🛩️Cloud Security Baseline for Azure Databricks
Last updated
Last updated
Identity and Access Management (IAM)
Control: Use Azure Active Directory (AAD) for authentication and authorization.
Recommendation: Integrate Azure Databricks with AAD to manage user access and permissions centrally.
Control: Implement Role-Based Access Control (RBAC).
Recommendation: Define roles and assign permissions based on the principle of least privilege. Regularly review and update access roles.
Control: Enable Multi-Factor Authentication (MFA).
Recommendation: Require MFA for all users accessing the Databricks workspace.
Network Security
Control: Use Virtual Network (VNet) integration.
Recommendation: Deploy Azure Databricks in a VNet to isolate and secure network traffic.
Control: Implement Private Link.
Recommendation: Use Azure Private Link to securely connect to data sources and other services without exposing them to the public internet.
Control: Configure Network Security Groups (NSGs).
Recommendation: Use NSGs to control inbound and outbound traffic to and from the Databricks VNet.
Data Protection
Control: Encrypt data at rest.
Recommendation: Ensure that all data stored in Azure Databricks is encrypted using Azure Storage Service Encryption (SSE).
Control: Encrypt data in transit.
Recommendation: Use TLS/SSL to encrypt data during transmission between clients, clusters, and data sources.
Control: Implement data masking and anonymization.
Recommendation: Apply data masking techniques to protect sensitive data in notebooks and jobs.
Logging and Monitoring
Control: Enable Diagnostic Logging.
Recommendation: Enable logging for Azure Databricks to capture and store detailed logs of user activities, cluster events, and other operations.
Control: Use Azure Monitor.
Recommendation: Integrate Azure Databricks with Azure Monitor to track performance metrics, set up alerts, and monitor the health of clusters.
Control: Enable Azure Security Center.
Recommendation: Use Azure Security Center to monitor security posture, detect threats, and receive security recommendations.
Compliance and Governance
Control: Implement Azure Policy.
Recommendation: Use Azure Policy to enforce compliance with organizational and regulatory requirements. Define and apply policies to Azure Databricks resources.
Control: Regular Security Assessments.
Recommendation: Conduct regular security assessments and audits to ensure compliance with security policies and identify potential vulnerabilities.
Control: Data Governance.
Recommendation: Use Azure Purview to manage and govern data across the Databricks environment, ensuring data lineage, classification, and protection.
Endpoint Security
Control: Secure Development and Testing Environments.
Recommendation: Isolate development and testing environments from production to prevent accidental exposure of sensitive data.
Control: Use Endpoint Protection.
Recommendation: Ensure that all endpoints accessing the Databricks environment have endpoint protection and antivirus software installed and updated.
Backup and Recovery
Control: Regular Backups.
Recommendation: Implement a backup strategy to regularly back up critical data and configurations. Store backups in a secure and geographically redundant location.
Control: Disaster Recovery Planning.
Recommendation: Develop and test a disaster recovery plan to ensure quick recovery in case of data loss or service disruption.
This baseline outlines the minimum-security requirements for your Azure Databricks environment. It leverages the Microsoft Cloud Security Benchmark (MCSB) and best practices to establish a strong security foundation. Remember, this is a starting point and may need to be adjusted based on your specific needs and regulations.
Key areas to consider:
Identity and Access Management (IAM):
Use Azure Active Directory (AAD) for user authentication and authorization.
Implement least privilege by assigning roles with only the necessary permissions.
Enable Multi-Factor Authentication (MFA) for all users.
Network Security:
Deploy your workspace in a private virtual network with no inbound access.
Utilize allow-lists and deny-lists to control network access to the workspace.
Restrict public access to Azure Databricks workspace resources.
Data Security:
Encrypt data at rest and in transit using Azure Key Vault for key management.
Configure workspace ACLs to control access to data objects (folders, tables).
Monitor data access activities through Azure Databricks audit logs.
Cluster Security:
Use Azure Databricks managed clusters for enhanced security features.
