Applications and Challenges of Artificial Intelligence in Cybersecurity

Abstract

Artificial Intelligence (AI) has emerged as a transformative tool in the cybersecurity domain, addressing the increasing sophistication and  scale of cyber threats. This study explores the role of AI in cybersecurity, focusing on its applications in network threats and anomaly  detection, malware identification, phishing prevention, and automated incident response. The purpose of the research is to evaluate AI's  effectiveness in enhancing cybersecurity frameworks and to identify challenges that may limit itsimplementation. Thestudy  highlightstheinadequacy of traditional methods,such as rule-based systems, in keeping pace with evolving threats. Using a combination  of literature review and practical analysis, the research examines machine learning, deep learning, and natural language processing techniques to understand their capabilities in real-time threat detection, anomaly identification, and predictive analytics. Key findings  reveal that AI significantly improves threat detection accuracy, reducesfalse positives, and acceleratesresponsetimes. However, issues  such as data quality, algorithmic bias, adversarial attacks, and ethical concerns persist as critical challenges. The implications of this study  emphasize the need for ethical frameworks, robust AI model training, and interdisciplinary collaboration to maximize AI's  potentials in cybersecurity. This research provides actionable insights for enhancing cyber defenses and underscores AI's pivotal role in  shaping future security strategies.