## 5 Discussion  
This study provides a comprehensive analysis of research trends in generative artificial intelligence, particularly focusing on security aspects. The bibliometric analysis indicates that the United States, China, and India lead in GAI security research, highlighting the global significance of AI technologies in mitigating security risks Prominent research institutions, such as the Chinese Academy of Sciences and the National University of Singapore, also play a pivotal role, highlighting the concentrated efforts from both Western and Asian regions in advancing GAI security research.  
The results from the TF-IDF and keyword centrality analyses provide valuable insights into the key terms shaping the discourse around GAI and security. While \"ChatGPT\", \"AI\", and \"model\" emerged as central terms, their varying TF-IDF and centrality scores suggest different roles within the research context. For instance, although \"ChatGPT\" is frequently mentioned, it does not hold the same centrality in linking core topics as terms like \"model\" and \"AI\". This distinction emphasizes the complexity of GAI applications, which span diverse industries--from education to healthcare--with distinct security implications.  
Latent Dirichlet allocation (LDA) topic modeling identified six key themes in the GAI security domain: AI and security in education, security in language models and data processing, secure software development with AI, security and risk management of AI systems, user privacy and AI security, and healthcare security with AI. The prominence of AI in education highlights the growing concerns over protecting student data and preserving academic integrity in AI-driven environments. At the same time, the focus on language models and data processing underscores the critical need to secure the foundational datasets and algorithms powering GAI systems. The relatively smaller share of research on healthcare security reflects an emerging but increasingly vital area of concern, as AI is integrated into sensitive sectors such as healthcare.  
A recurrent theme pervading the discourse on these subjects is the critical need for advanced security frameworks that extend beyond the mere remediation of technical vulnerabilities to encompass proactive threat detection and mitigation strategies. The recurrent appearance of keywords such as \"privacy\", \"data\", and \"system\" across various subjects underscores not only the necessity of comprehensive security strategies but also the increasing intricacy of GAI-related threats. This study emphasizes the necessity of transitioning from conventional security approaches to more advanced methods by incorporating real-time anomaly detection, adversarial robustness techniques, and trustworthiness verification into research in AI security. Moreover, the escalating risks of data manipulation, misinformation, and adversarial attacks on GAI models necessitate a paradigm shift in AI security methodologies. Rather than solely focusing on risk management, future research should emphasize proactive detection mechanisms, such as explainable AI for security audits, AI-generated content authenticity verification, and NLP-driven anomaly detection for AI-driven misinformation. By highlighting these emerging challenges, this study emphasizes the need for a multidisciplinary approach that combines cybersecurity, AI ethics, regulatory frameworks, and adversarial AI research to develop resilient and transparent AI security solutions.