AI red teaming focuses on simulating real-world attacks to identify vulnerabilities in artificial intelligence systems, diverging from traditional red teaming by emphasizing non-deterministic outcomes and the testing of AI models for issues such as toxicity, hallucinations, and data leaks. As AI systems become more integrated, the scope of AI red teaming includes testing not only the AI models themselves but also the broader system dynamics and interactions with plugins and agents, necessitating a cross-functional approach involving security engineers, ML specialists, and product teams. The evolution of red teaming practices can be structured in stages, from no testing to comprehensive AI assurance, with tools like Promptfoo enabling testing integration into development pipelines, thus fostering a culture of collaborative and continuous security assessment. Regulatory frameworks across regions, such as the EU AI Act and China's AI Measures, emphasize the need for robust AI security practices, making AI red teaming a critical component for compliance and risk management. Building a red teaming culture involves fostering cross-functional ownership, transparency, diverse perspectives, and incentives, ultimately leading to a trustworthy and resilient AI system that aligns with industry standards and earns user trust.