The process of removing personally identifiable information from data sets, so that the people whom the data describe remain anonymous.
Data Anonymization is a core preprocessing step for AI. Before data is vectorized or used for fine-tuning, automated pipelines need to remove PII, financial data, and classified IP so that the resulting model does not memorize sensitive information and leak it during inference.
See also Fine-Tuning, Air-Gapped Environment, Role-Based Access Control and Zero Trust Architecture.