Generative AI in medical practice presents significant privacy and security challenges. This paper explores the various applications of generative AI in healthcare, including medical diagnostics, drug discovery, virtual health assistants, medical research, and clinical decision support. It identifies security and privacy threats throughout the life cycle of these systems, from data collection to implementation. The study aims to analyze the current state of generative AI in healthcare, identify opportunities and privacy challenges, and propose strategies for mitigating security and privacy risks. The findings highlight the importance of addressing these threats to ensure the safe and effective use of generative AI in healthcare. The study contributes to theoretical discussions on AI ethics, security vulnerabilities, and data privacy regulations. It also provides practical insights for stakeholders looking to adopt generative AI solutions. Generative AI has the potential to transform healthcare by improving diagnostics, accelerating drug discovery, and enhancing patient care. However, its data-intensive nature and opacity pose acute privacy and security risks. Generative AI models can be trained on sensitive patient data, which could be exploited by malicious actors. The study emphasizes the need for robust data governance frameworks, secure infrastructure, and ethical guidelines to ensure the safe and responsible use of generative AI in healthcare. The findings of this study can inform the development of future generative AI systems in healthcare and help healthcare organizations better understand the potential benefits and risks associated with these systems. The study also discusses the challenges of using generative AI models, including potential biases that could lead to inaccurate diagnoses and treatments. The paper examines the security and privacy threats in the life cycle of generative AI in healthcare, including data collection, model training, and implementation. It highlights the importance of addressing these threats to ensure the safe and effective use of generative AI in healthcare. The study also discusses the risks associated with the use of generative AI in healthcare, including privacy concerns, security issues, and system vulnerabilities. The paper concludes that careful governance is necessary to realize the benefits of generative AI while safeguarding patient data and public trust.Generative AI in medical practice presents significant privacy and security challenges. This paper explores the various applications of generative AI in healthcare, including medical diagnostics, drug discovery, virtual health assistants, medical research, and clinical decision support. It identifies security and privacy threats throughout the life cycle of these systems, from data collection to implementation. The study aims to analyze the current state of generative AI in healthcare, identify opportunities and privacy challenges, and propose strategies for mitigating security and privacy risks. The findings highlight the importance of addressing these threats to ensure the safe and effective use of generative AI in healthcare. The study contributes to theoretical discussions on AI ethics, security vulnerabilities, and data privacy regulations. It also provides practical insights for stakeholders looking to adopt generative AI solutions. Generative AI has the potential to transform healthcare by improving diagnostics, accelerating drug discovery, and enhancing patient care. However, its data-intensive nature and opacity pose acute privacy and security risks. Generative AI models can be trained on sensitive patient data, which could be exploited by malicious actors. The study emphasizes the need for robust data governance frameworks, secure infrastructure, and ethical guidelines to ensure the safe and responsible use of generative AI in healthcare. The findings of this study can inform the development of future generative AI systems in healthcare and help healthcare organizations better understand the potential benefits and risks associated with these systems. The study also discusses the challenges of using generative AI models, including potential biases that could lead to inaccurate diagnoses and treatments. The paper examines the security and privacy threats in the life cycle of generative AI in healthcare, including data collection, model training, and implementation. It highlights the importance of addressing these threats to ensure the safe and effective use of generative AI in healthcare. The study also discusses the risks associated with the use of generative AI in healthcare, including privacy concerns, security issues, and system vulnerabilities. The paper concludes that careful governance is necessary to realize the benefits of generative AI while safeguarding patient data and public trust.