Cybersecurity Trends: AI-Driven Threat Detection and Prevention

Abstract

The rapid development of cyber threats has prompted the need to use Artificial Intelligence (AI)-based methods to detect and prevent them effectively. This review paper investigates the new trends in cybersecurity that revolve around AI techniques, such as Machine Learning (ML), Deep Learning (DL), Reinforcement Learning (RL), Natural Language Processing (NLP), and expert systems. AI promotes cybersecurity by allowing real-time observation, behavioral content analysis, anomaly discovery, predictive analytics, automatic threat search, and intelligent response to incidents. The paper is a synthesis of the latest literature concerning AI applications in the field of intrusion detection, malware classification, phishing detection, insider threat analysis, and endpoint security. It also considers issues like false positives, adversarial attacks, scalability, privacy and lack of interpretability. The results suggest that AI-based cybersecurity solutions can provide substantial enhancement in proactive defense tools and decrease the response time and operational load. To enhance resilience against advanced and dynamic cyber threats, consistent innovation, elucidate artificial intelligence, and adaptive learning models are necessary.