DCasv6 and ECasv6 confidential VMs in Azure Government Cloud
Today, we are announcing the launch of the DCasv6 and ECasv6 series of confidential virtual machines (CVMs) in Azure Government. Azure Government: Compliant, Hyperscale, Sovereign Cloud Azure Government was designed to remove the constraints that have historically limited federal cloud adoption by delivering hyperscale innovation without sacrificing regulatory certainty. Supporting over 180 services, Azure Government allows customers to consume advanced cloud capabilities without having to individually validate service availability or compliance. It is a complete end-to-end platform, delivering identity, DevOps, and services as commercial Azure, while operating entirely within accredited boundaries. Confidential virtual machines address one of the barriers to multi-tenant cloud adoption: When deployed on Azure Government, Confidential VMs combine physical isolation, sovereign operations, and hardware-enforced cryptographic isolation into a single execution environment. This enables customers to get additional protections from insider threats. At its core, Azure Government runs the same Azure codebase that powers Microsoft’s commercial cloud, providing access to compute, networking, storage, data, and AI services. DCasv6 and ECasv6: Confidential virtual machines in Azure government cloud The DCasv6 and ECasv6-series virtual machines built on 4th Generation AMD EPYC™ processors are the first in Azure Government to implement AMD SEV-SNP. This generation introduces several controls that change both security posture and operational readiness: Hardware-Enforced Memory Isolation: AMD SEV-SNP provides full, AES-128 encrypted memory with keys generated and managed by the onboard AMD Secure Processor. Online key rotation: Support for the online key rotation with the introduction of Virtual Machine Metablob disk (VMMD). Programmatic Attestation for Audit and Zero-Trust: Before provisioning any workload, customers can perform an attestation. This cryptographic procedure validates the integrity of the hardware and software, producing a signed report that proves the VM is a genuine confidential instance. Confidential OS Disk Encryption with Flexible Key Management: Cryptographic protection extends beyond runtime memory to the operating system disk itself. The disk's encryption keys are bound to the VM's virtual Trusted Platform Module (vTPM), which is protected within the TEE. Customers can choose between platform-managed keys (PMK) for simplicity and regulatory ease, or customer-managed keys (CKM) for full, sovereign control over the key lifecycle - a common requirement for the most stringent compliance regimes. Conclusion With the DCasv6 and ECasv6-series virtual machines now generally available in Azure government regions, customers can modernize their infrastructure deployments through confidential computing which replaces implicit trust with cryptographic isolation, and when deployed on Azure Government’s sovereign cloud within physically isolated data centers, it enables agencies to modernize at operational speed without compromising control. Azure Government is in a unique position to deliver the full operational depth of a hyperscale cloud, from identity and DevOps to monitoring and edge execution, inside an environment purpose-built for federal compliance. When combined with the latest Confidential VMs, customers gain secure infrastructure built on a platform where agility, visibility, and trust reinforce each other. Additional resources Azure Government documentation | Microsoft Learn Government Validation System
Public Preview: Azure Monitor pipeline transformations
Overview The Azure Monitor pipeline extends the data collection capabilities of Azure Monitor to edge and multi-cloud environments. It enables at-scale data collection (data collection over 100k EPS), and routing of telemetry data before it's sent to the cloud. The pipeline can cache data locally and sync with the cloud when connectivity is restored and route telemetry to Azure Monitor in cases of intermittent connectivity. Learn more about this here - Configure Azure Monitor pipeline - Azure Monitor | Microsoft Learn Why transformations matter Lower Costs: Filter and aggregate before ingestion to reduce ingestion volume and in turn lower ingestion costs Better Analytics: Standardized schemas mean faster queries and cleaner dashboards. Future-Proof: Built-in schema validation prevents surprises during deployment. Azure Monitor pipeline solves the challenges of high ingestion costs and complex analytics by enabling transformations before ingestion, so your data is clean, structured, and optimized before it even hits your Log Analytics Workspace. Check out a quick demo here - If the player doesn’t load, open the video in a new window: Open video Key features in public preview 1. Schema change detection One of the most