Azure SQL is Retiring the “No Minimum TLS” (MinTLS None) Configuration
As part of the retirement of lower TLS versions 1.0 and 1.1 and the enforcement of 1.2 as the new default minimum TLS version, we will be removing the No Minimum TLS (MinTLS = “None” or "0") option and updating these configurations to TLS 1.2. No Minimum TLS allowed Azure SQL Database and Azure SQL Managed Instance resources to accept client connections using any TLS protocol version and unencrypted connections. Over the past year, Azure has retired TLS 1.0 and 1.1 for all Azure databases, due to known security vulnerabilities in these older protocols. As of August 31, 2025, creating servers configured with versions 1.0 and 1.1 was disallowed and migration to 1.2 began. With legacy TLS versions being retired, TLS 1.2 will become the secure default minimum TLS version for new Azure SQL DB and MI configurations and for all client-server connections. As a result, the MinTLS = None configuration option will default to 1.2, and existing servers configured with No Minimum TLS will need to connect using 1.2 or higher. Update (July 16, 2026): This blog post has been updated to clarify the expected configuration behavior associated with the minimum TLS 1.0 retirement. The content below reflects the latest guidance. What is changing? Creating new Azure SQL Database and Managed Instance servers with MinTLS = None previously allowed any TLS version and unencrypted connections. After July 31, 2026, we will change the default behavior of the minimum TLS value "None", for the creation of new SQL DB and MI resources using PowerShell, Azure CLI, and any other REST based interface. This configuration option has been removed from the Portal as part of the retirement of TLS versions 1.0 and 1.1. Attempts to CREATE or UPDATE an Azure SQL server with MinTLS = None will default to connecting with TLS 1.2. If the server parameter value for the minimum TLS is left blank, it will default to minimum TLS version 1.2. Attempts to connect with TLS 1.0, 1.1 or unencrypted connections will fail with “Error: 47072/171 on Gateway.” Effective date (retirement milestone) MinTLS = None (0) MinTLS left blank (defaults to supported minimum) Before 8/31/25 Any + Unencrypted Any + Unencrypted After 8/31/25 1.2 + Unencrypted 1.2 After July 31, 2026 1.2 TLS error: 47072/171 (for unencrypted connections) 1.2 In summary, after July 31, 2026, Azure SQL Database and Azure SQL Managed Instance will require all client connections to use TLS 1.2 or higher and unencrypted connections will be denied. The minimum TLS version setting will no longer accept the value "None" for new or existing servers and servers currently configured with this value will be upgraded to explicitly enforce TLS 1.2. Who is impacted? For most Azure SQL customers, there is no action required. Most clients already use TLS 1.2 or higher. After July 31, 2026, if your Azure SQL Database or Managed Instance is still configured with No Minimum TLS and using 1.0, 1.1 or unencrypted connections, it will automatically update to TLS 1.2 to reflect the current minimum protocol enforcement in client-server connectivity. We do recommend you verify your client applications – especially any older or third-party client drivers – to ensure they can communicate with TLS 1.2 or above. In some rare cases, very old applications, such as an outdated JDBC driver or older .NET framework version, may need an update or need to enable TLS 1.2. Conclusion This retirement is part of Azure’s broader security strategy to ensure encrypted connections are secure by modern encryption standards. TLS version 1.2 is more secure than older versions and is now the industry standard (required by regulations like PCI DSS and HIPAA). This change eliminates the use of unencrypted connections which ensure all database connections meet current security standards. If you’ve already migrated to TLS 1.2 (as most customers have), you will most likely not notice any change, except that the No Minimum TLS option will disappear from configurations.3 Reasons Enterprise SQL Server Migrations Slow Down - and How to Avoid Them
Summary Many of Enterprises around the globe have relied on SQL Server for over 3 decades to run their mission critical business applications. Their SQL Server estates face pressure from downtime risk, cost volatility, end of support timelines and modernization demands. As these customers get ready to modernize their data to use the latest capabilities of A.I and cloud native application trends, they want to migrate and modernize their SQL Servers to use Azure SQL with a modernization strategy built on confidence of customer success. Enterprise migrations rarely fail because of migration tools. They slow down because organizations struggle to answer three questions: How much downtime can we tolerate? What will it cost after migration? Are we choosing the right target platform? The organizations that answer these questions early move faster and with less risk. For the DB Administrators, Data architects, application architect and cloud-cost decision makers there are important technical considerations before, during and after data modernization to avoid long term costs and operational concerns. The Microsoft SQL Team has helped many customers modernize their SQL. We discuss important guidelines that can help resolve the 3 major concerns that block or slow SQL Server migration and modernization in Enterprises. This is covered in the episode of DataExposed for which this companion blog goes into the details. What are important triggers that cause customers and partners to consider SQL modernization? There are many business triggers that force Enterprises to migrate their data to public cloud. As SQL Server 2012 to SQL Server 2016 are already in the end of support stage of their lifecycle, customers need to upgrade SQL Server in place or migrate to AzureSQL. Due to cyber security threats, customers are feeling more vulnerable to attackers. Moving their data into a secure environment is essential for protecting not just their data but their business. Customers are reporting the need to free up IT dollars to invest into other parts of the business that may need it more. These may be anything from datacenter contract expirations, need for Hardware refreshes to software license renewals. As the business grows or becomes cyclical, there is surge in demand. Capacity constraints become a barrier for such expansions. These are triggers that cause them to rethink their data modernization strategy. Data modernization and moving the data to a elastic, scalable, secure and resilient data platform such as Azure SQL, becomes essential. The Three Migration Blockers However, data modernization and migration is not without any risk. Based on our customers experience, here are three key reasons that we have commonly encountered that halt or slow down SQL modernization. 