AI and ML in Cyber Security: Emerging Trends and Future Directions

A comprehensive review published in Cogent Business & Management (2025) maps the fast-evolving landscape of AI and machine learning in cyber security, from real-time threat detection to the integration of AI with quantum computing and blockchain. As cyber threats grow in sophistication, the review argues that conventional rules-based security approaches are no longer sufficient; AI-driven systems now represent the backbone of modern cyber defense.

Technical Details 

The review identifies several cutting-edge developments reshaping the field. AI-driven Intrusion Detection Systems now utilise anomaly detection and generative adversarial networks to identify zero-day attacks, which are threats that evade signature-based methods entirely. Deep learning models, which process high-dimensional network and behavioral data across multiple layers, are proving particularly effective at detecting Advanced Persistent Threats: the stealthy, long-duration intrusions often utilised by state-sponsored actors.

Alongside detection, AI-powered Security Orchestration, Automation, and Response platforms can autonomously isolate compromised systems and update firewall rules in real time, dramatically reducing remediation time. The review also highlights Explainable AI as a critical priority, using interpretability techniques to address accountability concerns in regulated environments. Furthermore, while the convergence of AI with quantum computing presents a significant threat to current encryption standards, it also offers the opportunity to counter these risks through quantum-resistant cryptography and enhanced threat detection.

Why This Matters for Organisations Today 

For both producers and consumers of cyber security, the implications are immediate. The shift from reactive to proactive cyber security is already underway in sectors including finance, defense, and healthcare. Organisations with large Internet of Things networks face particular exposure as the volume of connected devices outpaces traditional tools; here, AI-driven edge computing is emerging as a scalable solution. However, the review also flags critical challenges: adversarial attacks designed to deceive AI models, the risk of algorithmic bias, and the growing burden of regulatory compliance. As the regulatory landscape tightens, the ability to audit AI-driven security decisions will become just as important as the decisions themselves.

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Source: Mohamed, N. (2025). Cutting-Edge Advances in AI and ML for Cybersecurity: A Comprehensive Review of Emerging Trends and Future Directions. Cogent Business & Management, 12(1). https://doi.org/10.1080/23311975.2025.2518496