Snowflake is a leading cloud-based data warehousing platform that offers businesses a secure and scalable data storage solution. Offered in a Software-as-a-Service or SaaS model, with its unique security architecture, Snowflake provides robust protection for sensitive data, making it a preferred choice for enterprises dealing with compliance-sensitive workloads. 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.
Ensuring strong Snowflake security is essential, as data breaches, unauthorized access, and compliance risks continue to rise. Implementing the Snowflake security best practices can help organizations safeguard their data while optimizing performance.
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.
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 increasing cyber threats, Snowflake data protection is more critical than ever. Enterprises face challenges such as unauthorized access, compliance violations, and data breaches. Additionally, Snowflake security concerns extend to insecure data transmission, weak authentication, and improper access management.
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.
Following a comprehensive Snowflake security checklist helps businesses address vulnerabilities while ensuring Snowflake HIPAA compliance and adherence to regulations like GDPR.
Key Challenges in Snowflake Security
There are many possible challenges present in the Security Protocols when utilized incorrectly by the users. Some of them are:
Weak Authentication
Simple username-password authentication is inadequate to secure Snowflake data. Attackers can easily exploit weak credentials, leading to data breaches.
Best Practices:
- Enforce multi-factor authentication (MFA) for all users.
- Implement OAuth authentication for secure access.
- Use Snowflake RBAC best practices to assign access privilege.
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.
Unauthorized Access
Malicious actors may gain access to Snowflake through compromised credentials or insider threats.
Best Practices:
- Utilize Snowflake row-level access control to limit access to data based on user roles.
- Implement Snowflake column-level access control for granular security.
- Apply external tokenization to mask sensitive data before storing it in Snowflake.
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 unauthorized access which may have been stopped earlier.
Protecto uses its 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
Organizations using Snowflake must adhere to regulations like HIPAA, GDPR, and CCPA. Non-compliance can result in legal penalties and reputational damage.
Best Practices:
- Use Snowflake encryption to protect sensitive data.
- Ensure Snowflake HIPAA compliance by following industry security standards.
- Implement Snowflake data encryption policies to secure information at rest and in transit.
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.
Insecure Data Transmission
If Snowflake data is transmitted through unsecured channels, it is vulnerable to interception and unauthorized modification.
Best Practices:
- Enable Snowflake external tokenization for secure data transfers.
- Utilize secure cloud storage access for Snowflake data loading.
- Restrict Snowflake network policies to known and trusted IP addresses.
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.
Snowflake Security Architecture
Snowflake employs a multi-layered security model that ensures comprehensive data protection. This security architecture includes:
1. Network Security
- Restrict access using network policies to allow only trusted IP ranges.
- Configure private connectivity to eliminate exposure to public endpoints.
- Secure cloud storage integration with encryption and authentication mechanisms.
2. Identity and Access Management (IAM)
- Implement Snowflake RBAC best practices to define granular access levels.
- Use SCIM integration or Active Directory for user synchronization.
- Leverage OAuth, key-pair authentication, and multi-factor authentication for secure logins.
3. Snowflake Data Encryption
- Utilize AES 256-bit encryption for securing data at rest.
- Implement Tri-Secret encryption for additional security layers.
- Enable customer-managed encryption keys (CMK) for greater control over data security.
Snowflake Row Level Security
Snowflake row-level security ensures that users can only access specific data rows based on defined criteria, such as department, geography, or user role.
Implementing Row-Level Security:
- Define a security policy specifying access conditions.
- Create a security function to evaluate user roles.
- Apply the CREATE SECURITY POLICY command in Snowflake.
- Assign the policy to relevant users and roles.
- Test access control measures to ensure proper enforcement.
Snowflake Column Level Security
Snowflake column-level security helps organizations protect sensitive data at the field level. This is implemented using dynamic data masking or snowflake external tokenization.
Techniques for Column-Level Security:
- Dynamic Data Masking: Prevents exposure of sensitive information by applying security policies.
- External Tokenization: Encrypts and replaces sensitive data with tokens before storing it in Snowflake.
Using Protecto’s intelligent tokenization, enterprises can ensure sensitive data protection in Snowflake, preventing unauthorized access while maintaining compliance.
Snowflake Security Best Practices Checklist
To maximize Snowflake security, organizations should follow these essential best practices:
- Enforce strong authentication methods (OAuth, MFA, key-pair authentication).
- Restrict access using Snowflake RBAC and role-based security policies.
- Monitor Snowflake access logs and analyze anomalies to prevent security breaches.
- Enable Snowflake encryption (AES 256-bit, Tri-Secret, CMK) for data at rest and in transit.
- Use network security policies to allow only trusted connections.
- Implement Snowflake row-level security for user-based data restrictions.
- Apply column-level access controls using dynamic data masking or tokenization.
- Ensure Snowflake HIPAA compliance and GDPR adherence.
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 accessing 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.
Snowflake Row-Level Security Access Control
Row-level security access control 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:
- 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 data values within the table.
- 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.
- Create a security policy: Use the CREATE SECURITY POLICY command to create a security policy based on the function created in step 2.
- Assign the security policy: Use the GRANT command to assign the security policy to a specific role or user.
- 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 Access Control
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:
- 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.
- External Tokenization
Another way to implement the best version of column-level security access control 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.
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 can I restrict data access in Snowflake?
Use role-based access control (RBAC), row-level security, and column-level security to limit user access.
Does Snowflake encrypt sensitive data?
Yes, Snowflake encryption uses AES 256-bit encryption and supports customer-managed keys for enhanced security.
How does Snowflake handle compliance with HIPAA and GDPR?
Snowflake provides built-in compliance measures, including encryption, access controls, and audit logs, to meet HIPAA and GDPR requirements.
What is Snowflake tokenization, and how does it improve security?
Snowflake tokenization replaces sensitive data with secure tokens, ensuring data remains protected even if accessed by unauthorized users.
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
Securing Snowflake requires a proactive approach to authentication, access management, encryption, and compliance. By implementing these Snowflake security best practices, businesses can safeguard sensitive data, maintain compliance, and reduce security risks.
Staying ahead of emerging threats and adapting to evolving Snowflake security architecture is crucial for maintaining a robust data protection strategy in Snowflake.
