Artificial Intelligence-Driven Cybersecurity: A Review of Modern Techniques and Future Directions

This article presents a comprehensive review of the application of artificial intelligence (AI) in cybersecurity, with a focus on how AI is reshaping defense strategies in an era of increasingly sophisticated cyber threats. Traditional cybersecurity approaches have relied heavily on reactive mechanisms, detecting and responding to attacks after they occur. However, the dynamic nature of modern threat landscapes—including zero-day exploits, advanced persistent threats, and AI-powered offensive tools—demands a shift toward proactive, adaptive, and intelligence-driven defense systems. AI offers this paradigm shift by enabling predictive analytics, anomaly detection, and behavioural analysis that can anticipate, identify, and mitigate attacks in real time.

We examine the theoretical foundations and practical implementations of AI-driven security systems across domains such as intrusion detection, malware classification, fraud prevention, and automated incident response. Special emphasis is placed on machine learning, deep learning, and graph-based models that extend detection capabilities to complex, multi-stage attacks. The review also interrogates key challenges limiting operational effectiveness, including the vulnerability of AI models to adversarial attacks, data poisoning, and evasion strategies that exploit algorithmic blind spots. Equally critical are concerns around transparency, accountability, and interpretability, as security practitioners increasingly require explainable AI (XAI) tools to ensure trust, compliance, and human–AI collaboration.

Looking forward, we highlight emerging research trends that hold promise for strengthening AI-driven cybersecurity. These include the development of robust adversarial defense mechanisms, the integration of causal and explainable modelling, the adoption of federated learning for privacy-preserving collaborative defense, and the growing role of automation in threat hunting, digital forensics, and response orchestration. By synthesizing the latest advances, this article underscores both the transformative potential and the inherent risks of applying AI in cybersecurity. We argue that realizing this potential requires interdisciplinary approaches that bridge technical innovation, policy, and human factors. Ultimately, AI has the capacity not only to enhance detection and resilience but also to redefine the global cybersecurity landscape, provided that challenges of robustness, interpretability, and governance are systematically addressed.