## 4 Results
### Analysis Results of LDA Topic Modeling
#### 4.4.3 Weight by Topics  
Figure 9 presents a network visualization that provides a comprehensive overview of the top 100 keywords identified through topic modeling analysis. This visual representation illustrates the complex interrelationships between various topics and their associated terms in the context of GAI and its security applications. The network highlights central themes within GAI security, revealing common keywords that link multiple topics and identifying broader thematic overlaps across different domains. The keyword \"ChatGPT\" stands out as particularly significant, appearing frequently in contexts related to healthcare security with AI, user privacy and AI security, and AI system security and risk management. This widespread presence underscores the multifaceted role of ChatGPT, an advanced AI language model, across various sectors. For example, in healthcare, ChatGPT is being explored as a tool to enhance communication between patients and healthcare providers, with the potential to improve diagnostic accuracy and patient engagement. However, its use raises serious concerns about data privacy, security vulnerabilities, and the potential for misuse, especially in sensitive areas like healthcare and cybersecurity. The repeated appearance of \"ChatGPT\" in these contexts emphasizes the need for robust, tailored security measures to address both its benefits and associated risks. Similarly, the keyword \"security\" emerges as a central theme that links multiple topics, such as AI system security and risk management, user privacy and AI security,  
Figure 9: Network visualizations for keywords by topics. and healthcare security with AI. This highlights the universal importance of security in the development, deployment, and operation of AI systems. Protecting patient data in healthcare, safeguarding user privacy in AI interactions, and securing AI systems against cyber threats are all critical areas where security is paramount. The frequent mention of \"security\" underscores the essential role that it plays in ensuring the resilience and trustworthiness of AI technologies across various domains. The term \"data\" also appears consistently across a number of topics, including security in language models and data processing, AI system security and risk management, and healthcare security with AI. Data are fundamental to AI, serving as the foundation on which AI systems are built and operate. Ensuring the security of data, especially in sensitive sectors like healthcare, is critical. The recurrence of \"data\" across different topics highlights the urgent need for robust data protection measures, such as encryption, secure storage, and rigorous access controls, to protect sensitive information throughout its lifecycle in AI applications. The term \"privacy\" is closely associated with several topics, including user privacy and AI security, healthcare security with AI, and AI system security and risk management. As AI systems increasingly handle personal and sensitive data, privacy concerns have become a major issue. The repeated mention of \"privacy\" reflects the ongoing challenge of balancing the advantages of AI with the need to protect individuals' privacy and comply with regulatory standards. This underscores the necessity of implementing technologies and practices that safeguard the confidentiality of user data. Lastly, the keyword \"system\" frequently appears in discussions related to AI systems security and risk management and secure software development with AI, reflecting broad concerns about the security and integrity of AI systems. The term \"system\" plays a central role, whether it refers to securing the entire AI infrastructure or incorporating security measures early in the software development process. The emphasis on \"system\" highlights the importance of taking a comprehensive approach to AI security, considering the entire ecosystem and implementing safeguards against potential threats.