1. Downtime Risk Business stakeholders often require strict service level commitments before authorizing production cutovers. Even when migrations are technically feasible, organizations may delay projects if they believe downtime windows could impact revenue, customer experience, or regulatory obligations. Most customers are still offered offline migration paths which can take hours to days, even though zero-downtime migrations are possible which take seconds to minutes. 2. Cost uncertainty Many modernization projects are approved based on expected cost savings. However, if infrastructure sizing, licensing assumptions, storage consumption, or disaster recovery requirements are not evaluated properly, the actual operational cost can exceed initial expectations. Cost uncertainty often slows executive approval processes and extends migration timelines. 3. Compatibility and Feature Fit When migrating SQL Server, Azure SQL has several deployment offerings from IaaS to PaaS. These include SQL Server on Azure VM, Azure SQL Managed Instance, Azure SQL DB Hyperscale and Azure SQL in Microsoft Fabric. Many customers maybe using SQL Server features like Cross-database queries, CLR, SSIS, SQL Agent, and linked servers. They make a safe decision to lift and shift migrate to SQL Server on Azure VMs IaaS instead of modernizing to a PaaS service like Azure SQL Managed Instance. However, in the process, they lose the opportunity to use the PaaS capabilities, manageability and AI/Fabric capabilities in Azure by making this choice. Enterprise Architects, Application Architects, Database developers and DB Administrators have to make the right choice taking both development as well as operational costs and compatibility when they make their SQL modernization decisions. Here are best practices some of the biggest and successful SQL migrations have used to make the migration and modernization journey with confidence. While we cannot disclose specific customer names, these guidelines are based on helping many large to small Enterprise customers. Azure SQL Managed Instance as the Resiliency Anchor Azure SQL Managed Instance is often the platform that helps organizations overcome all three concerns simultaneously because it combines near-full SQL Server compatibility with platform-as-a-service benefits. Azure SQL Managed Instance (Azure SQL MI) Next-gen General Purpose is now generally available, bringing a built-in performance and scale upgrade for General Purpose workloads, including up to 500 databases per instance, up to 32 TB storage, lower latency, and higher IOPS. The release also adds more flexible cost-performance tuning with independent vCore, IOPS, and memory scaling, plus faster management operations to adapt to changing workload demand. For enterprise SQL Server modernization, this positions Azure SQL MI as a stronger path for high-compatibility migrations that need better price-performance without moving to a full replatform. Let us dive deeper into how this helps address the downtime risk concerns by enables three levels of resiliency and high availability features. Local Redundancy Azure SQL Managed Instance provides first layer of Local Redundancy — built into every Azure SQL MI instance at no extra cost. Azure SQL Managed Instance uses local redundancy by default to keep workloads available during node, VM, rack, maintenance, and other local failures within a single datacenter, with Service Fabric orchestrating failover. In General Purpose (including Next-gen GP), this is implemented as stateless compute plus remote stateful storage; during failover, the engine process moves to another compute node and reattaches data, which can cause temporary performance impact due to cold cache. In Business Critical, local redundancy uses multiple synchronized replicas with local SSD storage (Always On-like architecture), enabling fast failover and read scale-out on secondaries.Next-gen General Purpose is an architectural upgrade to the existing General Purpose service tier that uses an upgraded remote storage layer that stores instance data and log files on Elastic SAN instead of page blobs and maintains it locally. Local redundancy protects against local infrastructure issues. This gives you a 99.99% SLA but not full datacenter/zone disasters, so zone redundancy (where supported) or disaster recovery (DR) options like failover groups/geo-restore are needed for broader resilience. Zone Redundancy The second layer is Zone Redundancy, which is accomplished placing data replicas across availability zones. Your Azure SQL MI resources are distributed across multiple availability zones within a region. This protects against the failure of an entire datacenter because each Azure availability zone is a separate physical location with independent power, cooling and networking. It relies on synchronous replication using zone-redundant storage for General Purpose. For Business critical, it uses Always On Availability group replicas across zones for Business Critical. Always On availability group technology replicates data changes from the primary instance to standby replicas in other availability zones. In the event of an outage, there's an automatic failover that seamlessly transitions one of the standby replicas to be prima. These replicas are always in sync — which means zero data loss. Failover typically happens in under 30 seconds, and your SLA jumps to 99.995%. Failover Groups The third layer is Failover Groups. This is your cross-region disaster recovery solution. It asynchronously replicates all user databases to a secondary Azure SQL MI instance in a different Azure region. Because it is asynchronous replication, there is potential for momentary data loss in the case of a datacenter outage. But it still protects the data against the worst case failure — a full regional outage. If the replica is a standby replica, there is no license required and it is used only for disaster recovery. Using these options, business stakeholders can get their assurance that they have Enterprise grade availability and resiliency platform of AzureSQL for running their mission critical workloads. You can read more about these HA and Resiliency options in Microsoft Learn. Cost Governance for Enterprise Buyers The total cost of data modernization and migration is not a one-time estimate but an ongoing one. In this case, Azure SQL MI provides Enterprise DB Administrators many levers through pricing model choice, right-sizing, elasticity, serverless options and dev/test free tiers. Let us explore how these can be combined for smart cost estimations. Lets also look at the best offering for the cost-conscious Enterprises - Azure SQL DB Hyperscale. With Azure SQL DB Hyperscale, you get the SQL Server engine, T-SQL compatibility, High Availability, Disaster recovery, security, backups, and management all bundled into the service price. No separate cost for SQL Server license. Hyperscale separates compute and storage that can scale