✈️Cloud Security Baseline for Azure Data Factory
Last updated
Last updated
Identity and Access Management (IAM)
Control: Use Azure Active Directory (AAD) for authentication.
Recommendation: Integrate Azure Data Factory with AAD to manage user authentication and authorization 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 and permissions.
Control: Use Multi-Factor Authentication (MFA).
Recommendation: Require MFA for all users accessing ADF to enhance security.
Data Protection
Control: Encrypt data at rest.
Recommendation: Ensure that all data handled by ADF is encrypted using Azure Storage Service Encryption (SSE) with either Microsoft-managed keys or customer-managed keys stored in Azure Key Vault.
Control: Encrypt data in transit.
Recommendation: Use HTTPS to secure data in transit between ADF and data sources or destinations. Enforce the use of secure transfer (HTTPS) for all data movements.
Control: Secure sensitive data.
Recommendation: Use Azure Key Vault to manage and store secrets, keys, and connection strings securely. Avoid storing sensitive data in plain text within ADF pipelines or datasets.
Network Security
Control: Use Virtual Network (VNet) integration.
Recommendation: Deploy ADF with VNet integration to securely access resources within the VNet.
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 ADF integration runtime.
Logging and Monitoring
Control: Enable Diagnostic Logging.
Recommendation: Enable logging for ADF to capture and store detailed logs of activities and operations. Use Azure Storage, Log Analytics, or Event Hubs to retain logs.
Control: Use Azure Monitor.
Recommendation: Integrate ADF with Azure Monitor to track performance metrics, set up alerts, and monitor the health of pipelines and activities.
Control: Enable Azure Security Center.
Recommendation: Use Azure Security Center to monitor the security posture of ADF, 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 ADF 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 processed by ADF, 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 ADF have endpoint protection and antivirus software installed and updated.
Backup and Recovery
Control: Regular Backups.
Recommendation: Implement a backup strategy to regularly back up ADF configurations and metadata. Use Azure Backup and ensure backups are stored securely and encrypted.
Control: Disaster Recovery Planning.
Recommendation: Develop and test a disaster recovery plan to ensure quick recovery in case of data loss or service disruption.
Core Controls Covered in the Baseline:
The baseline outlines essential security areas you should address in your Data Factory environment. These core controls closely resemble those for Azure Databricks:
Identity and Access Control (IAM):
Implement Azure Active Directory (AAD) for user authentication and authorization.
Enforce the principle of least privilege with granular access controls (RBAC) for users, groups, and services.
Utilize Azure Multi-Factor Authentication (MFA) for all access.
Network Security:
Restrict Data Factory access by deploying it within a private Azure Virtual Network (VNet).
Configure Network Security Groups (NSGs) to limit access only to authorized resources within the VNet.
Data Security:
Implement customer-managed keys (CMK) for data encryption at rest and in transit.
Classify data sensitivity and implement proper access controls based on that classification.
Configure data access logging to enable audit trails.
Monitoring and Logging:
Enable Azure Data Factory audit logging to track workspace activity and user actions.
Integrate Data Factory logs with Azure Monitor for centralized management and analysis of security events.
Utilize Microsoft Defender for Cloud to monitor your security posture and identify potential threats.
Security Posture Management:
Regularly review security configurations for Data Factory and underlying Azure resources.
Implement automated security assessments using tools like Azure Security Center.
Maintain a process for patching vulnerabilities in Data Factory and its dependencies.
Additional Considerations:
Conditional Access: Explore Azure AD Conditional Access to set up restrictions based on user location or device type for accessing Data Factory.
Azure Policy: Utilize Azure Policy definitions to enforce security best practices and configurations for Data Factory.
Data Exfiltration Prevention: Implement Data Loss Prevention (DLP) policies to prevent sensitive data from leaving your Azure environment.
Encryption at Rest: Consider enabling encryption at rest for data landing zones in Azure Data Lake Storage Gen2 used by Data Factory pipelines.
Remember: This serves as a foundational guideline. 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.
Key Security Considerations for Data Factory:
Identity and Access Control (IAM):
Implement Azure Active Directory (AAD) for user authentication and authorization.
Use Azure RBAC (Role-Based Access Control) to grant least privilege access to Data Factory resources.
Enforce Multi-Factor Authentication (MFA) for all access.
Network Security:
Consider deploying Data Factory in a separate Azure Virtual Network (VNet) for enhanced isolation.
Utilize Network Security Groups (NSGs) to restrict access to Data Factory resources.
Data Security:
Configure Data Factory to use customer-managed keys (CMK) for encryption of data at rest and in transit.
Classify data based on sensitivity and implement appropriate access controls.
Enable data access logging for audit purposes.
Monitoring and Logging:
Enable Azure Data Factory audit logging to track workspace activity and user actions.
Integrate Data Factory logs with Azure Monitor for centralized management and analysis.
Utilize Azure Security Center or Microsoft Defender for Cloud to monitor security posture and identify threats.
Additional Considerations:
Implement Azure Data Factory workspace configurations with Just-in-Time (JIT) access for added security.
Regularly review and update your security configurations based on security best practices and identified threats.
Remember, this is a starting point. You can customize your baseline to address your specific needs and security posture.