Generative AI introduces novel security risks that necessitate a reevaluation of traditional cybersecurity practices, prompting organizations to adopt new strategies for safeguarding AI systems. OWASP has released a generative AI Red Teaming Guide, offering a comprehensive framework for assessing AI models through red teaming, which involves targeted simulations to identify and mitigate vulnerabilities. This process is distinct from penetration testing, as it is typically conducted internally and focuses on specific scenarios. Red teaming serves multiple stakeholders, including AI engineers, risk managers, and business leaders, by identifying vulnerabilities, verifying control effectiveness, and managing risks like social engineering. The guide emphasizes the importance of collaborating with diverse stakeholders to define objectives and success criteria for AI security policies, ensuring alignment with organizational values and legal requirements. It also highlights the need for regular red teaming to secure large language model (LLM) applications, both pre- and post-deployment, by addressing threats such as adversarial attacks, alignment risks, and data leaks. Tools like Promptfoo are recommended for testing AI applications against various risks, supporting the integration of guardrails and red teaming to enhance security measures. Additionally, the guide underscores the significance of testing RAG (Retrieval-Augmented Generation) architectures and agent systems to ensure robust defenses against emerging threats. Overall, the evolving landscape of generative AI security demands continuous adaptation and collaboration among security teams, developers, and industry standards to protect AI applications effectively.