independently and does not force you to overprovision. You have to only pay what you use which is ideal for seasonal workloads, Dev/Test, SaaS applications, predictable daytime trends, and up to 60% savings when you use Elastic pools. Azure Hybrid benefit (AHB)- Azure Hybrid Benefit lets you bring your existing SQL Server investments to Azure and reduce compute costs, accelerating your ROI from cloud migration while preserving all the benefits of Azure SQL Azure SQL DB Free offer – is the strongest product offering. Enterprises can use all features of Azure SQL at no cost for up to 10 Azure SQL DB free-tier. 100,000 vCore-seconds of serverless compute per month, 32GB data storage, 32 GB backup storage, serverless auto-scaling and auto-pause if you hit the limit per month. Run your POCs at no cost and evaluate before you move to Azure SQLDB, especially SMB& some enterprise Azure SQL Managed Instance also offers 1 free Azure SQL MI instance per Azure subscription giving you 720vCore hours per month, 64GB storage, up to 500 databases, automated backups and 12 months free. And if data migration is not possible due to data compliance or data proximity purposes, Azure Arc Pay-As-You-Go (PAYG) gives you cloud-style SQL licensing for servers running anywhere—on-premises, at the edge, or in other clouds. Instead of making large up-front licensing investments, you only pay for SQL Server while it's running, while still gaining access to Azure Arc management, security, monitoring, and modernization capabilities. For seasonal, variable, or growth-oriented workloads, PAYG can improve cash flow and reduce licensing complexity. Reserved instances allow Enterprise customers to commit to using Azure SQL resource for a period of one or three years to receive a significant discount. This option combined with AHB can save you even more up to 80%. We have a comprehensive licensing guide for on-premises SQL Server for your reference. Azure SQL enables a variety of cloud cost-models for a wide range of enterprise workload needs to help Enterprise cloud cost decision makers and DB Administrators make the right choice for their workloads. Target selection guidance While Azure SQL has multiple deployment options to migrate your on-premises work loads, it is critical to make the right choice long term. Customers can install SQL Server on-premises, they can use Azure SQL deployment options, and also run SQL Server in other clouds like Amazon Web Services and Google Cloud. If there is an Enterprise workload that is not ready to modernize, you have the ability to lift and shift into SQL Server in Azure VM. It is a low cost migration option, because the application does not need any modification and it gives DB Administrators full control over the SQL server and underlying Windows or Linux OS. This can be a first step to modernization for some customers who are risk-averse. For those Enterprise customers who are willing to modernize their workloads and SQL Server instances, Azure SQL DB Hyperscale is the best option. Azure SQL Database Hyperscale helps organizations modernize their most demanding database workloads with virtually unlimited growth, high performance, and cloud-scale economics. Customers can scale storage and compute independently, support large multi-terabyte databases, accelerate application performance with read-scale replicas, and eliminate the operational complexity of managing infrastructure, backups, patching, and high availability. They can build cloud-native applications or cloud-enable existing applications. However, if Enterprise customers want good compatibility with their on-premises SQL Server but continue down the modernization path - their best option is Azure SQL Managed Instance. They can modernize the instance and not impact the application as there is no application change required. Applications will continue to work and the DB Administrators do not need to worry about managing infrastructure and all the overhead that comes with managing, self-managing your SQL Server virtual machines. For SQL Server customers, PostgreSQL may look like an attractive low cost option. However, it requires re-platforming that could add significant hidden cost due to retraining all their DBAs and their developers to do performance optimization, performance best practices and operational maintenance. Lastly, our same SQL engine is also available to customers as a SaaS-ified version, Fabric SQL database as well. All these options use the exact same SQL engine which makes it easier for Database developers and DB Administrators continue to use the same expertise, tools and process. Making the right choice of Azure SQL deployment is not just on the fastest way to modernize but the right long term approach. Conclusion and Next steps Enterprise SQL Server migrations rarely stall because of migration technology. More often, they are delayed by concerns around downtime, cost predictability, and platform selection. Organizations that address these questions early can accelerate modernization while reducing operational risk. Azure SQL provides multiple modernization paths—from SQL Server on Azure Virtual Machines to Azure SQL Managed Instance and Azure SQL Database—allowing organizations to balance compatibility, operational simplicity, resiliency, and cost efficiency based on their business requirements. As modernization initiatives accelerate, the most successful projects are those that treat migration not as a one-time infrastructure event, but as a long-term platform strategy. Whether its the newest and the fastest way for us to migrate customers, we have all the comprehensive Copilot enabled AI-assisted migration tooling, technical training and support you need. Look for more blogs, whitepapers, guides and training based on best practices used real-world data modernization projects.Generally Available: Microsoft Entra Server Principals and Server Roles for Azure SQL Database