exciting additions is schema validation for Syslog and CEF : Integrated into the “Check KQL Syntax” button in the Strato UI. Detects if your transformation introduces schema changes that break compatibility with standard tables. Provides actionable guidance: Option 1: Remove schema-changing transformations like aggregations. Option 2: Send data to a custom tables that support custom schemas. This ensures your pipeline remains robust and compliant with analytics requirements. For example, in the picture below, extending to new columns that don't match the schema of the syslog table throws an error during validation and asks the user to send to a custom table or remove the transformations. While in the case of the example below, filtering does not modify the schema of the data at all and so no validation error is thrown, and the user is able to send it to a standard table directly. 2. Pre-built KQL templates Apply ready-to-use templates for common transformations. Save time and minimize errors when writing queries. 3. Automatic schema standardization for syslog and CEF Automatically schematize CEF and syslog data to fit standard tables without any added transformations to convert raw data to syslog/CEF from the user. 4. Advanced filtering Drop unwanted events based on attributes like: Syslog: Facility, ProcessName, SeverityLevel. CEF: DeviceVendor, DestinationPort. Reduce noise and optimize ingestion costs. 5. Aggregation for high-volume logs Group events by key fields (e.g., DestinationIP, DeviceVendor) into 1-minute intervals. Summarize high-frequency logs for actionable insights. 6. Drop unnecessary fields Remove redundant columns to streamline data and reduce storage overhead. Supported KQL sunctions 1. Aggregation summarize (by), sum, max, min, avg, count, bin 2. Filtering where, contains, has, in, and, or, equality (==, !=), comparison (>, >=, <, <=) 3. Schematization extend, project, project-away, project-rename, project-keep, iif, case, coalesce, parse_json 4. Variables for Expressions or Functions let 5. Other Functions String: strlen, replace_string, substring, strcat, strcat_delim, extract Conversion: tostring, toint, tobool, tofloat, tolong, toreal, todouble, todatetime, totimespan Get started today Head to the Azure Portal and explore the new Azure Monitor pipeline transformations UI. Apply templates, validate your KQL, and experience the power of Azure Monitor pipeline transformations. Find more information on the public docs here - Configure Azure Monitor pipeline transformations - Azure Monitor | Microsoft Learn
Accelerating SCOM to Azure Monitor Migrations with Automated Analysis and ARM Template Generation
Accelerating SCOM to Azure Monitor Migrations with Automated Analysis and ARM Template Generation Azure Monitor has become the foundation for modern, cloud-scale monitoring on Azure. Built to handle massive volumes of telemetry across infrastructure, applications, and services, it provides a unified platform for metrics, logs, alerts, dashboards, and automation. As organizations continue to modernize their environments, Azure Monitor is increasingly the target state for enterprise monitoring strategies. With Azure Monitor increasingly becoming the destination platform, many organizations face a familiar challenge: migrating from System Center Operations Manager (SCOM). While both platforms serve the same fundamental purpose—keeping your infrastructure healthy and alerting you to problems—the migration path isn’t always straightforward. SCOM Management Packs contain years of accumulated monitoring logic: performance thresholds, event correlation rules, service discoveries, and custom scripts. Translating all of this into Azure Monitor’s paradigm of Log Analytics queries, alert rules, and Data Collection Rules can be a significant undertaking. To help with this challenge, members of the community have built and shared a tool that automates much of the analysis and artifact generation. The community-driven SCOM to Azure Monitor Migration Tool accepts Management Pack XML files and produces several outputs designed to accelerate migration planning and execution. The tool parses the Management Pack structure and identifies all monitors, rules, discoveries, and classes. Each component is analyzed for migration complexity: some translate directly to Azure Monitor equivalents, while others require custom implementation or may not have a direct equivalent. Results are organized into two clear categories: Auto-Migrated Components – Covered by the generated templates and ready for deployment Requires Manual Migration – Components that need custom implementation or review Instead of manually authoring Azure Resource Manager templates, the tool generates deployable infrastructure-as-code artifacts, including: Scheduled Query Alert rules mapped from SCOM monitors and rules Data Collection Rules for performance counters and Windows