In today’s data-driven world, organizations in several industries collect Sensitive Data every time new users sign up for their services or buy a product. For instance, retail, healthcare, transportation, and financial services may collect a few Sensitive Data categories like:
- First & Last Name
- Gender
- Birthday
- Residential Address
- Email Address
- Home or Cell Phone Number
- Social Security Number
The Challenge of Protecting Sensitive Data
Sensitive Data requires stricter security controls because of the severe harm it may cause to individuals if it is compromised. For example, if a customer’s Social Security Number, First & Last Name, and address are compromised, identity thieves can use this data to commit serious financial crimes. Therefore, Privacy Laws like the GDPR, CPRA, and LGPD mandate organizations to implement the most strict security controls to protect SD from unauthorized or unnecessary access.
More than 130 countries across six continents have passed strict data privacy laws, so it is more important than ever for global organizations to implement policies to ensure compliance with these laws.
Securing Sensitive Data in the Snowflake Data Cloud
Snowflake has emerged as a market leader in processing data for business analytics, helping organizations make better decisions, improving customer experience, and increasing revenue.
The massive amount of data being processed in Snowflake every day makes it difficult for organizations to discover, classify, and secure Sensitive Data at a petabyte scale. Although Snowflake’s Enterprise for Sensitive Data provides some built-in data protection capabilities, it doesn’t cover the entire spectrum of data protection needed for robust snowflake security.
Experts at Securiti have devised a 4-step process that organizations can follow to secure Sensitive Data in the Snowflake Data Cloud.
The Process of ensuring Snowflake Security for Sensitive Data:
Step 1: Discover Sensitive Data across all tables in the Snowflake Database
The first step is to scan all tables with each column and discover any Sensitive Data in them. Securiti integrates natively with Snowflake to discover and classify sensitive data. Securiti uses various built-in and custom data elements to discover hundreds of sensitive data elements stored in Snowflake. Additionally, it can scan petabytes of data using multiple out-of-the-box techniques that allow data scan to complete quicker than other traditional data scanning tools.
Step 2: Classify and Label Discovered Sensitive Data
The second step is to classify the discovered data into relevant categories. Classification is necessary to identify personal data and determine which personal data is sensitive. There are 100+ identifiers of personal data in modern privacy laws like the GDPR and CCPA. Privacy laws require organizations to take additional security measures to protect sensitive personal data because of its considerable potential impact on individuals in the case of a data breach.
After classifying sensitive data, organizations can synchronize and populate their data catalogs with appropriate column-level classifiers and tags, improving data governance workflows.
Step 3: Apply the Appropriate Usage & Access Security Controls to Sensitive Data
Once sensitive data has been classified and cataloged, admins need to:
- Identify has access to the discovered sensitive personal data,
- Formulate a user role-based data usage and access framework, and
- Implement a framework to restrict usage and access to the right user roles based on approved purposes.
This step is essential for effective data governance and strengthening sensitive personal data security.
Securiti provides insights into specific users/groups that access sensitive data in Snowflake and configure policies to prevent unauthorized access. For instance, organizations can fine-tune broad access policies to restrict data usage and access to authorized users only. For example, data analysts do not need to know sensitive information about customers to complete their analysis. They can be given access to the information that is necessary for them to complete their work.
Step 4: Continuously Monitor Security Misconfigurations to fortify Snowflake Security
Lastly, organizations must continuously monitor any changes in data security configurations and access policies. This proactive approach is necessary to anticipate security threats and neutralize them before they can cause any significant damage. Organizations must also map updated security controls to various compliance reports such as ISO, NIST, PCI, HIPAA, GLBA, GDPR, etc.
Securiti monitors misconfigurations in the Snowflake instance and sets policies that automatically remediate them. For example, Snowflake administrators can discover and enable multi-factor authentication for all users.
In addition to auto-remediation, Snowflake administrators can notify data system owners via email or a service ticket. Admins can track policy violations via owner assignment to ensure any security risks are reviewed and appropriately addressed. This adds an extra layer of security to the Snowflake instance.
Finally, the solution can help organizations track policy violations via owner assignment, ITSM, or customized workflows.
