The concept of distributed alignment in AI systems refers to the responsibility of organizations beyond frontier labs to monitor and control the alignment of AI systems through robust monitoring and control systems. This is a response to concerns about AI's potential risks, including the alignment problem, which ensures that AI systems reliably pursue and achieve intended objectives while respecting human values and safety constraints. The industry faces significant challenges in addressing this issue, with many organizations struggling to implement effective alignment protocols. A distributed approach to alignment, where multiple stakeholders are responsible for ensuring alignment, is seen as a solution. This requires developing tools and frameworks that enable organizations to implement and maintain alignment independently of the original model providers. The ultimate success of distributed alignment depends on creating an ecosystem where organizations have access to user-friendly alignment tools, best practices and metrics for alignment are standardized, and regulatory frameworks support and enforce alignment standards.