An engineering privacy barrier is a technical safeguard designed to prevent user data from being accessed or used for unintended purposes, such as AI training. Google uses this term to describe systems that isolate sensitive user information from machine learning models, ensuring that personal data remains private and is not incorporated into AI datasets. These barriers can include encryption, access controls, data anonymization, and strict separation of data environments. By implementing engineering privacy barriers, organizations can comply with privacy regulations, protect user trust, and reduce the risk of data leakage or misuse during the development and deployment of artificial intelligence technologies.