Configure automatic cluster termination after idle periods.
Disable public access to cluster nodes.
Monitoring and Logging:
Enable Azure Databricks audit logging to track user activity and API calls.
Integrate Azure Databricks logs with Azure Monitor for centralized logging and analysis.
Utilize Microsoft Defender for Cloud to continuously monitor your Azure Databricks environment for threats.
Key Areas to Address:
Identity and Access Management:
Use Azure Active Directory (AAD) for user authentication and authorization.
Implement least privilege access control (RBAC) for users and groups.
Enable Multi-Factor Authentication (MFA) for all users.
Network Security:
Deploy your workspace in a private virtual network with no public inbound access.
Configure Network Security Groups (NSGs) to restrict inbound and outbound traffic.
Use Azure Private Link to connect securely to other Azure services without exposing them to the internet.
Data Security:
Encrypt all data at rest and in transit using Azure Key Vault for key management.
Classify data based on sensitivity and implement appropriate access controls.
Regularly monitor data access and activity for suspicious behavior.
Monitoring and Logging:
Enable Azure Databricks audit logging to track user activity and cluster events.
Integrate Azure Databricks logs with Azure Monitor for centralized monitoring and alerting.
Use Microsoft Defender for Cloud to continuously monitor your environment for potential threats.
Security Automation:
Utilize Azure Policy to enforce security best practices through automated rules.
Configure alerts for suspicious activity and security incidents.
Regularly review and update security policies to adapt to evolving threats.
Additional Considerations:
Just-In-Time (JIT) Access: Configure JIT access for clusters to minimize the time workspace resources are available.
Customer-Managed Keys (CMK): Use CMK in Azure Key Vault for additional control over encryption keys.
Data Loss Prevention (DLP): Implement DLP policies to prevent sensitive data exfiltration.
Threat Protection: Utilize Microsoft Defender for Cloud to identify and mitigate potential security threats.
Remember:
This is a baseline, continuously evaluate and improve your security posture.
Regularly review and update your security policies and procedures.
Conduct security awareness training for all users who access Azure Databricks.
By implementing these minimum baseline recommendations, you can significantly improve the security of your Azure Databricks workspace and protect your valuable data assets.
Core Controls:
Identity and Access Control (IAM):
Implement Azure Active Directory (AAD) for user authentication and authorization.
Enforce least privilege principle with granular access controls (RBAC) for users, groups, and services.
Utilize Azure Multi-Factor Authentication (MFA) for all access.
Network Security:
Deploy your workspace in a private Azure Virtual Network (VNet) with no inbound access.
Configure Network Security Groups (NSGs) to restrict access to workspace resources.
Utilize Azure Private Endpoints for secure communication between your VNet and Azure Databricks services.
Data Security:
Enable customer-managed keys (CMK) for encryption of data at rest and in transit.
Classify data based on sensitivity and implement appropriate access controls.
Configure data access logging for audit purposes.
Monitoring and Logging:
Enable Azure Databricks audit logging for workspace activity and user actions.
Integrate Azure Databricks logs with Azure Monitor for centralized management and analysis.
Utilize Microsoft Defender for Cloud to monitor security posture and identify potential threats.
Security Posture Management:
Regularly review security configurations for Azure Databricks and underlying Azure resources.
Implement automated security assessments using tools like Azure Security Center.
Maintain a process for patching vulnerabilities in Azure Databricks and its dependencies.
Additional Considerations:
Just-in-Time (JIT) Access: Utilize Azure Databricks workspace configurations with JIT access for clusters.
Data Exfiltration Prevention: Implement Data Loss Prevention (DLP) policies to prevent sensitive data from leaving your Azure environment.
Threat Protection: Utilize Azure Sentinel for advanced threat detection and incident response.
Remember: This is a starting point. Your specific security baseline will depend on your organization's risk tolerance, regulatory requirements, and data sensitivity. Continuously evaluate and update your baseline as needed.