Protegrity Data Privacy is a data anonymization and transformation platform that de-identifies personally identifiable information (PII), protected health information (PHI), and other sensitive identifiers. The solution enables organizations to use sensitive data for analytics, AI/ML workflows, and other data-driven use cases while maintaining compliance with global privacy standards. The platform implements privacy-enhancing technologies (PETs) including k-anonymity, l-diversity, and t-closeness models. It provides automated detection of quasi-identifiers and suggests appropriate anonymization approaches for complex datasets. The solution supports selective record-level anonymization within relational databases, allowing organizations to apply privacy controls to specific rows based on criteria such as retention policies or consent status. Protegrity Data Privacy includes risk assessment capabilities that quantify re-identification risk using scoring models for prosecutor and journalist attack scenarios. The platform measures the impact of anonymization on data utility to help organizations balance privacy requirements with analytical value. The solution can be deployed across cloud-native environments (AWS EKS, Azure Kubernetes Service), on-premises infrastructure, and major data platforms. It offers integration options through REST APIs and Python SDKs for embedding privacy controls into CI/CD, ETL/ELT, and MLOps pipelines. The platform supports various enforcement mechanisms including native database protectors, proxy-based interception, connectors/SDKs, and batch utilities.