13 Best Snowflake Security Practices: Expert Guide to Keeping Your Data Safe

With 47% of corporate cloud data classified as sensitive and high-profile breaches making 2024 headlines, securing your Snowflake data isn’t optional – it’s business critical. The 2024 Snowflake-related security incidents affecting hundreds of organizations proved that even minor security gaps can lead to massive data exposure, financial losses, and reputational damage. 

The good news? There are a lot of steps you can take to take your tech stack from having average to stunning security. 

This guide will walk you through the thirteen best practices to keep your Snowflake platform secure: 

1. Practicing good network security
2. Monitoring all user sessions
3. Maintaining strong identity and access management
4. Regularly auditing user roles 
5. Implementing and using object-level access control
6. Implementing and using column-level access control
7. Investing in MFA
8. Implementing authentication and SSO
9. Always using data encryption 
10. Prevent data exfiltration 
11. Implement data masking and tokenization
12. Plan for incident responses
13. Amp up your third-party integration security 

Read on to learn exactly how to build enterprise-grade security for your Snowflake platform. 

Practice Good Network Security 

The start of a solidly secured Snowflake tech stack is network security. If you decide to implement any practices from this list, pick this one. You need to be certain that your network connections to Snowflake are completely secure and only accessible by trusted sources. 

How can you do that? 

• Create a secure network architecture: Use AWS PrivateLink, Azure Private Link, or Google Cloud Private Service Connect to establish private connectivity that bypasses the public internet. 
• Monitor network traffic: Use Snowflake’s LOGIN_HISTORY to identify any unusual connection patterns or geographic anomalies. 
• Enable VPN requirements: Require VPN connections for remote access to add an additional layer of security.
• Use IP allowlisting: Add network policies in Snowflake to restrict access by IP address or CIDR blocks: 

CREATE NETWORK POLICY office_only
ALLOWED_IP_LIST = ('192.168.1.0/24', '10.0.0.0/16');
ALTER ACCOUNT SET NETWORK_POLICY = office_only;

Doing this means you’ll be much better prepared to defend against cyber attacks. 

Monitor All Sessions

We touched on this some in the point above, but regular user session monitoring deserves a point all on its own. You should be controlling and monitoring user sessions. This is another key step to take to prevent unauthorized access, and to quickly spot suspicious activity. 

Here’s how to get started: 

• Active session monitoring: Use Snowflake’s ACCOUNT_USAGE views to track login patterns, query history, and data access. Set up alerts for logins from unusual locations or at unusual hours for high-security tables. 
• Terminate suspicious or unauthorized activity: Invest in a third-party tool that will automatically log out any activity that appears suspicious or unauthorized. 
• Use session timeouts: Automatically log users out after a set period of inactivity. 

Maintain Strong Identity and Access Management Policies

Identity and access management (IAM) means controlling who can access what resources in your Snowflake platform. It means only letting certain users access and manage cloud data, which reduces the risk of potential data breaches. 

Implement and maintain strong IAM policies by: 

• Implementing role-based access control (RBAC): Create granular roles following the principle of least privilege: 

CREATE ROLE data_analyst;
GRANT USAGE ON WAREHOUSE compute_wh TO ROLE data_analyst;
GRANT USAGE ON DATABASE sales_db TO ROLE data_analyst;
GRANT SELECT ON sales_db.public.customers TO ROLE data_analyst;

• Using account-level security features: Use Snowflake’s built-in password policies, session timeouts, and login failure tracking features. 

ALTER ACCOUNT SET PASSWORD_POLICY = (
    MIN_LENGTH = 12,
    MAX_AGE_DAYS = 90,
    HISTORY = 5
);

• Implementing custom roles: Avoid default roles like ACCOUNTADMIN for daily operations. Instead create custom roles that better match your organizational structure. 

By only allowing users access to the data that they need to complete their jobs you massively reduce the risk of data exposure. 

Regularly Audit Users and Roles

Create regular audits to review your current users and their roles’ level of access. This is essential for maintaining a secure environment. Review what levels of permissions each user has and confirm that roles have been correctly defined. 

The best way to stay on top of user and role permissions is to: 

• Implement automated user provisioning: This means new users are added securely and with the appropriate permissions, no extra work needed from you. 
• Create role hierarchies: To simplify permission management and have users inherit the right access permissions. 
• Review policies on a semi-regular basis: Depending on your company’s size and how many new users added or old users removed, audit roles and permissions on a quarterly to yearly basis. 

Use Object-Level Access Control

Object-level control lets you manage what users have access to specific Snowflake account objects. Basically, it means only certain users can access sensitive data. As you might have noticed, granular controls are key to maintaining security. 