The problem we're solving Previously, Microsoft Entra identities in Azure SQL Database could only be created as contained database users - principals scoped to a single database with no server-level presence. That meant: No granular server-level delegation. You couldn't assign a server role such as ##MS_ServerStateReader## (to query DMVs across databases) or ##MS_LoginManager## (to manage logins) to an Entra principal. Only the Entra admin or a SQL login could perform these server-scoped tasks. Per-database provisioning overhead. Each Entra principal had to be created separately as a contained database user in every database that required access, with no way to inherit server-scoped permissions. No centralized “disable” switch. Offboarding meant tracking down a contained database user in every database - there was no server-level login to disable. These gaps forced many teams to keep SQL authentication for administrative tasks, even when they wanted to go password-less with Entra. What changes with GA Microsoft Entra logins become first-class server principals in the logical master database, just like SQL logins. This capability has been in public preview on Azure SQL Database (and is already generally available on Azure SQL Managed Instance and SQL Server 2022+); with this release it reaches general availability on Azure SQL Database, unlocking three things for production use: 1. Server role assignment for Entra identities Azure SQL Database's seven fixed server-level roles can be assigned to Entra server principals(logins). These roles cover database connectivity, database management, definition and security-definition reads, login management, and server-state read/manage. This means you can give your monitoring service principal read-only DMV access across all databases (##MS_ServerStateReader##), delegate login management to a security team member (##MS_LoginManager##), or let a DevOps app create databases (##MS_DatabaseManager##). All without SQL auth, all with Entra identities. 2. Server-wide login model Instead of provisioning contained users independently in every database, you can create database users mapped to a server login (CREATE USER ... FROM LOGIN). These users inherit server-scoped permissions automatically. One login, many databases — managed from a single place. For the T-SQL syntax, see Create and utilize Microsoft Entra server logins. 3. Centralized logins enable/disable ALTER LOGIN [user@contoso.com] DISABLE - one command blocks that identity from connecting to every database on the server. No more hunting down per-database users during an offboarding or incident response. When you re-enable the login, access is restored everywhere. Note: ALTER LOGIN ... DISABLE applies only to login-based users, not contained database users. It blocks new connections only; existing sessions remain active until terminated with KILL if needed. For immediate effect, see cache propagation. Microsoft Entra group logins are not supported; see the server principals documentation for alternatives. What does this unlock for your organization Ability to go password-less. With server principals and roles now generally available, organizations can adopt Entra-only authentication without a remaining server-level functionality gap. Entra logins bring parity with SQL logins closer, making it practical to disable SQL authentication entirely and using Entra as the sole authentication path. Least-privilege administration. Server-level roles simplify permission management by enabling customers to delegate common management and monitoring responsibilities without requiring admin privileges, enabling adherence to least privilege and separation of duties at scale, while making administration across databases on the same logical server much easier. Server roles let you scope access precisely, previously, the only server-wide option for an Entra identity was the all-powerful Entra admin. Give your security auditors ##MS_SecurityDefinitionReader## role instead of 'db_owner'. Give your monitoring tools ##MS_ServerStateReader## instead of an over-privileged administrator role. Zero-touch DevOps. A service principal with ##MS_DatabaseManager## and ##MS_LoginManager## can automate database and user provisioning end-to-end. After the initial Entra admin bootstrap, no human needs to be in the loop for routine operations. Faster incident response. When a principal is compromised, disable the login at the server level. New connections are blocked across all databases immediately - without needing to know which databases the user had access to. To cut off active sessions immediately, flush the authentication caches and KILL existing sessions. Geo-replica support. Entra logins created on the primary server are automatically available on geo-replicas, with read-only access to replicated databases. Key things to know Bootstrap requirements. The Microsoft Entra admin must create the first Entra login. After that, any Entra principal with ALTER ANY LOGIN or ##MS_LoginManager## membership can create additional logins. Entra admin takes precedence. If a principal is both the Entra admin and has a login, the admin permissions win. The login permissions have no additional effect. Cache propagation. Role membership and permission changes take effect on the next connection. For immediate effect, clear the auth cache with DBCC FLUSHAUTHCACHE and DBCC FREESYSTEMCACHE('TokenAndPermUserStore'). EXECUTE AS LOGIN is not supported for Entra logins on Azure SQL Database (it is supported on Managed Instance). Get started Configure a Microsoft Entra admin on your logical server Create your first Entra login and assign server roles (step-by-step tutorial) Understand the server roles and their permissions Consider enabling Entra-only authentication to eliminate SQL auth entirely Ready to migrate from SQL Authentication? If you're looking to move your existing SQL logins to Entra, check out Securing Azure SQL Database with Microsoft Entra password-less authentication - migration guide. It walks through the end-to-end journey from SQL auth to Entra, including how to identify SQL login dependencies, convert them to Entra principals, and enable Entra-only mode. Learn more Microsoft Entra server principals (logins) - full reference: syntax, permissions, limitations. Azure SQL Database server roles - role descriptions, permission matrix, examples. Microsoft Entra authentication overview - how Entra auth works with Azure SQL. Manage logins and users - login lifecycle management.Transparent data encryption in Azure SQL Database now supports AES keys (Public Preview)
For teams thinking about long-term cryptographic resilience, this preview is especially relevant. TDE with customer-managed keys has traditionally used asymmetric RSA-based key protectors, while broader industry guidance is increasingly focused on preparing for a post-quantum cryptographic (PQC) future and adopting cryptographic approaches that are better aligned with that transition. This update aligns with broader security guidance, including the NSA’s CNSA 2.0 recommendations, which emphasize modern cryptographic planning for a quantum-resistant future. For organizations building crypto agility into their platforms, AES support is a practical step in that direction. Why it matters Preparing for a post‑quantum world With current technology, breaking asymmetric algorithms such as Elliptic Curve and RSA-2048 using the best-known classical methods would take billions of years. Even with large-scale distributed computing, it is still considered computationally infeasible. Asymmetric algorithms are vulnerable to Shor’s algorithm, which means a sufficiently powerful quantum computer could break RSA-2048 much faster. That said, this would require millions of stable qubits, and current quantum systems are still far from that point. AES, as a symmetric algorithm, is not affected by Shor’s algorithm and remains more resistant to known quantum