A Review of AI in Cybersecurity: Applications, Challenges, and Future Directions

The growing complexity of cyber threats has made traditional defense strategies inadequate for modern enterprises. Artificial Intelligence (AI) has emerged as a transformative force in cybersecurity by enabling real-time threat detection, advanced malware analysis, phishing prevention, and automated incident response. AI-driven systems leverage machine learning and deep learning to analyze massive volumes of data, identify anomalies, and adapt to evolving attack patterns faster than human analysts. This paper provides a comprehensive review of AI applications in cybersecurity, highlighting its role in intrusion detection systems, user behavior analytics, and security operations automation. While AI offers significant benefits such as scalability, predictive defense, and reduction of human error, challenges remain in the form of adversarial attacks, data privacy concerns, and model bias. The paper concludes with a discussion on future directions, including the integration of AI with Zero Trust models, privacy-preserving approaches such as federated learning, and the potential impact of quantum computing on AI-driven security.