Events Custom Log DCRs for collecting script-generated log files Action Groups for notification routing Log Analytics workspace configuration (for new environments) For streamlined deployment, the tool offers a combined ARM template that deploys all resources in a single operation: Log Analytics workspace (create new or connect to an existing workspace) Action Groups with email notification All alert rules Data Collection Rules Monitoring Workbook One download, one deployment command — with configurable parameters for workspace settings, notification recipients, and custom log paths. The tool generates an Azure Monitor Workbook dashboard tailored to the Management Pack, including: Performance counter trends over time Event monitoring by severity with drill-down tables Service health overview (stopped services) Active alerts summary from Azure Resource Graph This provides immediate operational visibility once the monitoring configuration is deployed. Each migrated component includes the Kusto Query Language (KQL) equivalent of the original SCOM monitoring logic. These queries can be used as-is or refined to match environment-specific requirements. The workflow is designed to reduce the manual effort involved in migration planning: Export your Management Pack XML from SCOM Upload it to the tool Review the analysis — components are separated into auto-migrated and requires manual work Download the All-in-One ARM template (or individual templates) Customize parameters such as workspace name and action group recipients Deploy to your Azure subscription For a typical Management Pack, such as Windows Server Active Directory monitoring, you may see 120+ components that can be migrated directly, with an additional 15–20 components requiring manual review due to complex script logic or SCOM-specific functionality. The tool handles straightforward translations well: Performance threshold monitors become metric alerts or log-based alerts Windows Event collection rules become Data Collection Rule configurations Service monitors become scheduled query alerts against Heartbeat or Event tables Components that typically require manual attention: Complex PowerShell or VBScript probe actions Monitors that depend on SCOM-specific data sources Correlation rules spanning multiple data sources Custom workflows with proprietary logic The tool clearly identifies which category each component falls into, allowing teams to plan their migration effort with confidence. A Note on Validation This is a community tool, not an officially supported Microsoft product. Generated artifacts should always be reviewed and tested in a non-production environment before deployment. Every environment is different, and the tool makes reasonable assumptions that may require adjustment. Even so, starting with structured ARM templates and working KQL queries can significantly reduce time to deployment. Try It Out The tool is available at https://tinyurl.com/Scom2Azure.Upload a Management Pack, review the analysis, and see what your migration path looks like.Announcing Microsoft Azure Network Adapter (MANA) support for Existing VM SKUs
As a leader in cloud infrastructure, Microsoft ensures that Azure’s IaaS customers always have access to the latest hardware. Our goal is to consistently deliver technology to support business critical workloads with world class efficiency, reliability, and security. Customers benefit from cutting-edge performance enhancements and features, helping them to future proof their workloads while maintaining business continuity. In February 2026, Azure will be deploying the Microsoft Azure Network Adapter (MANA) for existing VM Size Families. The intent is to provide the benefits of new server hardware to customers of existing VM SKUs as they work towards migrating to newer SKUs. The deployments will be based on capacity needs and won’t be restricted by region. Once the hardware is available in a region, VMs can be deployed to it as needed. Workloads on operating systems which fully support MANA will benefit from sub-second Network Interface Card (NIC) firmware upgrades, higher throughput, lower latency, increased Security and Azure Boost-enabled data path accelerations. If your workload doesn't support MANA today, you'll still be able to access Azure’s network on MANA enabled SKUs, but performance will be comparable to previous generation (non-MANA) hardware. Check out the Azure Boost Overview and the Microsoft Azure Network Adapter (MANA) overview for more detailed information and OS compatibility. To determine whether your VMs are impacted and what actions (if any) you should take, start with MANA support for existing VM SKUs. This article provides additional information about which VM Sizes are eligible to be deployed on the new MANA-enabled hardware, what actions (if any) you should take, and how to determine if the workload has been deployed on MANA-enabled hardware.Beyond the Desktop: The Future of Development with Microsoft Dev Box and GitHub Codespaces
