Abstract
The rapid integration of Artificial Intelligence (AI) in cybersecurity has redefined traditional security protocols, providing advanced mechanisms to combat increasingly sophisticated cyber threats. This article investigates the transformative role of AI in enhancing cybersecurity by focusing on three core areas: threat detection, defence mechanisms, and risk management. The primary aim is to assess how AI technologies—specifically machine learning, deep learning, and natural language processing—can improve the detection, prevention, and mitigation of cyber threats beyond traditional methods. By leveraging AI-driven solutions, cybersecurity can anticipate emerging threats, quickly adapt defensive strategies, and significantly reduce response times. To achieve this, the study will analyse recent advances in AI applications within cybersecurity, using a systematic literature review and case study analysis. The literature review will highlight existing knowledge gaps, explore the current limitations of conventional cybersecurity measures, and identify how AI fills these gaps. Case studies of real-world AI deployment in cybersecurity will be critically examined to understand the practical effectiveness and challenges associated with these technologies. The expected results include an in-depth understanding of AI's specific advantages in threat detection accuracy, predictive analysis, and anomaly identification. Furthermore, we anticipate uncovering key limitations and ethical considerations, such as data privacy issues, potential biases in AI algorithms, and the risk of adversarial attacks exploiting AI vulnerabilities. By examining these aspects, the article aims to present a balanced perspective on the future of AI in cybersecurity, suggesting critical improvements and policy recommendations.
Published Version
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