Identify and manage privacy settings on data sources

When working with data in Azure, there are several services and platforms to consider, such as Azure SQL Database, Azure Data Lake Storage, and Azure Synapse Analytics. Each of these services has its own privacy settings, which can be configured to protect your data.

1. Azure SQL Database:

To manage privacy settings for Azure SQL Database, you can leverage the built-in features like Always Encrypted, Row-level security (RLS), and Dynamic Data Masking (DDM).

– Always Encrypted: This feature allows you to encrypt sensitive data at rest and in transit, ensuring that only authorized users can access it. You can implement client-side encryption, where the data is encrypted before it reaches the database, or use Azure Key Vault to manage the encryption keys.

– Row-level security (RLS): RLS enables you to define fine-grained access control policies at the row level. This ensures that users only have access to the data they are authorized to see based on their roles and permissions. RLS is especially useful in scenarios where multiple users or departments share the same database.

– Dynamic Data Masking (DDM): DDM allows you to define masking rules for sensitive data, preventing unauthorized users from viewing the actual values. Masking can be applied to specific columns or tables, ensuring that sensitive information remains protected.

2. Azure Data Lake Storage:

Azure Data Lake Storage provides a scalable and secure solution for storing and analyzing big data. To manage privacy settings in Data Lake Storage, you can utilize Access Control Lists (ACLs) and Azure Active Directory (AAD) integration.

– Access Control Lists (ACLs): ACLs let you define permissions at the file and directory level, allowing you to control who can read, write, and execute the data stored in Data Lake Storage. By assigning the appropriate permissions, you can ensure that only authorized users can access the data.

– Azure Active Directory (AAD) integration: By integrating Data Lake Storage with Azure Active Directory, you can enforce role-based access control and enable single sign-on for your users. This integration provides a centralized way to manage user access and ensures that data access is tied to user identities.

Power BI Data Sources:

With Power BI, you can easily connect to various data sources, including on-premises databases, cloud-based services, and online sources. To manage privacy settings in Power BI, you can use DirectQuery, Import, or Live Connection modes, depending on your data source and privacy requirements.

1. DirectQuery:

DirectQuery mode allows Power BI to query the data source directly and display near-real-time data. When using DirectQuery, data privacy settings can be configured at the data source level. This ensures that Power BI respects the privacy settings defined in the data source itself.

2. Import mode:

In Import mode, data is loaded into Power BI’s internal data model. To manage privacy settings in Import mode, you can leverage Power BI’s built-in features like Query Dependencies and Privacy Levels.

– Query Dependencies: Power BI automatically analyzes the queries used to import data and identifies dependencies on external data sources. Based on these dependencies, you can configure privacy settings to ensure sensitive data is protected.

– Privacy Levels: Power BI provides privacy level options to control the interaction between different data sources. By assigning privacy levels to your data sources, you can specify how sensitive data should be handled when combining data from multiple sources.

3. Live Connection mode:

Live Connection mode allows Power BI to establish a direct connection to a data source, such as Azure Analysis Services or SQL Server Analysis Services. When using Live Connection, privacy settings are managed within the data source itself, ensuring the security and privacy of the data.

In conclusion, identifying and managing privacy settings on data sources is crucial when designing and implementing enterprise-scale analytics solutions using Azure and Power BI. By leveraging the built-in security features and following best practices, you can ensure the confidentiality and integrity of your data throughout the analytics process.