Do you create and manage separate test environments to secure customer data? Save time and costs with Jade's Snowflake data masking solution, designed to enhance data governance and compliance in the cloud test environment.
With the increasing need to comply with regulations and standards such as GDPR, HIPAA, and PCI DSS, it has become crucial for organizations to protect sensitive data. In software development, data governance becomes a challenge in non-production environments.
These non-production environments, such as testing, QA, and staging, are crucial for software development, but in these environments, data is accessed by several stakeholders and poses a risk of security and non-compliance.
To help businesses maximize data governance in Snowflake test environments, Jade, a Snowflake Select Partner, has built a unique Data Masking solution.
This blog will delve into the concept of data masking, its significance, and the need for Jade to build a data masking solution for Snowflake test environments. Additionally, the blog covers the detailed architecture of the solution built by Jade.
What is Data Masking?
Data masking is the process of replacing sensitive data with fictitious data or scrambling values that preserve the characteristics of the original data while protecting its confidentiality. The goal of data masking is to prevent unauthorized access to sensitive data in non-production environments.
Data Masking Solution for Snowflake Test Environments Built by Jade
Jade has developed a Snowflake data masking solution that enables businesses to enhance their data governance in test environments within Snowflake while ensuring compliance with various regulations and standards that require the safeguarding of sensitive data.
Jade's Snowflake data masking solution offers an automated process that takes data from the source system, conducts PIA discovery, performs a lookup, deploys masking policies, and loads the data. By automating the data masking process, businesses can improve security, comply with regulations, save costs, and focus on real development needs.
Read the full blog here - How to Maximize Data Governance in Snowflake