## 1 Introduction  
In recent years, interest in generative artificial intelligence has surged, driven largely by its ability to autonomously generate various types of content, including text, images, speech, and videos. Traditionally, these tasks were performed by humans, but the advent of AI has significantly transformed this landscape. The global interest in generative AI hasbeen further fueled by the introduction of innovative, user-friendly services like ChatGPT, which have made these technologies more accessible to the public [1]. ChatGPT, a large-scale AI language model, responds to questions with remarkable speed, accuracy, and quality, surpassing traditional chatbot services [2]. As shown in Figure 1, by August 2024, ChatGPT dominated the GAI chatbot market in the United States, holding a 60% market share, followed by the Microsoft Copilot program, also based on ChatGPT, at 14.6% [3]. Recent studies, including [PERSON]'s, have demonstrated that ChatGPT is capable of emulating human-like discourse and providing insightful responses to complex queries [4]. Trained on an extensive corpus of textual data, ChatGPT has gained a deep understanding of numerous subjects, enabling it to engage in coherent and contextually relevant discussions. One of its most remarkable features is its ability to address intricate inquiries. It can analyze nuanced questions, break them down into meaningful components, and generate structured responses that consider the context and subtleties of the conversation [5]. As illustrated in Figure 2, ChatGPT's capabilities have led to its widespread use in various fields, including healthcare [6; 7], business [8], education [9; 10], and content creation [11]. Remarkably, ChatGPT reached one million users within just five days of its launch, a feat that took Netflix three-and-a-half years and Instagram two-and-a-half months to achieve [12]. Figure 1: Generative AI chatbots by market share. Figure 2: Time to reach one million users for selected online services. Despite the widespread adoption of ChatGPT, a debate continues regarding the potential benefits and challenges posed by GAI models in various industries [13]. On the positive side, large-scale AI services like ChatGPT have the potential to enhance productivity in business processes and significantly impact industries such as healthcare, law, and media [14]. For instance, in healthcare, ChatGPT can analyze patient data to suggest personalized treatment plans; in law, it can efficiently review vast volumes of legal documents; and in media, it can automatically generate content to provide timely information. The adoption of such AI technologies has the potential to reduce costs, improve efficiency, and enhance service delivery. However, concerns remain about the risks associated with these technologies, particularly the potential for misinformation, misuse, and exploitation by malicious actors for cyberattacks and criminal activities [15]. For example, ChatGPT could be used to develop malicious software, automate phishing attacks, or spread misinformation, leading to social disruptions. Consequently, it is crucial to uphold ethical standards and establish regulations to mitigate these risks [14]. As GAI technologies continue to evolve rapidly, the discussion surrounding their safe integration into industries and the responses to these changes remains ongoing. Therefore, further research is essential to explore the use of GAI models from a security perspective, ensuring that they can be safely and appropriately deployed across various industrial applications in the future. The objective of this study was to assess the potential security impact of using GAI models, such as ChatGPT, and to provide practical recommendations for their safer deployment in real-world applications. To achieve this, 1047 SCI-level academic articles on GAI and security were collected from the SCOPUS database and analyzed using scientometric methods.