Cutting-edge advances in AI and ML for cybersecurity: a comprehensive review of emerging trends and future directions
Nachaat AbdElatif Mohamed · Cogent Business & Management · 2025
The rapid evolution of cyber threats has accelerated the adoption of Artificial Intelligence (AI) and Machine Learning (ML) in cybersecurity. This review explores the latest advancements in these technologies, focusing on emerging trends, key challenges, and future directions. It highlights developments in AI-driven threat detection, adversarial machine learning, federated learning, and explainable AI. The integration of AI with blockchain, the Internet of Things (IoT), and quantum computing is also discussed, showing how these combinations are strengthening cybersecurity defenses. Particular attention is given to explainable AI, which improves transparency, and federated learning, which enables decentralized security while preserving data privacy. The review further examines AI’s role in securing IoT ecosystems and assesses how quantum computing may disrupt traditional cryptographic methods. Despite their promise, AI and ML in cybersecurity face challenges such as ethical concerns, scalability, and the growing complexity of cyber threats. These issues call for continued research and development. The paper suggests future directions that emphasize interdisciplinary collaboration, sustainable AI models, and strong ethical and regulatory frameworks. This review serves as a valuable resource for researchers, practitioners, and policymakers by offering a detailed overview of how AI and ML are transforming cybersecurity in today’s increasingly complex digital landscape.