Red teaming is a critical tool for improving the safety and security of AI systems by adversarially testing them to identify potential vulnerabilities. Despite its importance, there is currently a lack of standardized practices for AI red teaming, which can lead to inconsistency in how threats are assessed and mitigated. To address this, researchers and developers have developed various red teaming methods, including domain-specific expert teaming, policy vulnerability testing, frontier threats red teaming, multilingual and multicultural red teaming, using language models to red team, automated red teaming, red teaming in new modalities, open-ended general red teaming, crowdsourced red teaming for general harms, community-based red teaming for general risks and system limitations. These methods can be integrated into an iterative process from qualitative red teaming to the development of automated evaluations, enabling more efficient and comprehensive testing. Establishing standardized practices and standards for systematic red teaming is crucial to ensure AI systems are safe and beneficial to society. Policymakers can support further adoption and standardization by funding organizations to develop technical standards, establishing independent government bodies or non-profit organizations, encouraging the development of a market for professional AI red teaming services, and promoting transparency and model access. By investing in red teaming, researchers and developers can work towards building AI systems that are safe and beneficial to society.