Essential Insights Before Employing Snowflake's Built-in Data Categorization

Essential Insights Before Employing Snowflake's Built-in Data Categorization

Snowflake's Data Classification feature offers a powerful solution for handling Personally Identifiable Information (PII) within your data warehouse. Here's a step-by-step guide to help you implement this feature effectively.

Setting Up the Governance Environment

1. Prepare your Snowflake environment with necessary roles and permissions to manage data classification and tagging.

Defining Custom Tags (Optional)

1. Define classification tags that represent different sensitivity levels or PII categories you want to apply to data. This step is optional but can help streamline your PII management.

Creating a Classification Profile

1. Build a profile that specifies what tags to apply and under what conditions, essentially configuring how to classify data columns as PII or sensitive data.

Linking Tag Mapping to the Profile

1. Connect your tags to the classification profile to automate tagging based on the defined rules.

Applying the Classification Profile to Schemas

1. Assign the profile to database schemas so that tables and their columns within these schemas are subject to the defined classification rules.

Identifying Tables with Sensitive Columns

1. Define or identify tables that contain PII or sensitive information so classification rules can target these appropriately.

Inserting Data for Classification

1. Load data to enable Snowflake to scan and classify columns according to the profile’s rules.

Running Manual Classification (If Needed)

1. You can manually trigger classification runs to refine or adjust tagging where automation may not be fully accurate or complete.

Important Considerations

• Clearly define classification levels (e.g., Confidential, Highly Confidential) and assign consistent labels or metadata tags. This ensures uniform understanding and handling of PII across the organization.
• Classification must align with regulatory requirements (e.g., GDPR, HIPAA). Implement corresponding security controls such as access restrictions, encryption, masking, and auditing for each classification level.
• Automated classification should be supplemented by verification processes (manual review or statistical analysis) to reduce false positives or negatives, critical for sensitive data like PII.
• For PII, policies like randomized response masking can be used to protect sensitive attributes while preserving data utility. Snowflake integrates such policies that can automatically mask data in tagged columns.
• As schemas evolve, data classification must adapt (e.g., newly added columns should be automatically classified or flagged for review) to maintain data governance integrity.
• Incorporate Snowflake’s expectations framework to monitor data quality and compliance with classification rules, enabling proactive governance and detection of anomalies.

By following these steps and best practices, Snowflake's data classification feature can effectively manage PII, helping organizations achieve compliance and reduce risks related to handling sensitive information.

Additional Considerations

• The Data Classification feature does not automatically surface any PII using tags, and data engineers still need to architect the end-to-end process, including building tooling to facilitate the manual review process and optimizations for the data volume, budget, and usage patterns.
• Data that will never make it to the business/metrics layer should also be considered in the data classification process.
• The Data Classification feature in Snowflake is limited to analyzing a VARIANT with one single data type, such as a varchar or a number.
• If tables in Snowflake contain JSON fields, the Data Classification feature cannot be used and a multi-step process is required.
• Questions to consider before implementing Snowflake's Data Classification feature include data quality, volume, data governance, and security.
• Snowflake offers stored procedures for classifying tables in a schema, database, or on classified object columns using tags, but these stored procedures may not meet expectations in terms of automation, scalability, and monitoring.
• Snowflake provides a list of available classification tags, but these tags may not be sufficient for all use cases, especially those outside the US. Users may need to define their own list of tags and figure out how to integrate it with the native list.
• Performance assessment for Snowflake's Data Classification feature shows that a full table scan is performed every time the function is called, regardless of the sample size. The sample size mainly affects the accuracy of the classification result, not the performance.
• Data classification is often a hard problem to solve, with some companies using manual methods and others using machine learning.

In conclusion, Snowflake's Data Classification solution is not a one-size-fits-all solution and requires careful consideration and customization to meet specific needs.

Technology plays a crucial role in effectively implementing Snowflake's Data Classification feature, especially with the help of data-and-cloud-computing solutions. By leveraging these technologies, organizations can streamline the identification, protection, and management of Personally Identifiable Information (PII).

Moreover, technology aids in the automation of various steps in the data classification process, such as automatic tagging, masking, and monitoring for compliance. This helps organizations reduce risks associated with mishandling sensitive information and achieve regulatory compliance more efficiently.

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