Here’s how to implement this to your system: 

• Review object access control: Just as you could with any role permissions, make sure you regularly review and update object ownership permissions. 
• Be judicious with ownership privileges: Assign your object permissions carefully. Users should only have access to data they need to complete their tasks. 

Use Column-Level Access Control

Column-level access control is similar to object-level in that you can assign and limit certain levels of data access based on user needs. 

Implementation looks a bit different: 

• Assign column-level permissions: Restrict access to certain fields with column-level permissions. 
• Use column masking: This will hide any sensitive data so only authorized users can see that info. 

Invest in Multi-Factor Authentication (MFA) 

Multi-Factor Authentication (MFA) means requiring multiple forms of verification for user sign-ins, making it much more difficult for unauthorized users to gain access to your Snowflake platform. 

MFA is deceptively simple. The trick is getting your employees to use it. 

Here’s how to ensure MFA is widely used across your entire organization: 

• Invest and enforce MFA: Make sure all users are required to use MFA. 
• Adopt adaptive MFA: Adaptive MFA adjusts authentication methods based on assessed risk levels, providing an added level of security. 

Pro tip: For service accounts that can’t use interactive MFA, implement key pair authentication instead of passwords. 

Implement Authentication and Single Sign-On (SSO) 

Authentication and Single Sign-On (SSO) make it easier to identify user identities. A centralized authentication management system means you don’t have to worry about user permissions being granted to an unauthorized user. 

Best practices for authentication and SSO include: 

• Encouraging strong authentication methods: Have your employees use strong, unique passwords, and make sure they regularly update their login information. 
• SSO integration: Centralize your authentication systems by integrating Snowflake with your SSO provider. 

Always Use Data Encryption 

You should always be using data encryption. It’s crucial for keeping your data safe and secure at all times, and means that if your data is ever intercepted, it can’t be read. 

The key to data encryption is that you need it all times, both when your data is at rest and in transit. Here’s how to do that: 

• For data at rest: Snowflake automatically encrypts all data at rest using AES-256 encryption with hierarchical key management. 
• For data in transit: Snowflake uses TLS 1.2+ for all connections.
• For additional security: Use client-side encryption before loading sensitive data. Or use Snowflake’s External Functions with customer-managed keys for field-level encryption. 

Prevent Data Exfiltration 

Preventing data exfiltration is essential to keep your data from being stolen or extracted. The best way to keep everything secure here is to: 

• Monitor large data exports: Set up alerts for queries that return usually large result sets or access multiple sensitive tables. 

-- Monitor queries returning >100K rows from sensitive tables
SELECT query_text, rows_produced, user_name, start_time
FROM snowflake.account_usage.query_history
WHERE rows_produced > 100000
AND query_text ILIKE '%sensitive_table%';

• Implement data loss prevention (DLP) policies: These policies are pre-defined rules that are key to helping you monitor and restrict data transfers. 
• Regularly audit your system: Recurring auditing and monitoring means you can stay on top of data access and transfers so you’re well positioned to respond to potential exfiltrations. 

Implement Data Masking & Tokenization

Data masking and tokenization let you add critical layers of protection to keep sensitive information hidden even from authorized users who don’t need to see the actual values. 

Implement these best practices to make take this extra step: 

• Dynamic data masking: Snowflake’s dynamic data masking automatically hides sensitive data based on user roles without changing the underlying data. 

-- Create a masking policy for credit card numbers
CREATE MASKING POLICY credit_card_mask AS (val STRING)
RETURNS STRING ->
  CASE
    WHEN CURRENT_ROLE() IN ('FINANCE_ADMIN', 'COMPLIANCE_OFFICER') THEN val
    ELSE 'XXXX-XXXX-XXXX-' || RIGHT(val, 4)
  END;

-- Apply the policy to sensitive columns
ALTER TABLE customer_data MODIFY COLUMN credit_card_number
SET MASKING POLICY credit_card_mask;

• Column-level tokenization: For highly sensitive data like Social Security numbers or patient IDs, implement tokenization before loading the data into Snowflake. 

-- Example of applying masking to PII data
CREATE MASKING POLICY ssn_tokenize AS (val STRING)
RETURNS STRING ->
  CASE
    WHEN CURRENT_ROLE() = 'HR_ADMIN' THEN val
    WHEN CURRENT_ROLE() = 'MANAGER' THEN 'XXX-XX-' || RIGHT(val, 4)
    ELSE 'XXX-XX-XXXX'
  END;

Before implementing these changes, just make sure to test your masking policies thoroughly, and that you document which data elements are masked and who has access to unmasked values. 

