AI-Driven Cybersecurity: A Review of Threat Detection, Prevention, and Challenges
Abstract
The growing sophistication of cyber threats has necessitated the adoption of artificial intelligence (AI) in cybersecurity. This paper provides a detailed review of AI-based methods for threat detection, prevention, and response. We discuss techniques such as anomaly detection, intrusion detection systems, and predictive analytics, emphasizing their role in mitigating risks. The review also explores challenges, including adversarial attacks, data quality, and the need for explainable AI in cybersecurity. Case studies of AI implementation in real-world scenarios are presented, offering insights into its potential and limitations.
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