Derive insights and collaborate on sensitive data with confidence


How Can Technology Reduce the Risk of Using Sensitive Data for Devops?

For DevOps, data needs to be made available to users in order to do bug fixes and software improvements. However, when that data is sensitive, it is risky to make that available to a broad group of users who could either be internal or third parties.

Privitar can de-identify sensitive data and make it useful for DevOps scenarios. Our privacy algorithms can preserve the richness and referential integrity of the dataset. This means de-identified data has higher quality than data from synthetic generators.

Key Features

  • De-identify sensitive data with masking, perturbation or generalization techniques 
  • Preservation of useful patterns in data and retention referential integrity maintains more richness of de-identified data compared to synthetically generated data
  • Embed watermarks in datasets to facilitate auditing and data lineage
  • Automate privacy policy creation and centrally manage policies consistently across datasets in the enterprise
  • Retain customer data format, e.g. email addresses can be represented in email form



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