The modern developer platform has already moved past the desktop. We’re no longer defined by what’s installed on our laptops, instead we look at what tooling we can use to move from idea to production. An organisations developer platform strategy is no longer a nice to have, it sets the ceiling for what’s possible, an organisation can’t iterate it's way to developer nirvana if the foundation itself is brittle. A great developer platform shrinks TTFC (time to first commit), accelerates release velocity, and maybe most importantly, helps alleviate everyday frictions that lead to developer burnout. Very few platforms deliver everything an organization needs from a developer platform in one product. Modern development spans multiple dimensions, local tooling, cloud infrastructure, compliance, security, cross-platform builds, collaboration, and rapid onboarding. The options organizations face are then to either compromise on one or more of these areas or force developers into rigid environments that slow productivity and innovation. This is where Microsoft Dev Box and GitHub Codespaces come into play. On their own, each addresses critical parts of the modern developer platform: Microsoft Dev Box provides a full, managed cloud workstation. Dev Box gives developers a consistent, high-performance environment while letting central IT apply strict governance and control. Internally at Microsoft, we estimate that usage of Dev Box by our development teams delivers savings of 156 hours per year per developer purely on local environment setup and upkeep. We have also seen significant gains in other key SPACE metrics reducing context-switching friction and improving build/test cycles. Although the benefits of Dev Box are clear in the results demonstrated by our customers it is not without its challenges. The biggest challenge often faced by Dev Box customers is its lack of native Linux support. At the time of writing and for the foreseeable future Dev Box does not support native Linux developer workstations. While WSL2 provides partial parity, I know from my own engineering projects it still does not deliver the full experience. This is where GitHub Codespaces comes into this story. GitHub Codespaces delivers instant, Linux-native environments spun up directly from your repository. It’s lightweight, reproducible, and ephemeral ideal for rapid iteration, PR testing, and cross-platform development where you need Linux parity or containerized workflows. Unlike Dev Box, Codespaces can run fully in Linux, giving developers access to native tools, scripts, and runtimes without workarounds. It also removes much of the friction around onboarding: a new developer can open a repository and be coding in minutes, with the exact environment defined by the project’s devcontainer.json. That said, Codespaces isn’t a complete replacement for a full workstation. While it’s perfect for isolated project work or ephemeral testing, it doesn’t provide the persistent, policy-controlled environment that enterprise teams often require for heavier workloads or complex toolchains. Used together, they fill the gaps that neither can cover alone: Dev Box gives the enterprise-grade foundation, while Codespaces provides the agile, cross-platform sandbox. For organizations, this pairing sets a higher ceiling for developer productivity, delivering a truly hybrid, agile and well governed developer platform. Better Together: Dev Box and GitHub Codespaces in action Together, Microsoft Dev Box and GitHub Codespaces deliver a hybrid developer platform that combines consistency, speed, and flexibility. Teams can spin up full, policy-compliant Dev Box workstations preloaded with enterprise tooling, IDEs, and local testing infrastructure, while Codespaces provides ephemeral, Linux-native environments tailored to each project. One of my favourite use cases is having local testing setups like a Docker Swarm cluster, ready to go in either Dev Box or Codespaces. New developers can jump in and start running services or testing microservices immediately, without spending hours on environment setup. Anecdotally, my time to first commit and time to delivering “impact” has been significantly faster on projects where one or both technologies provide local development services out of the box. Switching between Dev Boxes and Codespaces is seamless every environment keeps its own libraries, extensions, and settings intact, so developers can jump between projects without reconfiguring or breaking dependencies. The result is a turnkey, ready-to-code experience that maximizes productivity, reduces friction, and lets teams focus entirely on building, testing, and shipping software. To showcase this value, I thought I would walk through an example scenario. In this scenario I want to simulate a typical modern developer workflow. Let's look at a day in the life of a developer on this hybrid platform building an IOT project using Python and React. Spin up a ready-to-go workstation (Dev Box) for Windows development and heavy builds. Launch a Linux-native Codespace for cross-platform services, ephemeral testing, and PR work. Run "local" testing like a Docker Swarm cluster, database, and message queue ready to go out-of-the-box. Switch seamlessly between environments without losing project-specific configurations, libraries, or extensions. 