We provide a comprehensive review of current AI applications in threat intelligence, illustrating how these technologies enable proactive threat management and enhance situational awareness. Through detailed case studies, we demonstrate the effectiveness of AI-driven solutions in various sectors, including finance and healthcare. The paper also addresses key challenges such as data privacy, system integration, and adversarial AI, offering recommendations for future research and development. This study underscores the critical role of AI in advancing cyber security practices and provides insights into how organizations can harness AI to achieve a more robust and responsive threat intelligence framework.
AI; Cyber Security; Threat Intelligence; Situational Awareness; Machine Learning; Data Analytics
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Edim Bassey Edim, Akpan Itoro Udofot, and Omotosho Moses Oluseyi. AI-augmented cyber security threat intelligence – enhancing situational awareness. International Journal of Science and Research Archive, 2025, 14(01), 890-897. Article DOI: https://doi.org/10.30574/ijsra.2025.14.1.2650
Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0







