AI in Cybersecurity and Digital Forensics: A Comparative Review of Techniques, Applications, and Research Gaps

Hensei Patel, Jay Pathak

Abstract: Cybersecurity and digital forensics are experiencing serious challenges as a consequence of a sharp rise in cyber risks and cybercrimes driven on by the rapid growth of digital technologies, cloud computing, and connected services. A great deal of digital evidence produced by contemporary systems are becoming too much for manual forensic methods and conventional security technologies to manage. To overcome these limitations, artificial intelligence (AI) methods such as machine learning (ML), deep learning (DL), convolutional neural networks (CNN), generative adversarial networks (GAN), natural language processing (NLP), reinforcement learning, and federated learning have shown promise. This review's goals are to identify performance trends, highlight unresolved issues, and offer a thorough review of current AI-driven research in cybersecurity and digital forensics. 19 investigations covering intrusion detection, malware analysis, phishing identification, smart grid security, cloud forensics, multimedia evidence analysis, generative AI security, and human-focused cybersecurity that were published between 2020 and 2026 were carefully selected from major scientific databases, including IEEE Xplore, Springer, Elsevier, ScienceDirect, ACM Digital Library, and SSRN. The contrast shows that while generative AI and NLP enhance forensic capabilities through threat simulation and automated analysis, deep learning models-particularly CNN-based architectures?achieve detection accuracy above 98% in intrusion detection and forensic categorisation tasks. The black-box nature of AI models, adversarial vulnerability, dataset scarcity, poor cross-domain generalisation, privacy issues, and high processing cost are some of the enduring challenges, however. Also, future directions for Explainable AI (XAI) research are provided in the paper.

Keywords: Artificial Intelligence, Cybersecurity, Digital Forensics, Machine Learning, Deep Learning, Convolutional Neural Networks, Generative AI, Intrusion Detection System, Explainable AI

How to Cite?: Hensei Patel, Jay Pathak, "AI in Cybersecurity and Digital Forensics: A Comparative Review of Techniques, Applications, and Research Gaps", Volume 15 Issue 5, May 2026, International Journal of Science and Research (IJSR), Pages: 1321-1326, https://www.ijsr.net/getabstract.php?paperid=SR26521101749, DOI: https://dx.dx.doi.org/10.21275/SR26521101749

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