9:00 AM – Morning Kickoff on Dev Box I start my day on my Microsoft Dev Box, which gives me a fully-configured Windows environment with VS Code, design tools, and Azure integrations. I select my teams project, and the environment is pre-configured for me through the Dev Box catalogue. Fortunately for me, its already provisioned. I could always self service another one using the "New Dev Box" button if I wanted too. I'll connect through the browser but I could use the desktop app too if I wanted to. My Tasks are: Prototype a new dashboard widget for monitoring IoT device temperature. Use GUI-based tools to tweak the UI and preview changes live. Review my Visio Architecture. Join my morning stand up. Write documentation notes and plan API interactions for the backend. In a flash, I have access to my modern work tooling like Teams, I have this projects files already preloaded and all my peripherals are working without additional setup. Only down side was that I did seem to be the only person on my stand up this morning? Why Dev Box first: GUI-heavy tasks are fast and responsive. Dev Box’s environment allows me to use a full desktop. Great for early-stage design, planning, and visual work. Enterprise Apps are ready for me to use out of the box (P.S. It also supports my multi-monitor setup). I use my Dev Box to make a very complicated change to my IoT dashboard. Changing the title from "IoT Dashboard" to "Owain's IoT Dashboard". I preview this change in a browser live. (Time for a coffee after this hardwork). The rest of the dashboard isnt loading as my backend isnt running... yet. 10:30 AM – Switching to Linux Codespaces Once the UI is ready, I push the code to GitHub and spin up a Linux-native GitHub Codespace for backend development. Tasks: Implement FastAPI endpoints to support the new IoT feature. Run the service on my Codespace and debug any errors. Why Codespaces now: Linux-native tools ensure compatibility with the production server. Docker and containerized testing run natively, avoiding WSL translation overhead. The environment is fully reproducible across any device I log in from. 12:30 PM – Midday Testing & Sync I toggle between Dev Box and Codespaces to test and validate the integration. I do this in my Dev Box Edge browser viewing my codespace (I use my Codespace in a browser through this demo to highlight the difference in environments. In reality I would leverage the VSCode "Remote Explorer" extension and its GitHub Codespace integration to use my Codespace from within my own desktop VSCode but that is personal preference) and I use the same browser to view my frontend preview. I update the environment variable for my frontend that is running locally in my Dev Box and point it at the port running my API locally on my Codespace. In this case it was a web socket connection and HTTPS calls to port 8000. I can make this public by changing the port visibility in my Codespace. https://fluffy-invention-5x5wp656g4xcp6x9-8000.app.github.dev/api/devices wss://fluffy-invention-5x5wp656g4xcp6x9-8000.app.github.dev/ws This allows me to: Preview the frontend widget on Dev Box, connecting to the backend running in Codespaces. Make small frontend adjustments in Dev Box while monitoring backend logs in Codespaces. Commit changes to GitHub, keeping both environments in sync and leveraging my CI/CD for deployment to the next environment. We can see the Dev Box running local frontend and the Codespace running the API connected to each other, making requests and displaying the data in the frontend! Hybrid advantage: Dev Box handles GUI previews comfortably and allows me to live test frontend changes. Codespaces handles production-aligned backend testing and Linux-native tools. Dev Box allows me to view all of my files in one screen with potentially multiple Codespaces running in browser of VS Code Desktop. Due to all of those platform efficiencies I have completed my days goals within an hour or two and now I can spend the rest of my day learning about how to enable my developers to inner source using GitHub CoPilot and MCP (Shameless plug). The bottom line There are some additional considerations when architecting a developer platform for an enterprise such as private networking and security not covered in this post but these are implementation details to deliver the described developer experience. Architecting