What Are the Best Practices for Using Snowflake Secure Views to Protect Your Data?

In the ever-evolving landscape of data security, it’s crucial to implement robust measures to protect sensitive information, especially within a powerful platform like Snowflake. Secure views, a key feature in Snowflake, are an essential tool in this endeavor, allowing you to restrict access to specific data while maintaining a seamless user experience. However, even with secure views in place, there are potential vulnerabilities to consider. Let’s explore best practices for using secure views effectively in Snowflake and mitigating potential data exposure risks.

Understanding Secure Views

Secure views are a powerful feature in Snowflake that helps control access to data within the platform. They allow you to filter data, ensuring that users can only see the information they are authorized to access. However, it’s important to recognize that even with secure views in Snowflake, there are ways data could be exposed inadvertently.

A Real-Life Scenario

Let’s illustrate this with a real-life example using the Snowflake suppose sample database. Suppose we have a table called ORDERS, and we want to create a secure view that filters orders for a specific clerk:

CREATE SECURE VIEW ORDERS_VW AS
SELECT * FROM SNOWFLAKE_SAMPLE_DATA.TP.ORDERS
WHERE clerkID = '000000612';

In this case, Clerk#000000612 should only access their own order data through the secure view ORDERS_VW in Snowflake. However, there’s a potential loophole. If the primary key column, ORDERKEY, is generated from a sequence, this clerk could deduce the total number of orders created between two order dates by executing a query like this:

SELECT orderKey, orderDate
FROM ORDERS_VW
ORDER BY orderDate;

Mitigating Data Exposure Risks

To prevent inadvertent data exposure and maintain the highest level of security within Snowflake, consider the following best practices:

1. 1. Exclude Unnecessary Columns: If a sequence-generated column, such as ORDERKEY in our example, isn’t needed in the view, it’s best to exclude it in Snowflake. Reducing the available data minimizes the risk of users deducing information.
   2. Obfuscate Sequence-Generated Values: If the sequence-generated column is necessary in the view within Snowflake, consider replacing its values with random identifiers or obfuscating them using hashes. This ensures that even if users gain access to the values, they won’t be able to decipher sensitive information.
   3. Materialized Secure Views: To further enhance security within Snowflake, consider using materialized secure views. These views precompute and store data, making it impossible for users to gauge the amount of underlying data based on query execution time. In our ORDERS example, you could create a separate secure materialized view for each clerk, containing only the orders accessible to them.

Conclusion

In the age of data privacy and security, the use of secure views is a fundamental practice, especially within the Snowflake platform. However, it’s essential to be aware of potential vulnerabilities and take proactive measures to mitigate risks. By following best practices such as excluding unnecessary columns, obfuscating data, and considering materialized secure views, you can strengthen your data protection strategy within Snowflake and ensure that sensitive information remains confidential.

Anyon Consulting Can Help

For organizations looking to implement robust data security measures within Snowflake, Anyon Consulting offers expert guidance and solutions tailored to your specific needs. Our team of data security professionals can assist in the setup and optimization of secure views, ensuring your data remains protected against potential vulnerabilities. Contact us today to learn more about how we can enhance your data security strategy within Snowflake and safeguard your valuable information.