Anonymization is a cybersecurity process that transforms data so it can no longer be linked to an identifiable individual, either directly or indirectly. This technique involves the irreversible alteration, removal, or generalization of personally identifiable information (PII) and quasi-identifiers from datasets. Unlike pseudonymization, anonymization aims for permanent unlinkability, ensuring that re-identification remains technically impossible even with additional data or advanced analytical methods.

The primary purpose of anonymization is to mitigate risks associated with data breaches and unauthorized access, enabling organizations to responsibly use, share, and publish data for research, analytics, and business intelligence while maintaining privacy standards. Implementing robust anonymization strategies is essential for compliance with global privacy regulations such as GDPR and CCPA, which demand rigorous data protection measures. This process represents a proactive approach to data lifecycle management, fostering trust in digital ecosystems by demonstrating commitment to ethical data handling.