Paper Key : IRJ************698
Author: Oluwabiyi Oluwawapelumi Ajakaye
Date Published: 05 Apr 2025
Abstract
The rapid integration of artificial intelligence (AI) into cybersecurity has catalyzed a new dimension of digital conflictone characterized by the continuous evolution of AI-powered cyber offense and AI-driven defense. As cyber threats become increasingly sophisticated, adversaries are leveraging machine learning, generative models, and automation to execute real-time attacks that adapt, learn, and evade traditional detection systems. In parallel, defenders are deploying AI systems to monitor vast data streams, detect anomalies, and orchestrate automated responses faster than human operators ever could. This dynamic interaction represents the core of what is now described as the Cyber-AI arms race. This paper investigates the growing competition between offensive and defensive AI systems, analyzing key advancements in adversarial machine learning, deepfake exploitation, intelligent malware, and AI-assisted threat hunting. It explores how nation-states, cybercriminal groups, and private enterprises are leveraging AI not only for defense but also to develop offensive capabilities that can target critical infrastructure, financial systems, and political institutions. The dual-use nature of AI technologies raises ethical and regulatory challenges, as the line between innovation and weaponization continues to blur. The study also considers the geopolitical implications of this arms race, particularly its impact on global cybersecurity governance, deterrence strategies, and technological sovereignty. By evaluating real-world case studies, defense frameworks, and emerging international policy responses, the paper provides a forward-looking perspective on how AI may shape cyber power dynamics, risk landscapes, and the stability of global digital ecosystems. The findings underscore the urgency of collaborative governance and the need for responsible AI development to prevent catastrophic escalations in the cyber domain.
DOI LINK : 10.56726/IRJMETS71715 https://www.doi.org/10.56726/IRJMETS71715