This paper explores the role of Generative AI (GenAI) and Large Language Models (LLMs) in penetration testing, examining the benefits, challenges, and risks associated with their application in cyber security. The authors discuss how GenAI, particularly ChatGPT 3.5, can enhance the efficiency and creativity of penetration testing by automating test scenarios, identifying vulnerabilities, and generating novel attack vectors. They demonstrate the effectiveness of GenAI in a simulated pentesting engagement, showing that it can produce commands for a full penetration test and generate accurate reports. However, the paper also highlights potential risks, including overreliance on AI, ethical and legal concerns, and the risk of uncontrolled AI development. The authors provide guidelines for responsible AI deployment, emphasizing the importance of human oversight, data security, and collaboration between organizations and governments. The paper concludes with a discussion of best practices for implementing GenAI in pentesting, ensuring transparency, explainability, and the protection of sensitive information.This paper explores the role of Generative AI (GenAI) and Large Language Models (LLMs) in penetration testing, examining the benefits, challenges, and risks associated with their application in cyber security. The authors discuss how GenAI, particularly ChatGPT 3.5, can enhance the efficiency and creativity of penetration testing by automating test scenarios, identifying vulnerabilities, and generating novel attack vectors. They demonstrate the effectiveness of GenAI in a simulated pentesting engagement, showing that it can produce commands for a full penetration test and generate accurate reports. However, the paper also highlights potential risks, including overreliance on AI, ethical and legal concerns, and the risk of uncontrolled AI development. The authors provide guidelines for responsible AI deployment, emphasizing the importance of human oversight, data security, and collaboration between organizations and governments. The paper concludes with a discussion of best practices for implementing GenAI in pentesting, ensuring transparency, explainability, and the protection of sensitive information.