attacks, including Grover’s algorithm, when used with larger key sizes such as AES-256. The figure below highlights the difference in the estimated effort required to break RSA-2048 and AES-256. For context, the green dashed line represents the age of the universe, about 13.8 billion years. Aligning with modern security guidance Security guidance is moving toward stronger crypto agility and long-term resilience. By supporting AES keys for TDE protectors, Azure SQL Database gives customers a way to align data-at-rest protection with evolving security and compliance expectations. For a broader overview of quantum computing and cryptography, see Microsoft’s post-quantum cryptography overview. How it works (high level) At a high level, nothing changes about the purpose of TDE: it still protects data at rest by encrypting the Database Encryption Key (DEK) with a TDE protector. What changes in this preview is the type of key you can use to protect, or wrap, the AES DEK. The AES DEK encrypts database data files and log files. The TDE protector encrypts the DEK. With TDE with customer‑managed keys, the TDE protector is stored in Azure Key Vault or Azure Key Vault Managed HSM. With this preview, the TDE protector can now be a symmetric AES key instead of an RSA key. For background on customer-managed TDE, see the Customer-managed transparent data encryption (TDE) Get started If you want to try the preview, make sure the following prerequisites are in place: An Azure SQL Database logical server or database with customer-managed TDE enabled. An Azure Key Vault Managed HSM with support for AES keys. Soft-delete and purge protection enabled on the key store. The required permissions for Azure SQL Database to access the key. You can review the full prerequisites in Microsoft Learn under requirements to configure customer-managed TDE. The setup flow for AES keys is essentially the same as for RSA-based TDE protectors. The main difference is the type of key you create and register. Create an AES key (for example, AES‑256) in Azure Key Vault Managed HSM. Add the key to your Azure SQL logical server. Set the AES key as the TDE protector. Verify that encryption is enabled using system views. For step-by-step configuration guidance, see Microsoft Learn on Create Azure SQL Database Logical Server Configured with User-Assigned Managed Identity and Customer-Managed TDE. Example configuration (PowerShell) The following example shows the basic PowerShell flow: create an AES key, register it with the logical server, and then set it as the TDE protector. # Variables $hsmName = "MyHSM" $keyName = "TDE-AES-Key" $sqlServerName = "my-sql-server" $sqlResourceGroup = "my-sql-rg" # Create an AES-256 HSM-backed key in MHSM Add-AzKeyVaultKey ` -HsmName $hsmName ` -Name $keyName ` -KeyType oct-HSM ` -Size 256 # Get key URI $key = Get-AzKeyVaultKey -HsmName $hsmName -Name $keyName # Register the key with the SQL server Add-AzSqlServerKeyVaultKey ` -ResourceGroupName $sqlResourceGroup ` -ServerName $sqlServerName ` -KeyId $key.Id # Set the key as the TDE protector Set-AzSqlServerTransparentDataEncryptionProtector ` -ResourceGroupName $sqlResourceGroup ` -ServerName $sqlServerName ` -Type AzureKeyVault ` -KeyId $key.Id After you enable TDE with AES keys, you can verify the database encryption status by running the following query: SELECT DB_NAME(database_id) AS DatabaseName, encryption_state_desc, encryptor_type FROM sys.dm_database_encryption_keys WHERE database_id <> 2; If the database is encrypted, the view returns an ENCRYPTED state, or ENCRYPTION_IN_PROGRESS while encryption is still underway, with SYMMETRIC_KEY shown as the encryptor type. Public preview notice Transparent data encryption in Azure SQL Database with AES keys support is currently in Public Preview. Preview features are provided for evaluation purposes and are subject to the Azure Preview Supplemental Terms . Availability is rolling out gradually across Azure regions. You may see this capability appear over time depending on your region and service deployment status. Azure SQL Database is the first SQL offering to receive this feature, with additional SQL platforms planned in the future. Learn more Microsoft Learn: customer-managed transparent data encryption for Azure SQL Database Microsoft Learn: configure customer-managed TDE for Azure SQL Database Microsoft Research: post-quantum cryptography overview Conclusion AES key support for customer-managed TDE gives Azure SQL Database customers a practical way to strengthen their encryption strategy while preparing for long-term cryptographic change, including post quantum cryptography. Because the setup experience remains familiar, teams can evaluate this preview without rethinking how TDE works operationally. We want your feedback If you’re exploring this preview, now is a good time to test it in your environment and share feedback with the product group before general availability.Automatic Connectivity Tests for Azure SQL Managed Instance
To further enhance connectivity monitoring and improve service reliability, we’re introducing automatic internal connectivity tests for all Azure SQL Managed Instances. These tests are fully automated and require no action from you. Beginning May 2026, the tests will be continuously performed at regular intervals on all managed instances. By proactively monitoring internal network connections, we’re able to quickly identify potential issues and maintain stable end-to-end connectivity. These tests are performed from a pair of internal IP addresses from the subnet range that hosts the managed instance, so they do not require any external inbound or outbound connectivity. Please note that additional IP addresses will be reserved for these tests and that tests may leave traces in your observability logs. Automatic tests diagnose issues in internal service and network availability. This results in accelerated issue discovery and shorter time to mitigate incidents that involve degraded connectivity of managed instances’ internal networking components. This suite of connectivity tests examines internal network connections at several levels, boosting the supportability and visibility into the service’s internal state and offering you peace of mind regarding your managed instances. Do note that your audit and security systems, if configured to track certain types of events emitted by SQL Server, may record failed login attempts. Those are normal and expected byproducts of the end-to-end connectivity test suite. If you would prefer to not have those events register in your SQL Server audit logs, SQL error logs, or captured Extended Events, we provide you with their event signatures so you can set up event filters or configure your SIEM system to ignore them: Observing failed logins caused by end-to-end tests. You can read more about the automated connectivity tests at Automatic internal connectivity tests for Azure SQL Managed Instance.
