Articles

Large amounts of data are essential for driving ML models and training large language models (LLMs).While many organisations already have this data, the underlying problem is they can’t safely use it. Strict data privacy regulations and security concerns can prohibit the use of sensitive customer data like PII, financial information, or healthcare records for model training.

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 can use deterministic masking to securely generate consistent data across data stores, which gives more realistic masked data.