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Data Security best practices for
Snowflake implementations

Foundations

Data security is foundational when designing data and analytics platform architectures. Our approach takes into consideration aspects such as organizational specific data classification and categories along with data platform-provisioned features and functionalities when assessing and designing for Security.

Classification and Categories

Data classification may be determined by various factors such as Scale, Domains, Governance, Data Types, and an organization's approach for managing sensitive data for their customers, such as HIPAA, PII, or GDPR-specific data candidates.

Areas where we can assist in a
Snowflake specific Security implementation

Brainstorm and establish foundational security elements and their definitions.

User or Apps authentication using one or more combinations like SSO/MFA including IP policies.

Application of features based on Roles driven by TAGS, Row Level Security (RLS), Column Level Security (CLS) and Data Classification.

Column level masking policies using Dynamic Data Masking and External Tokenization.
User or Apps authentication using one or more combinations like SSO/MFA including IP policies.

Row level masking policies based on row access policies.

Data Classification covering Snowflake Data Classification, Classification Process, and Custom Data Classification in Snowflake.

Tags and Tag Based masking policies that combine object tagging and masking policy features.

Leverage the data classification definitions relevant to your enterprise and map these to Snowflake specific roles by access and functions.

Definitions of accounts and roles to manage the specific accounts.

Domain based definition and mapping of Access and Functional roles.

Implementation of Object tagging to identify data that needs to be secured by enforcing Snowflake’s native governance controls such as data masking, column-level security, and row-level policies on the tagged objects.

Assignment of enterprise classification categories to tables and columns based on data classification dataset which will be used by custom stored procedures to create tags.

We propose an automation framework for TAG based Custom data classification and TAG based Policies.

This is particularly helpful in de-centralized domain owned ecosystems for domain owners to exercise data security and control.

Leverage built-in classification functionality to discover sensitive data by querying the INFORMATION_SCHEMA.

Perform object tagging as a first step to identify data that needs to be secured by enforcing Snowflake’s native governance controls such as data masking, column-level security, and row-level policies on the tagged objects.

Review Classification categories (Semantic and Privacy) for columns storing personal attributes name, age, address, age, gender etc.

Classify columns with a privacy category for which a semantic category is identified.

Analyze columns in a table or view, by generating semantic and privacy categories using Snowflake’s EXTRACT_SEMANTIC_CATEGORIES function.

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