Dynamic Data Masking – What it is, What it isn’t, and How to use it effectively
In this post, we’ll explain the core purpose of Dynamic Data Masking (to ease application development), how it works, and its proper use cases – as well as its limitations. If you’re considering using Dynamic Data Masking or reviewing your data security strategy, this information will help you make informed decisions. What Dynamic Data Masking is designed for Dynamic Data Masking Dynamic Data Masking - SQL Server | Microsoft Learn is a database feature that can be used to alter how certain data elements are presented in query results for users who do not have privileged access or required permission. For example, a query on an email column may return a masked value such as jXXX@XXXX.com rather than the full address, depending on user permissions, while the original data remains unchanged in storage. Masking rules are defined within the database schema and are applied to query results for applicable users at runtime. This approach can simplify application developer’s job and reduce the need for application‑level logic that modifies how sensitive values are displayed across different application(s) or reports. DDM can help prevent accidental or casual exposure of sensitive information. How Does DDM differ from other security features? Dynamic Data Masking affects only what users see in query results—it does not protect the underlying data. Unlike encryption Always Encrypted - SQL Server | Microsoft Learn or Row‑Level security Row-Level Security - SQL Server | Microsoft Learn, DDM does not encrypt data, filter rows, or override SQL permissions. Users with elevated privileges (such as UNMASK, db_owner, or sysadmin) always see unmasked data or can modify or remove masking rules. What DDM doesn’t protect against Because Dynamic Data Masking is applied when query results are returned, there are several considerations to be aware of: Inference through queries: In some scenarios, users with database access may be able to make inferences about masked values by applying query filters or conditions that rely on underlying stored data. The database is still comparing the real values under the hood, so these queries work. It’s an expected behavior given DDM’s design. Privileged users: Users who are granted sufficient database permissions, such as the ability to alter table schemas, can directly disable or remove masking. Users with sysadmin, db_owner or CONTROL permission can view unmasked data. Thus, controlling and auditing who holds such privileges is vital. Metadata visibility: Masking rules and associated columns can be discoverable through system metadata. Data movement: Because masking is defined at the schema level in a given database instance, backups or exported datasets may contain unmasked values depending on permissions and configuration. Understanding these design characteristics is important when incorporating DDM into a broader data governance or privacy strategy. Proper use and best practices for DDM Organizations may consider using Dynamic Data Masking in scenarios where consistent display of sensitive values is needed across application(s) or reporting environments. Some implementation considerations include: Using DDM to help standardize how sensitive fields are displayed in query results and reduce developmental effort for data masking Combining DDM with other database or access‑control features as part of a layered data protection strategy Reviewing which users are granted permissions to view unmask data or alter masking configurations. Implementing auditing or monitoring database activity as part of broader governance practices Educating internal stakeholders on how masking operates at the query‑result level Testing masking configurations in non‑production environments prior to deployment Conclusion Dynamic Data Masking can be useful in scenarios where organizations want to manage how sensitive data is displayed in application outputs without modifying stored values. It is designed to operate as part of a broader data access or governance approach rather than as a standalone protection mechanism for stored data. When implemented alongside complementary database features and appropriate access controls, DDM may help support more consistent handling of sensitive values across environments.Announcing Public Preview: Auditing for Fabric SQL Database
We’re excited to announce the public preview of Auditing for Fabric SQL Database—a powerful feature designed to help organizations strengthen security, ensure compliance, and gain deep operational insights into their data environments. Why Auditing Matters Auditing is a cornerstone of data governance. With Fabric SQL Database auditing, you can now easily track and log database activities—answering critical questions like who accessed what data, when, and how. This supports compliance requirements (such as HIPAA and SOX), enables robust threat detection, and provides a foundation for forensic investigations. Key Highlights Flexible Configuration: Choose from default “audit everything,” preconfigured scenarios (like permission changes, login attempts, data reads/writes, schema changes), or define custom action groups and predicate filters for advanced needs. Seamless Access: Audit logs are stored in One Lake, making them easily accessible via T-SQL or One Lake Explorer. Role-Based Access Control: Configuration and log access are governed by both Fabric workspace roles and SQL-level permissions, ensuring only authorized users can view or manage audit data. Retention Settings: Customize how long audit logs are retained to meet your organization’s policy. How It Works Audit logs are written to a secure, read-only folder in One Lake and can be queried using the sys. fn_get_audit_file_v2 T-SQL function. Workspace and artifact IDs are used as identifiers, ensuring logs remain consistent even if databases move across logical servers. Access controls at both the workspace and SQL database level ensure only the right people can configure or view audit logs. Example Use Cases Compliance Monitoring: Validate a full audit trail for regulatory requirements. Security Investigations: Track specific events like permission changes or failed login attempts. Operational Insights: Focus on specific operations (e.g., DML only) or test retention policies. Role-Based Access: Verify audit visibility across different user roles. Getting Started You can configure auditing directly from the Manage SQL Auditing blade in the Fabric Portal. Choose your preferred scenario, set retention, and (optionally) define custom filters—all through a simple, intuitive interface. Learn more about auditing for Fabric SQL database here Data exposed session with demo hereZero Trust for data: Make Microsoft Entra authentication for SQL your policy baseline
