Privacy-enhancing technologies, or PETs, are methods that reduce exposure of sensitive data while it is being processed, shared, or analyzed. Common examples include pseudonymization, encryption, secure multi-party computation, federated learning, and differential privacy. Instead of moving raw personal data everywhere, PETs let systems compute useful results while revealing less about the underlying records.
In cyber security, PETs matter because data exposure is a major attack surface. They can limit damage from insider misuse, cloud compromise, and overbroad analytics access, and they help organizations meet privacy and compliance requirements. In real defenses, PETs are used to share data more safely between teams, train AI models without centralizing all source data, and protect reports from re-identification. They are not a replacement for access control, logging, or governance, but they reduce the amount of sensitive information that attackers or unauthorized users can reach.