such a platform is a valuable investment to deliver the developer platform foundations we discussed at the top of the article. While in this demo I have quickly built I was working in a mono repository in real engineering teams it is likely (I hope) that an application is built of many different repositories. The great thing about Dev Box and Codespaces is that this wouldn’t slow down the rapid development I can achieve when using both. My Dev Box would be specific for the project or development team, pre loaded with all the tools I need and potentially some repos too! When I need too I can quickly switch over to Codespaces and work in a clean isolated environment and push my changes. In both cases any changes I want to deliver locally are pushed into GitHub (Or ADO), merged and my CI/CD ensures that my next step, potentially a staging environment or who knows perhaps *Whispering* straight into production is taken care of. Once I’m finished I delete my Codespace and potentially my Dev Box if I am done with the project, knowing I can self service either one of these anytime and be up and running again! Now is there overlap in terms of what can be developed in a Codespace vs what can be developed in Azure Dev Box? Of course, but as organisations prioritise developer experience to ensure release velocity while maintaining organisational standards and governance then providing developers a windows native and Linux native service both of which are primarily charged on the consumption of the compute* is a no brainer. There are also gaps that neither fill at the moment for example Microsoft Dev Box only provides windows compute while GitHub Codespaces only supports VS Code as your chosen IDE. It's not a question of which service do I choose for my developers, these two services are better together! *Changes have been announced to Dev Box pricing. A W365 license is already required today and dev boxes will continue to be managed through Azure. For more information please see: Microsoft Dev Box capabilities are coming to Windows 365 - Microsoft Dev Box | Microsoft LearnAutomated Test Framework - Missing Tests in Test Explorer
Have your tests created using the Logic Apps Standard Automated Test Framework disappeared from the Test Explorer in VS Code all of a sudden? The answer is a mismatch between the MSTest versions used. A recent update in the C# DevKit extension changed the minimum requirements for the MSTest library - it now requires the minimum of 3.7.*. The project scaffolding created byt the Logic Apps Standard extension uses 3.0.2. The good news is that you can fix this by just changing package versions on your project. Follow the instructions below to have this fixed: Open the .csproj that the extension created (e.g. LogicApps.csproj). Find the ItemGroup containing your package references. It should look like this: <ItemGroup> <PackageReference Include="MSTest" Version="3.2.0"/> <PackageReference Include="Microsoft.NET.Test.Sdk" Version="17.8.0"/> <PackageReference Include="MSTest.TestAdapter" Version="3.2.0"/> <PackageReference Include="MSTest.TestFramework" Version="3.2.0"/> <PackageReference Include="Microsoft.Azure.Workflows.WebJobs.Tests.Extension" Version="1.0.0"/> <PackageReference Include="coverlet.collector" Version="3.1.2"/> </ItemGroup> Replace the package references with this new version: <ItemGroup> <PackageReference Include="Microsoft.Azure.Workflows.WebJobs.Tests.Extension" Version="1.*" /> <PackageReference Include="MSTest" Version="4.0.2" /> <PackageReference Include="coverlet.collector" Version="3.2.0" /> </ItemGroup> Once you make this change, restart VSCode window and rebuild your project - Test Explorer will start showing your tests again. Notice that this package reference is simplified as some of the previous references are already in the dependency chain, so don't need to be explicitly added. ℹ️ We are updating our extension to make sure that new projects are being generated with the new values, but you should make those changes manually on existing projects.GA: DCasv6 and ECasv6 confidential VMs based on 4th Generation AMD EPYC™ processors
Today, Azure has expanded its confidential computing offerings with the general availability of the DCasv6 and ECasv6 confidential VMs. Regional availability Jan 30 2026: Canada Central, Canada East, Norway East, Norway West, Italy North, Germany North, France South, Australia East, West US, West US 3, Germany West Central Sep 16 2025: Korea Central, South Africa North, Switzerland North, UAE North, UK South, West Central US These VMs are powered by 4th generation AMD EPYC™ processors and feature advanced Secure Encrypted Virtualization-Secure Nested Paging (SEV-SNP) technology. These confidential VMs offer: Hardware-rooted attestation Memory encryption in multi-tenant environments Enhanced data confidentiality Protection against cloud operators, administrators, and insider