A policy-driven path from enabled to enforced. Why this matters now Security and compliance programs were once built on an assumption that internal networks were inherently safer. Cloud adoption, remote work, and supply-chain compromise have steadily invalidated that model. U.S. federal guidance has now formalized this shift: Executive Order 14028 calls for modernizing cybersecurity and accelerating Zero Trust adoption, and OMB Memorandum M-22-09 sets a federal Zero Trust strategy with specific objectives and timelines. Meanwhile, attacker economics are changing. Automation and AI make reconnaissance, phishing, and credential abuse cheaper and faster. That concentrates risk on identity—the control plane that sits in front of systems, applications, and data. In Zero Trust, the question is no longer “is the network trusted,” but “is this request verified, governed by policy, and least-privilege?” Why database authentication is a first‑order Zero Trust control Databases are universally treated as crown-jewel infrastructure. Yet many data estates still rely on legacy patterns: password-based SQL authentication, long-lived secrets embedded in apps, and shared administrative accounts that persist because migration feels risky. This is exactly the kind of implicit trust Zero Trust architectures aim to remove. NIST SP 800-207 defines Zero Trust as eliminating implicit trust based solely on network location or ownership and focusing controls on protecting resources. In that model, every new database connection is not “plumbing”—it is an access decision to sensitive data. If the authentication mechanism sits outside the enterprise identity plane, governance becomes fragmented and policy enforcement becomes inconsistent. What changes when SQL uses Microsoft Entra authentication Microsoft Entra authentication enables users and applications to connect to SQL using enterprise identities, instead of usernames and passwords. Across Azure SQL and SQL Server enabled by Azure Arc, Entra-based authentication helps align database access with the same identity controls organizations use elsewhere. The security and compliance outcomes that leaders care about Reduce password and secret risk: move away from static passwords and embedded credentials. Centralize governance: bring database access under the same identity policies, access reviews, and lifecycle controls used across the enterprise. Improve auditability: tie access to enterprise identities and create a consistent control surface for reporting. Enable policy enforcement at scale: move from “configured” controls to “enforced” controls through governance and tooling. This is why Entra authentication is a high-ROI modernization step: it collapses multiple security and operational objectives into one effort (identity modernization) rather than a set of ongoing compensating programs (password rotation programs, bespoke exceptions, and perpetual secret hygiene projects). Why AI makes this a high priority decision AI accelerates both reconnaissance and credential abuse, which concentrates risk on identity. As a result, policy makers increasingly treat phishing-resistant authentication and centralized identity enforcement as foundational—not optional. A practical path: from enabled to enforced Successful security programs define a clear end state, a measurable glide path, and an enforcement model. A pragmatic approach to modernizing SQL access typically includes: Discover active usage: Identify which logins and users are actively connecting and which are no longer required. Establish Entra as the identity authority: Enable Entra authentication on SQL logical servers, starting in mixed mode to reduce disruption. Recreate principals using Entra identities: Replace SQL Authentication logins/users with Entra users, groups, service principals, and managed identities. Modernize application connectivity: Update drivers and connection patterns to use Entra-based authentication and managed identities. Validate, then enforce: Confirm the absence of password‑based SQL authentication traffic, then move to Entra‑only where available and enforce via policy. By adopting this sequencing, organizations can mitigate risks at an early stage and postpone enforcement until the validation process concludes. For a comprehensive migration strategy, refer to Securing Azure SQL Database with Microsoft Entra Password-less Authentication: Migration Guide. Choosing which projects to fund — and which ones to stop When making investment decisions, priority is given to database identity projects that can demonstrate clear risk reduction and lasting security benefits. Microsoft Entra authentication as the default for new SQL workloads, with a defined migration path for the existing workloads. Managed identities for application-to-database connectivity to eliminate stored secrets. Centralized governance for privileged database access using enterprise identity controls. At the same time, organizations should explicitly de-prioritize investments that perpetuate password risk: password rotation projects that preserve SQL Authentication, bespoke scripts maintaining shared logins, and exception processes that do not scale. Security and scale are not competing goals Security is often seen as something that slows down innovation, but database identity offers unique benefits. When enterprise identity is used for access controls, bringing in new applications and users shifts from handing out credentials to overseeing policies. Compliance reporting also becomes uniform rather than customized, making it easier to grow consistently thanks to a single control framework. Modern database authentication is not solely about mitigating risk— it establishes a scalable operational framework for secure data access. A scorecard designed for leadership readiness To elevate the conversation from implementation to governance, use outcome-based metrics: Coverage: Percentage of SQL workloads with Entra authentication enabled. Enforcement: Percentage operating in Entra-only mode after validation. Secret reduction: Applications still relying on stored database passwords. Privilege hygiene: Admin access governed through enterprise identity controls. Audit evidence: Ability to produce identity-backed access reports on demand. These map directly to Zero Trust maturity expectations and provide a defensible definition of “done.” Closing Zero Trust is an operating posture, not a single control. For most organizations, the fastest way to make that posture measurable is to standardize database access on the same identity plane used everywhere else. If you are looking for a single investment that improves security, reduces audit friction, and supports responsible AI adoption, modernizing SQL access with Microsoft Entra authentication — and driving it from enabled to enforced — is one of the most durable choices you can make. References US Government sets forth Zero Trust architecture strategy and requirements (Microsoft Security Blog) Securing Azure SQL Database with Microsoft Entra Password-less Authentication: Migration Guide (Microsoft Tech Community) OMB Memorandum M-22-09: Federal Zero Trust Strategy (White House) NIST SP 800-207: Zero Trust Architecture CISA: Zero Trust Enforce Microsoft Entra-only authentication for Azure SQL Database and Azure SQL Managed InstanceVersionless keys for Transparent Data Encryption in Azure SQL Database (Generally Available)
