Artificial Intelligence in Cybersecurity

Artificial Intelligence (AI) is radically reshaping cybersecurity by enabling data-driven threat analysis and response capabilities that far surpass traditional, signature-based methods. Machine learning and deep learning techniques now allow systems to sift through massive security logs and network data automatically, detecting attacks in real time and at scale. This review surveys how AI methods are integrated across security tools – from automated intrusion detection and malware analysis to advanced threat intelligence platforms. We highlight recent advances (such as deep neural networks for pattern recognition, reinforcement learning for adaptive defenses, and explainable AI for transparent alerts) and summarize how AI models are evaluated (accuracy, false-positive rate, detection latency, etc.). We also discuss representative deployments of AI in practice, compare recent research developments, and address current challenges (including adversarial attacks on models, data bias, and interpretability issues). Finally, we outline promising directions like federated learning for collaborative defense and robust AI governance. In conclusion, AI offers a transformative toolkit for proactive security, but realizing its full potential requires ongoing innovation and careful oversight.