threats You can get started today by creating confidential VMs in the Azure portal as explained here. Highlights: 4th generation AMD EPYC processors with SEV-SNP 25% performance improvement over previous generation Ability to rotate keys online AES-256 memory encryption enabled by default Up to 96 vCPUs and 672 GiB RAM for demanding workloads Streamlined Security Organizations in certain regulated industries and sovereign customers migrating to Microsoft Azure need strict security and compliance across all layers of the stack. With Azure Confidential VMs, organizations can ensure the integrity of the boot sequence and the OS kernel while helping administrators safeguard sensitive data against advanced and persistent threats. The DCasv6 and ECasv6 family of confidential VMs support online key rotation to give organizations the ability to dynamically adapt their defenses to rapidly evolving threats. Additionally, these new VMs include AES-256 memory encryption as a default feature. Customers have the option to use Virtualization-Based Security (VBS) in Windows, which is currently in preview to protect private keys from exfiltration via the Guest OS or applications. With VBS enabled, keys are isolated within a secure process, allowing key operations to be carried out without exposing them outside this environment. Faster Performance In addition to the newly announced security upgrades, the new DCasv6 and ECasv6 family of confidential VMs have demonstrated up to 25% improvement in various benchmarks compared to our previous generation of confidential VMs powered by AMD. Organizations that need to run complex workflows like combining multiple private data sets to perform joint analysis, medical research or Confidential AI services can use these new VMs to accelerate their sensitive workload faster than ever before. "While we began our journey with v5 confidential VMs, now we’re seeing noticeable performance improvements with the new v6 confidential VMs based on 4th Gen AMD EPYC “Genoa” processors. These latest confidential VMs are being rolled out across many Azure regions worldwide, including the UAE. So as v6 becomes available in more regions, we can deploy AMD based confidential computing wherever we need, with the same consistency and higher performance." — Mohammed Retmi, Vice President - Sovereign Public Cloud, at Core42, a G42 company. "KT is leveraging Azure confidential computing to secure sensitive and regulated data from its telco business in the cloud. With new V6 CVM offerings in Korea Central Region, KT extends its use to help Korean customers with enhanced security requirements, including regulated industries, benefit from the highest data protection as well as the fastest performance by the latest AMD SEV-SNP technology through its Secure Public Cloud built with Azure confidential computing." — Woojin Jung, EVP, KT Corporation Kubernetes support Deploy resilient, globally available applications on confidential VMs with our managed Kubernetes experience - Azure Kubernetes Service (AKS). AKS now supports the new DCasv6 and ECasv6 family of confidential VMs, enabling organizations to easily deploy, scale and manage confidential Kubernetes clusters on Azure, streamlining developer workflows and reducing manual tasks with integrated continuous integration and continuous delivery (CI/CD) pipelines. AKS brings integrated monitoring and logging to confidential VM node pools with in-depth performance and health insights, the clusters and containerized applications. Azure Linux 3.0 and Ubuntu 24.04 support are now in preview. AKS integration in this generation of confidential VMs also brings support for Azure Linux 3.0, that contains the most essential packages to be resource efficient and contains a secure, hardened Linux kernel specifically tuned for Azure cloud deployments. Ubuntu 24.04 clusters are also supported in addition to Azure Linux 3.0. Organizations wanting to ease the orchestration issues associated with deploying, scaling and managing hundreds of confidential VM node pools can now choose from either of these two for their node pools. General purpose & Memory-intensive workloads Featuring general purpose optimized memory-to-vCPU ratios and support for up to 96 vCPUs and 384 GiB RAM, the DCasv6-series delivers enterprise-grade performance. The DCasv6-series enables organizations to run sensitive workloads with hardware-based security guarantees, making them ideal for applications processing regulated or confidential data. For more memory demanding workloads that exceed even the capabilities of the DCasv6 series, the new ECasv6-series offer high memory-to-vCPU ratios with increased scalability up to 96 vCPUs and 672 GiB of RAM, nearly doubling the memory capacity of DCasv6. You can get started today by creating confidential VMs in the Azure portal as explained here. Additional Resources: Quickstart: Create confidential VM with Azure portal Quickstart: Create confidential VM with ARM template Azure confidential virtual machines FAQ