With this release, you no longer need to reference a specific key version stored in Azure Key Vault or Managed HSM when configuring Transparent Data Encryption (TDE) with customer‑managed keys. Instead, Azure SQL Database now supports a versionless key URI, automatically using the latest enabled version of your key. This means: Simpler key management—no longer necessary to specify the key version. Reduced operational overhead by eliminating risks tied to outdated key versions. Full control remains with the customer. This enhancement streamlines encryption at rest, especially for organizations operating at scale or enforcing strict security and compliance standards. Versionless keys for TDE are available today across Azure SQL Database with no additional cost. Versioned vs. Versionless Key URIs To highlight the difference, here are real examples: Versioned Key URI (old approach — explicit version required) https://demotdeakv.vault.azure.net/keys/TDECMK/40acafb8a7034b20ba227905df090a1f Versionless Key URI (new approach) https://demotdeakv.vault.azure.net/keys/TDECMK A versionless key URI references only the key name. Azure SQL Database automatically uses the newest enabled version of the key. Learn more Transparent Data Encryption - Azure SQL Database Azure SQL transparent data encryption with customer-managed key Transparent data encryption with customer-managed keys at the database levelWhy Developers and DBAs love SQL’s Dynamic Data Masking (Series-Part 1)
Dynamic Data Masking (DDM) is one of those SQL features (available in SQL Server, Azure SQL DB, Azure SQL MI, SQL Database in Microsoft Fabric) that both developers and DBAs can rally behind. Why? Because it delivers a simple, built-in way to protect sensitive data—like phone numbers, emails, or IDs—without rewriting application logic or duplicating security rules across layers. With just a single line of T-SQL, you can configure masking directly at the column level, ensuring that non-privileged users see only obfuscated values while privileged users retain full access. This not only streamlines development but also supports compliance with data privacy regulations like GDPR and HIPAA, etc. by minimizing exposure to personally identifiable information (PII). In this first post of our DDM series, we’ll walk through a real-world scenario using the default masking function to show how easy it is to implement and how much development effort it can save. Scenario: Hiding customer phone numbers from support queries Imagine you have a support application where agents can look up customer profiles. They need to know if a phone number exists for the customer but shouldn’t see the actual digits for privacy. In a traditional approach, a developer might implement custom logic in the app (or a SQL view) to replace phone numbers with placeholders like “XXXX” for non-privileged users. This adds complexity and duplicate logic across the app. With DDM’s default masking, the database can handle this automatically. By applying a mask to the phone number column, any query by a non-privileged user will return a generic masked value (e.g. “XXXX”) instead of the real number. The support agent gets the information they need (that a number is on file) without revealing the actual phone number, and the developer writes zero masking code in the app. This not only simplifies the application codebase but also ensures consistent data protection across all query access paths. As Microsoft’s documentation puts it, DDM lets you control how much sensitive data to reveal “with minimal effect on the application layer” – exactly what our scenario achieves. Using the ‘Default’ Mask in T-SQL : The ‘Default’ masking function is the simplest mask: it fully replaces the actual value with a fixed default based on data type. For text data, that default is XXXX. Let’s apply this to our phone number example. The following T-SQL snippet works in Azure SQL Database, Azure SQL MI and SQL Server: SQL -- Step 1: Create the table with a default mask on the Phone column CREATE TABLE SupportCustomers ( CustomerID INT PRIMARY KEY, Name NVARCHAR(100), Phone NVARCHAR(15) MASKED WITH (FUNCTION = 'default()') -- Apply default masking ); GO -- Step 2: Insert sample data INSERT INTO SupportCustomers (CustomerID, Name, Phone) VALUES (1, 'Alice Johnson', '222-555-1234'); GO -- Step 3: Create a non-privileged user (no login for simplicity) CREATE USER SupportAgent WITHOUT LOGIN; GO -- Step 4: Grant SELECT permission on the table to the user GRANT SELECT ON SupportCustomers TO SupportAgent; GO -- Step 5: Execute a SELECT as the non-privileged user EXECUTE AS USER = 'SupportAgent'; SELECT Name, Phone FROM SupportCustomers WHERE CustomerID = 1 Alternatively, you can use Azure Portal to configure masking as shown in the following screenshot: Expected result: The query above would return Alice’s name and a masked phone number. Instead of seeing 222-555-1234, the Phone column would show XXXX. Alice’s actual number remains safely stored in the database, but it’s dynamically obscured for the support agent’s query. Meanwhile, privileged users such as administrator or db_owner which has CONTROL permission on the database or user with proper UNMASK permission would see the real phone number when running the same query. How this helps Developers : By pushing the masking logic down to the database, developers and DBAs avoid writing repetitive masking code in every app or report that touches this data. In our scenario, without DDM you might implement a check in the application like: If user_role == “Support”, then show “XXXX” for phone number, else show full phone. With DDM, such conditional code isn’t needed – the database takes care of it. This means: Less application code to write and maintain for masking Consistent masking everywhere (whether data is accessed via app, report, or ad-hoc query). Quick changes to masking rules in one place if requirements change, without hunting through application code. From a security standpoint, DDM reduces the risk of accidental data exposure and helps in compliance scenarios where personal data must be protected in lower environments or by certain roles, while reducing the developer effort drastically. In the next posts of this series, we’ll explore other masking functions (like Email, Partial, and Random etc) with different scenarios. By the end, you’ll see how each built-in mask can be applied to make data security and compliance more developer-friendly! Reference Links : Dynamic Data Masking - SQL Server | Microsoft Learn Dynamic Data Masking - Azure SQL Database & Azure SQL Managed Instance & Azure Synapse Analytics | Microsoft Learn
