SQL Server offers a wide range of native security features that can help protect your data at rest, in transit, and during access. This post explores each major feature in SQL Server.
1. Transparent Data Encryption (TDE)
What it does: Encrypts the entire database, transaction log, and backup files at the file level.
- Best suited for: Disk file(mdf, ldf files) and database backup protection
- Limitations: Does not encrypt data in memory or in transit
2. Always Encrypted
What it does: Encrypts specific columns at the client level; the SQL Server engine never sees decrypted data.
- Best suited for: Finance, healthcare, and sensitive data scenarios where even DBAs should not see the data
- Limitations: Requires application changes; limited operations on encrypted columns.
3. Always Encrypted with Secure Enclaves
What it does: Always Encrypted with Secure Enclaves allows sensitive data to be encrypted on the client side while enabling rich computations such as pattern matching and range comparisons within a secure enclave inside SQL Server. This balances privacy and functionality for highly confidential data scenarios.
- Best suited for:
- Handling highly sensitive data (e.g., credit card numbers, social security numbers)
- Performing operations like LIKE, sorting, and range queries on encrypted data
- Limitations:
- Requires Enterprise Edition and Windows Server with virtualization-based security (VBS)
- Setup and infrastructure are more complex than basic Always Encrypted
4. Dynamic Data Masking (DDM)
What it does: Masks query results based on user permissions, without altering the stored data.
- Best suited for: Developer or support environments
- Limitations: Can be bypassed with certain permissions or functions; masks SELECT output only
5. Row-Level Security (RLS)
What it does: Filters rows based on user context, restricting access inside the database engine.
- Best suited for: Multi-tenant databases or departmental data segregation
- Limitations: May impact performance; logic complexity requires careful design
6. SQL Server Audit
What it does: Logs access and changes to the database, including logins and permission modifications.
- Best suited for: Compliance and activity monitoring
- Limitations: Requires Enterprise edition; log management needed for scale
7. Ledger (SQL Server 2022+)
What it does: Creates immutable, blockchain-like records of all changes to a table.
- Best suited for: Tamper-evident auditing in financial or legal systems
- Limitations: Enterprise edition only; increased storage cost due to history tracking
8. Data Classification and Sensitivity Labels
What it does: Tags sensitive columns for governance and compliance awareness.
- Best suited for: Data governance, GDPR/CCPA readiness
- Limitations: Does not enforce access control; intended for visibility and reporting
Feature Comparison Table
| Feature | Encrypts Data at Rest | Encrypts Data in Transit | Protects from DBAs | Granularity | Audit Support |
|---|---|---|---|---|---|
| Transparent Data Encryption (TDE) | ○ | × | × | Database | × |
| Always Encrypted | ○ | ○ | ○ | Column | × |
| Always Encrypted with Secure Enclaves | ○ | ○ | ○ | Column | × |
| Dynamic Data Masking (DDM) | × | × | △ | Column | × |
| Row-Level Security (RLS) | × | × | △ | Row | × |
| SQL Server Audit | × | × | × | Server/Database | ○ |
| Ledger | × | × | ○ (Tamper-evident) | Table | ○ |
| Data Classification and Sensitivity Labels | × | × | × | Column | ○ (Visibility only) |
How to Choose the Right Security Feature
- To protect the entire database and backups: Use Transparent Data Encryption (TDE).
- To protect data from even DBAs: Use Always Encrypted or Always Encrypted with Secure Enclaves.
- To show masked values for non-privileged users (e.g., support teams): Use Dynamic Data Masking (DDM).
- To control row-level access within a shared table: Use Row-Level Security (RLS).
- To monitor database access and changes for compliance: Use SQL Server Audit.
- To create tamper-proof audit logs with integrity: Use Ledger (SQL Server 2022+).
- To improve data visibility and classification: Use Data Classification and Sensitivity Labels.