This article systematically reviews the academic literature on the applications of artificial intelligence (AI) in cybersecurity, with a specific focus on generative techniques (generative AI or GenAI), such as large language models (LLMs). The analysis covers publications from 1993 to 2025 extracted from the IEEE Xplore Digital Library (1,647 publications) and SpringerLink (1,742 publications) databases, resulting in a collection of 3,389 documents. The litstudy tool was utilised for thematic mapping, automatic topic modelling, and n-gram analysis. The analysis shows an exponentially upward interest since 2022, particularly between 2023 and 2024, indicating rapidly growing interest in GenAI methods. Through topic modelling, the study identified the following key thematic areas: LLMs (734 publications; 21.88%), identified as the most dominant topic, blockchain and cyber attacks (394 publications; 11.74% each topic), generative coding for software (296 publications; 8.82%), smart energy and internet of things (278 publications; 8.29%), and malware (70 publications; 2.09%). The study revealed that GenAI and LLMs present a significant dual-use dilemma. Malicious actors increasingly leverage these methods for offensive purposes. Conversely, the same methods are actively developed to enhance cybersecurity defences. GenAI and LLMs are fundamentally reshaping the cybersecurity landscape, as evidenced by the visible growth in research interest. In light of the dual-use dilemma posed by GenAI, organisations should urgently consider investing in securing their sensitive data, and enhance staff capabilities in threat detection and response using GenAI-/LLM-driven methods.