Artificial intelligence for cybersecurity: Literature review and future directions

Abstract

As cyber threats continue to evolve at breakneck speed, you can also expect AI to play a key role in security solutions and influence how organisations collect information on past attacks and use it to fortify their defences. AI powered methods like machine learning, deep learning and natural language processing can predict threats in time at the same time they can scan huge and changing data sets to quickly spot anomalous behavior and take automated action. This paper reviews the latest progress on AI-inspired security defenses from 2018-2025, and covers a wide range of domains including intrusion detection mechanisms, malware classification, phishing detectors as well as behavioural analytics. The authors review various key frameworks and methods, including supervised/ unsupervised learning methods, hybrid analysis templates that help boost prediction accuracy by reducting false posotives. It also explores the increasingly important role of explainable AI (XAI) in providing trust and transparency for security operations, as well as adoption of federated learning to deliver privacy-preserving threat intelligence. However, Key challenges around adversarial robustness, data imbalance, ethical governance and model interpretability remain. The paper concludes with a discussion of future research opportunities focused on self-adaptive defence mechanisms, cognitive security architectures as well as AI-human collaboration systems expected to enhance cyber resilience in more and more decentralised and autonomous digital spaces.