AI-Driven Cybersecurity: Threat Detection, Prevention, and Autonomous Defense

Abstract

The rapid expansion of digital ecosystems has intensified cyber threats, exposing the limitations of traditional, signature-based security systems. Artificial Intelligence (AI) has emerged as a transformative enabler of advanced cyber defense, offering adaptive, scalable, and autonomous security capabilities. This review provides a comprehensive synthesis of AI-driven cybersecurity mechanisms across three core defense layers, threat detection, proactive prevention, and autonomous response. Deep learning architectures including CNNs, LSTMs, GRUs, hybrid CNN-BiLSTM models, and self-normalizing networks have significantly improved intrusion detection accuracy and reduced false positives. Predictive Vulnerability Exploitation (PVE) models, such as EPSS, enhance vulnerability prioritization by quantifying real-world exploit likelihood. Autonomous defense frameworks, powered by Deep Reinforcement Learning (DRL) and agent-based Large Language Models (LLMs), enable zero-day attack detection, dynamic playbook generation, and zero-shot incident response. However, challenges such as adversarial machine learning, model poisoning, bias, privacy concerns, supply-chain insecurity, and dual-use risks remain substantial barriers to trustworthy deployment. Future directions emphasize federated learning, privacy-preserving intelligence sharing, post-quantum security integration, and neuromorphic hardware for ultra-low-latency edge defense. Overall, AI has shifted cybersecurity from reactive monitoring to predictive and autonomous protection, marking a foundational transformation in digital defense ecosystems.