IDC Names Securiti a Worldwide Leader in Data Privacy


Data Intelligence for Snowflake

By Securiti Research Team
Published November 24, 2020 / Updated September 21, 2023

According to IDC, by 2023, 102.6 zettabytes of new data will be created every year. Businesses are collecting and processing large amounts of data to make better decisions. Companies rely on cloud data warehouse products to analyze business data. One such product that has seen exponential growth in the last few years is Snowflake.

Snowflake is a cloud based data warehouse that is ideal for running  large scale data analytics projects to uncover business insights, run or train machine learning models, and modernize their data infrastructure. That being said, there are certain challenges that come along with cloud migration to Snowflake.

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Cloud data warehouses provide flexibility and scale allowing organizations to analyze large quantities of data  helping making business decisions faster. However Global security regulations such as PCI, HIPAA, FERPA and privacy laws such as GDPR, CPRA require organizations to identify all sensitive and personal  data, ensure  data is used only for its  intended purpose and implement adequate security controls to protect the data from breaches.

Since data arrives into Snowflake from several sources, administrators do not have visibility into  what sensitive data is in Snowflake, whose data is it, and how many personal records are present. In short, organizations could be  non-compliant without their knowledge, and if there was a data breach it could cause large penalties and fines based on jurisdictions and local laws.


Here are a few things organizations can do to reduce their privacy and security risks.

  • Discover personal data
    The first step is to scan your Snowflakes instance to detect all personal & sensitive data stored in tables and schemas. This will help you track down all sensitive and personal data in your Snowflake instance
  • Reveal all sensitive and personal  data
    Detect all sensitive and personal data records in schemas, tables and columns. This will make it easier to implement data protection or retrieve  personal data when necessary
  • Identify Data Risk
    Assess data risk in Snowflake based on personal data attributes, data residencies, & Snowflake instance location. This can help flag data  risks on an on-going basis.  dangerous.
  • Identify data owners & fulfill DSRs in a timely manner
    Linking all personal data can help you respond to data subject rights (DSRs) swiftly and efficiently.
  • Protect PII and PHI
    Organizations need to make sure that there are adequate security controls in place in order to protect on PII and PHI identified in Snowflake. Data obfuscation controls such as data encryption or masking on specific columns with sensitive data will reduce security risks in case of breaches.
  • Visibility and enforcement of access governance
    Review who has access to specific tables in Snowflake & enforce access to ensure only users with access to data can view it.

To learn more about how can help you automate your data privacy and security in Snowflake signup & watch a demo today!

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At Securiti, our mission is to enable enterprises to safely harness the incredible power of data and the cloud by controlling the complex security, privacy and compliance risks.


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