Snowflake Security - Guide and Best Practices

Snowflake is one of the most popular cloud-based data warehousing platforms currently available. Offered in a Software-as-a-Service or SaaS model, Snowflake takes advantage of unique architecture and full support for ANSI SQL to provide unmatched features and functionality for companies seeking a versatile data platform. Migrating to Snowflake can bring many benefits to businesses of all sizes, provided you understand the security features integrated into Snowflake and follow the security best practices.

Security is a crucial aspect of any cloud-based data solution. Snowflake comes with a full suite of security features that can help you eliminate potential risks and issues. Let us take a look at some of the most important Snowflake security best practices you need to follow.

What is Snowflake Security?

When you have a Relational Database and want to ensure the highest level of security, you can use the Snowflake as an option to use it as your data warehouse. But, similar to every new piece of technology, questions arise asking how secure Snowflake warehouses are. Snowflake provides robust security protocols to eliminate and deal with cybersecurity threats.

Join Protecto to make sense of Snowflake security and get advice on effectively protecting your data in Snowflake.

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As the age-old saying goes, “You can’t be too careful”. The same can extend to using any cloud service to store and protect your data. But why does it have to be this way? Why do we need to consider the data security aspects of Snowflake?

Why is Snowflake Security Important?

With the advent of technology, not only has cyberspace become more digitized, but it is also the lifeline of several enterprise businesses. Not only that, there are many cloud service providers and many enterprise companies to choose from. Generative AI has revolutionized cyberspace. The caveat of all these is that hacking attacks, phishing emails, and data leaks have become more malicious and have an even lower bar of entry. These days, you can generate malicious code using Generative AI.

In such scenarios as this, the security of each software must be considered. Especially, when Snowflake seems to be advertised as the prime data storage platform. But, since it provides its security system, that would be it, right? Not really, there are a few complications in implementing Snowflake security to your Enterprise data.

Challenges of Snowflake Security

There are many possible challenges present in the Security Protocols when utilized incorrectly by the users. Some of them are:

Weak Authentication

Things such as Username and password to your database may not be enough to maintain the highest levels of security. If the credentials are compromised, there is no coming back to it. One of the ways that you can mitigate this in Snowflake is by allowing 2FA or other security protocols adjacent to the username and password such as personal questions and so on.

Protecto uses the concept of intelligent tokenization to tokenize your data before uploading it to Snowflake so that no PII (Personally Identifiable Information) would be available.

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Unauthorised Access

If multiple people have access to your data and your account, it makes it less safe. Malicious users may target others related to your Snowflake data warehouse and gain access through them.

Protecto prevents unauthorised access by implementing granular access to those who are vetted and trusted. The same will be ensured for your data in Snowflake.

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Less Emphasis on Monitoring Sign-ins

There isn’t a strict, rigorous data traffic tracker available. Logins and logouts are not frequently updated in the database. This may make the tracker miss potentially unauthorised access which may have been stopped earlier.

Protecto uses their own SaaS server to host and store documents. Their state-of-the-art Gen AI LLMs provide better security and privacy than the public LLMs out there.

Compliance Risks

Sometimes, enterprise users storing data may not follow data security laws such as HIPAA or GDPR leading to a violation of rules, resulting in their accounts being terminated and potentially sensitive data being leaked unintentionally as a result.

Protecto guarantees data storage and privacy according to HIPAA or GDPR.

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Insecure Data Transmission

If you are sending data from Snowflake in less secure channels, you might run into the risk of leaking your data. Malicious users may use IP Spoofing and steal your data packets and use them for their selfish purposes.

Protecto guarantees data security with their intelligent data tokenization where all sensitive data points are converted to tokens and are mapped. These mapping points are stored in a secure token vault kept in secret in their privately hosted server.

But Snowflake has its security measures implemented automatically. They are implemented row-wise and column-wise. Let’s learn more about that below.

 Snowflake Security Best Practices

Snowflake Security Layers

Snowflake makes use of multiple techniques to secure user data. This security is divided into three concentric layers: network security, identity & access management, and data encryption. To understand and follow Snowflake security best practices, you need to understand all three of these aspects and find out the best practices for each layer applicable to your unique use case. Within each layer, there are several things you can do to enhance overall security.