Plan for Incident Responses

The best way to handle an incident is to have a plan prepared prior. A comprehensive response plan means your team can quickly identify and contain security incidents before they escalate. 

Here’s how to do that: 

1. Step one: establish detection capabilities. Do this by setting up automated monitoring for suspicious activities that could indicate security incidents: 

-- Monitor for unusual access patterns
CREATE OR REPLACE TASK security_anomaly_detection
  WAREHOUSE = security_wh
  SCHEDULE = '5 MINUTE'
AS
SELECT
    user_name,
    COUNT(*) as login_attempts,
    LISTAGG(DISTINCT client_ip, ', ') as ip_addresses
FROM snowflake.account_usage.login_history
WHERE event_timestamp >= DATEADD('minute', -5, CURRENT_TIMESTAMP())
GROUP BY user_name
HAVING COUNT(*) > 10 OR COUNT(DISTINCT client_ip) > 3;

2. Step two: Create a clear response procedure. Document each escalation path and response steps so anyone can replicate each step. Break this down into the initial minutes to hours after crisis:
   1. First 0-15 minutes: Initial assessment. Identify the scope and severity with Snowflake’s query history and access logs. 
   2. Second 15-30 minutes: Containment. Disable compromised accounts, revoke suspicious sessions, and implement emergency access restrictions. 
   3. Next 30 minutes to four hours: Investigation. Use Snowflake’s audit logs to trace unauthorized access. 
   4. Next four to 24 hours: Recovery. Restore normal operations, update security controls, and implement additional monitoring. 
3. Step three: Create an incident response toolkit. Prepare pre-written SQL queries for rapid incident analysis: 

-- Quickly identify data accessed by a compromised account
SELECT
    query_text,
    database_name,
    schema_name,
    rows_produced,
    execution_time,
    start_time
FROM snowflake.account_usage.query_history
WHERE user_name = 'SUSPECTED_COMPROMISED_USER'
  AND start_time >= DATEADD('day', -7, CURRENT_TIMESTAMP())
  AND query_type = 'SELECT'
ORDER BY start_time DESC;

4. Step four: Create communication channels. Create clear communication channels with all departments (legal, compliance, executive), and educate each on breach notification requirements. 

Third-Party Integration Security

Third-party integrations are often one of Snowflake’s biggest security vulnerabilities. Securing these connections requires careful management and monitoring: 

• Secure service account management: Create dedicated service accounts for each integration that provide only minimal required permissions: 

-- Create integration-specific roles
CREATE ROLE etl_service_role;
CREATE ROLE bi_tool_role;

-- Grant only necessary permissions
GRANT USAGE ON WAREHOUSE etl_wh TO ROLE etl_service_role;
GRANT USAGE ON DATABASE staging TO ROLE etl_service_role;
GRANT INSERT, SELECT ON staging.raw.* TO ROLE etl_service_role;

-- Create service users with strong authentication
CREATE USER fivetran_service
  PASSWORD = 'ComplexPassword123!'
  DEFAULT_ROLE = etl_service_role
  MUST_CHANGE_PASSWORD = FALSE;

• Implement API key rotation: Use regular rotation schedules for all service account credentials. 
• Monitor-third party access patterns: Create alerts for unusual activity from service accounts: 

-- Monitor for service accounts accessing unexpected data
SELECT
    user_name,
    database_name,
    schema_name,
    table_name,
    COUNT(*) as access_count
FROM snowflake.account_usage.access_history
WHERE user_name LIKE '%_SERVICE'
  AND query_start_time >= DATEADD('day', -1, CURRENT_TIMESTAMP())
GROUP BY 1,2,3,4
HAVING COUNT(*) > NORMAL_THRESHOLD;

• Create a vendor risk assessment checklist: Before connecting with any third-party tool, evaluate their security certifications (SOC 2, ISO27001), data handling practices, and incident response abilities. Require vendors to notify you of any security incidents that could affect your platform. 

Take Your Snowflake Security to the Next Level

Keeping your Snowflake data secure can be intimidating. There’s always more users and account permissions to manage, and implementing all these practically manually requires significant resources. 

When evaluating security tools to help with this (carefully following the vendor assessment practices above), look for solutions that provide: 

• Traffic alerts for suspicious or unmonitored activity 
• User permission monitoring and anomaly detection
• Automated compliance reporting for SOX, PCI DSS, and other financial regulations

Tools like Yuki exemplify these security-first principles with SOC 2 compliance, transparent security practices, and integration monitoring that actually strengthens your security strategy. Previous clients have seen savings up to 30% – all while reducing security risks. 

Curious to see how a properly vetted security partner can help? Reach out for your free demo.