Create next-gen voice agents with Azure AI's Voice Live API and Azure Communication Services
Today at Microsoft Build, we’re excited to announce the General Availability of bidirectional audio streaming for Azure Communication Services Call Automation SDK. Unveiling the power of speech-to-speech AI through Azure Communication Services! As previously seen at Microsoft Ignite in November 2024 the Call Automation bidirectional streaming APIs already work with services like Azure OpenAI to build conversational voice agents through speech to speech integrations. Now with General Availability release of Call Automation bidirectional streaming API and Azure AI Speech Services Voice Live API (Preview), creating voice agents has never been easier. Imagine AI agents that deliver seamless, low-latency, and naturally fluent conversations, transforming the way businesses and customers interact. Bidirectional Streaming APIs allow customers to stream audio from ongoing calls to their webserver in near real-time, where their voice enabled Large Language Models (LLMs) can ingest the audio to reason over and provide voice responses to stream back into the call. In this release we have added support for extra security by adding JSON Web Token (JWT) based authentication for the websocket connection allowing developers to make sure they’re creating secure solutions. As industries like customer service, education, HR, gaming, and public services see a surge in demand for generative AI voice chatbots, businesses are seeking real-time, natural-sounding voice interactions with the latest and greatest GenAI models. Integrating Azure Communication Services with the new Voice Live API from Azure AI Speech Services provides a low-latency interface that facilitates streaming speech input and output with Azure AI Speech’s advanced audio and voice capabilities. It supports multiple languages, diverse voices, and customization, and can even integrate with avatars for enhanced engagement. On the server side, powerful language models interpret the caller's queries and stream human-like responses back in real time, ensuring fluid and engaging conversations. By integrating these two technologies customers can create new innovative solutions for: Multilingual agents Develop virtual customer service representatives capable of of having conversations with end customers in their preferred language, allowing customers creating solutions for multilingual regions to create one solution to serve multiple languages and regions. Noise suppression and echo cancellation For AI voice agents to be effective they need clear audio to understand what the user is requesting, in order to improve AI efficiency, you can use out of the box noise suppression and echo cancellation built into the Voice Live API to help provide your AI agent the best quality audio to be able to clearly understand the end users requests and assist them. Support for branded voices Build voice agents that stay on brand with custom voices that represent your brand in any interaction with the customer, use Azure AI Speech services to create custom voice models that represent your brand and provide familiarity for your customers. How to Integrate Azure Communication Services with Azure AI Speech Service Voice Live API Language support With the integration to Voice Live API, you can now create solutions for over 150+ locales for speech input and output with 600+ realistic voices out of the box. I if these voices don’t suit your needs, customers can take this one step further and create custom speech models for their brand. How to start bidirectional streaming var mediaStreamingOptions = new MediaStreamingOptions( new Uri(websocketUri), MediaStreamingContent.Audio, MediaStreamingAudioChannel.Mixed, startMediaStreaming: true ) { EnableBidirectional = true, AudioFormat = AudioFormat.Pcm24KMono }; How to connect to Voice Live API (Preview) string GetSessionUpdate() { var jsonObject = new { type = "session.update", session = new { turn_detection = new { type = "azure_semantic_vad", threshold = 0.3, prefix_padding_ms = 200, silence_duration_ms = 200, remove_filler_words = false }, input_audio_noise_reduction = new { type = "azure_deep_noise_suppression" }, input_audio_echo_cancellation = new { type = "server_echo_cancellation" }, voice = new { name = "en-US-Aria:DragonHDLatestNeural", type = "azure-standard", temperature = 0.8 } } }; Next Steps The SDK and documentation along will be available in the next few weeks following this announcement, allowing you to build your own solutions using Azure Communication Services and Azure AI Voice Live API. You can download our latest sample from GitHub to try this for yourself. To learn more about the Voice Live API and all its different capabilities, see Azure AI Blog.