Also Read: Protect PII And Sensitive Data With Data Tokenization

Network Security

With network security features, you can successfully isolate your instance of Snowflake from outside attacks and unauthorized access. These features can also be used to set up secure access for cloud storage and client applications with Snowflake.

Here are some things to do:

• Use Snowflake's network policies only to allow connections to known clients. You can declare a specific IP address range that can connect to Snowflake while other IP addresses get blocked. You can also set policies for specific circumstances that allow for overriding the account-level policies.
• Set up private connectivity by configuring your DNS to resolve to Snowflake's private URL. With the appropriate forwarding rules, users can securely access from the cloud and on-premises. You can then choose to exclude public endpoints or create specific rules for access public endpoints.
• Set up secure cloud storage access for Snowflake for data loading by using the relevant IDs for Azure or AWS in your storage rules.
• Configure your firewall to secure the outgoing traffic to other client applications. You can use specific firewall rules depending on whether you are using a private or public endpoint, set up an SSL passthrough if you are using a proxy, or check for correct connectivity using SnowCD.

Identity and Access Management

Creating and authorizing the right users is a crucial part of securely accessing Snowflake. You can use different security features to manage users, sessions, and authentication. Here are some pointers:

• Use SCIM or build a customized tool for Active Directory syncing users and profiles.
• Create complex passwords and change them frequently to maintain security.
• Understand the different authentication methods supported by Snowflake and allocate based on the specificity of the use case. You can use passwords, OAuth, key pairs, SAML, or external browser-based login. Snowflake also supports Okta native authentication.

OAuth is the most preferred method of authentication if you want heightened security. Snowflake supports both Snowflake OAuth and external OAuth.

• External browser-based authentication is safe to use with desktop tools and SnowSQL.
• If you do not integrate with an Identity Provider, you could use Snowflake's built-in multi-factor authentication. Otherwise, you can enable multi-factor authentication with your Identity Provider.
• Use roles for object-level access control. You can define a set of roles, set access levels for each, and prevent access in particular cases by roles that do not need access. Through access roles and functional roles, you can create a complex access control system that can provide read-only, read-write, and admin access based on user privileges.
• Facilitate column-level access control through secure views, dynamic data masking, and external tokenization. Either create a centralized masking policy or decentralize to different teams.
• For mixed data tables, you can also restrict access to specific rows by defining access criteria and generating dynamically filtered secure views.

Encryption

Data encryption is an integral part of data security. Snowflake encrypts all stored data using transparent encryption and a key hierarchy. Individual data pieces are encrypted using different keys, and the keys are automatically rotated every 30 days. Users can also opt for encryption through a customer-managed key. Here are some points to remember:

• Use Tri-Secret encryption when using Snowflake alongside Azure or AWS.
• When replicating a database to a different account, make sure that you enable the Tri-Secret feature in the destination account.
• When using customer-managed keys or CMK, use the automatic key rotation feature that comes from the cloud provider.
• Use the periodic rekeying feature in Snowflake if your organization needs the rekeying of data after specific intervals.
• Use the built-in encryption functions of Snowflake to provide additional encryption to specific columns in addition to the transparent encryption that Snowflake provides by default.

Interesting Read:"Understanding the Impact of GDPR on Data Privacy"

Monitoring

Once you have all these Snowflake security best practices in place, you need to set up proper monitoring to understand if your measures are working correctly. Monitoring can be crucial for the compliance and audit requirements of your organization.

To facilitate this, one thing that can come in handy is Snowflake's built-in shared account usage database, which can give you access to a year's worth of audit logs. The login history logs all connections made with Snowflake and a query history that logs every Snowflake query. You can also retain audit data for more than a year by moving this data into a custom database. You can use the user account usage view to get user-specific access information or access the grants account usage views to see where each user has access.

Row-Level Security

Row-level security in Snowflake is a feature that enables organizations to control access to specific rows within a database table based on a user's role or other attributes. This enhances data security by preventing unauthorized access to sensitive information, and it enables organizations to enforce compliance with data privacy regulations such as GDPR and HIPAA. By limiting access to only the data that a user needs to perform their job, row-level security also helps to prevent accidental data breaches or other security incidents.

