OpenAI recently released Privacy Filter, an open-weight model for detecting and redacting PII. This post looks at where it works, where it falls short and what production data masking actually requires.
DataMasque collaborates with Cohesity, securing data compliance without sacrificing utility or innovation.
As data breach and cyberattack threats continue to grow, data privacy has become a serious concern for organisations. This has created a tension between the need for data utility and the importance of data privacy.
Data is a highly valuable currency for businesses and cybercriminals alike, and protecting it is a non-negotiable. Data masking involves altering or obfuscating sensitive data so it becomes unidentifiable while still maintaining functional integrity.
DataMasque version 2.11 adds support for DynamoDB, along with a raft of other improvements.
DataMasque can use deterministic masking to securely generate consistent data across data stores, which gives more realistic masked data.
Introducing file masking in DataMasque 2.9.
.png)
