Data masking Hush Hush
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Government

Governments are well versed in handling confidential information, but more and more government agencies are understanding the risks associated with exposing the Personally identifiable information (PII) of its citizens to unauthorized parties, and are applying privacy safeguards to educational, statistical, tax, and many other types of data.

 

For government agencies, it is of vital importance that all compliance measures are met for data privacy and that both internal and external risks are mitigated, not only for PII, but for confidential information as well.

 

Besides following the regulatory guidelines on data privacy that mainly apply to static data masking, they also need to de-identify data in real-time so that only "authorized eyes" have access to sensitive information. Data masking effectively de-identifies sensitive data elements whilst maintaining the integrity of relevant information, allowing researchers to continue their work without risk.


In short, data protection methods like data masking allow you to control access to sensitive information, remain compliant with data privacy laws, and manage the risk of data breaches.


Data masking is a recommended method of data protection that meets the minimum requirements of most data privacy laws including:

  • HIPAA

  • HITECH

  • GDPR

  • GLBA

  • PCI/DCC

  • FERPA

  • PIPEDA

  • CCPR

  • PRIVACY SHIELD

  • COPPA

  • NYPA

Learn more about privacy laws here.

How Hush-Hush can help governments secure data

Hush-Hush Data Masking removes the danger out of potentially sharing confidential information with journalists, partners or third parties and gives you full control over access to sensitive information.

It is one of the most effective data protection methods that safeguards sensitive data from internal threats. It works by replacing identifiable or sensitive elements of data with harmless replacement data that looks real, but actually can't be used to identify someone.

Make a best practice out of securing data before you send it out by using data masking across your organization.

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