The importance of row-level security in Snowflake is due to the increasing need for organizations to secure their data and ensure that it is not misused. With the rise of big data and the Internet of Things (IoT), organizations are collecting and storing massive amounts of data, much of which is sensitive. This data needs to be protected from unauthorized access, theft, or accidental misuse, which can result in data breaches, loss of reputation, and potential financial penalties.

Snowflake provides a flexible and scalable security architecture that allows organizations to define and enforce security policies at the row level. This allows organizations to control access to data based on a user's role, department, or other attributes, which helps to ensure that only authorized users can access sensitive information.

To implement row-level security in Snowflake, you need to follow these steps:

1. Define the security policy: Specify the conditions under which a user or role can access specific rows of data within a database table. This policy can be based on user attributes, such as role or department, or on data values within the table.
2. Create a security policy function: Write a function that implements the security policy defined in step 1. This function should return either 'ALLOW' or 'DENY' based on the evaluation of the policy conditions.
3. Create a security policy: Use the CREATE SECURITY POLICY command to create a security policy based on the function created in step 2.
4. Assign the security policy: Use the GRANT command to assign the security policy to a specific role or user.
5. Test the security policy: Verify that the security policy is working as expected by executing queries as the user or role assigned in step 4 and checking the results returned.

Snowflake column-level security

As the name suggests, Snowflake can mask entire columns containing sensitive data such as IP Address, SSID, Bank Details, Card Details, Address and so on. These are implemented in two main ways:

1. Dynamic Data Masking

Masking policies are implemented to prevent sensitive data from being shown while querying. Snowflake provides data masking with little to no coding needed. Alternatively, you can hardcode a query to stop private data from being shown with a simple query initiating dynamic data masking.

2. External Tokenization

Another way to implement the best version of column-level security in Snowflake is to tokenize your data before you upload it to Snowflake. This double-layer security will guarantee that no one will be able to decrypt your data. Along with Snowflake’s column masking, you can tokenize your data using Protecto and also integrate their services to your Snowflake data warehouse since their service is agentless, making it integrate easily into any cloud service.

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In conclusion, row-level security in Snowflake is a critical tool for organizations that need to secure their data and ensure compliance with data privacy regulations. It enables organizations to control access to specific rows within a database table based on a user's role or other attributes, which helps to prevent unauthorized access to sensitive information. By following the steps outlined above, organizations can implement row-level security in Snowflake and take a crucial step towards securing their data and protecting their reputation.

Suggested Read,

Shadow AI: The Emerging, Invisible Problem Putting Your Company's Data at Risk

8 Simple Tips for a Snowflake Data Engineer

Snowflake Security Best Practices: FAQs

How do I ensure my users only have access to the data they're authorized to see? 

Snowflake provides several ways to ensure that users only have access to the data they're authorized to see, including role-based access control, row-level security, and data masking. With role-based access control, you can assign specific roles to users and limit the data they can access based on those roles. Row-level security allows you to restrict access to specific rows in a database table based on a user's role or other attributes. Data masking enables you to mask sensitive data so that it is not viewable by unauthorized users.

What methods of authentication are available with Snowflake? 

Snowflake supports a variety of authentication methods, including single sign-on (SSO) using SAML, Snowflake internal user authentication, and Snowflake external OAuth authentication. You can also authenticate using Snowflake authentication methods such as username/password or Snowflake access keys.

Can I make sure my sensitive data at rest is encrypted? 

Yes, Snowflake provides built-in encryption for sensitive data at rest, which includes all data stored within Snowflake databases. By default, Snowflake uses Advanced Encryption Standard (AES) 256-bit encryption to secure all data stored within Snowflake. This encryption is applied automatically, ensuring that sensitive data is protected even if a database or table is inadvertently left unsecured. Additionally, Snowflake allows you to configure your own encryption keys, providing even greater security for your sensitive data.

Key Takeaways

Remaining secure is crucial to meet compliance requirements and to keep the reputation of your business intact. While following these Snowflake security best practices might be enough to secure your data currently, it is important to remember that security threats evolve. Therefore, your strategies should evolve accordingly. Stay updated about the latest security trends and come up with proactive solutions.

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