Implementing Disaster Recovery for Azure App Service Web Applications
Starting March 31, 2025, Microsoft will no longer automatically place Azure App Service web applications in disaster recovery mode in the event of a regional disaster. This change emphasizes the importance of implementing robust disaster recovery (DR) strategies to ensure the continuity and resilience of your web applications. Here’s what you need to know and how you can prepare. Understanding the Change Azure App Service has been a reliable platform for hosting web applications, REST APIs, and mobile backends, offering features like load balancing, autoscaling, and automated management. However, beginning March 31, 2025, in the event of a regional disaster, Azure will not automatically place your web applications in disaster recovery mode. This means that you, as a developer or IT professional, need to proactively implement disaster recovery techniques to safeguard your applications and data. Why This Matters Disasters, whether natural or technical, can strike without warning, potentially causing significant downtime and data loss. By taking control of your disaster recovery strategy, you can minimize the impact of such events on your business operations. Implementing a robust DR plan ensures that your applications remain available and your data remains intact, even in the face of regional outages. Common Disaster Recovery Techniques To prepare for this change, consider the following commonly used disaster recovery techniques: Multi-Region Deployment: Deploy your web applications across multiple Azure regions. This approach ensures that if one region goes down, your application can continue to run in another region. You can use Azure Traffic Manager or Azure Front Door to route traffic to the healthy region. Multi-region load balancing with Traffic Manager and Application Gateway Highly available multi-region web app Regular Backups: Implement regular backups of your application data and configurations. Azure App Service provides built-in backup and restore capabilities that you can schedule to run automatically. Back up an app in App Service How to automatically backup App Service & Function App configurations Active-Active or Active-Passive Configuration: Set up your applications in an active-active or active-passive configuration. In an active-active setup, both regions handle traffic simultaneously, providing high availability. In an active-passive setup, the secondary region remains on standby and takes over only if the primary region fails. About active-active VPN gateways Design highly available gateway connectivity Automated Failover: Use automated failover mechanisms to switch traffic to a secondary region seamlessly. This can be achieved using Azure Site Recovery or custom scripts that detect failures and initiate failover processes. Add Azure Automation runbooks to Site Recovery recovery plans Create and customize recovery plans in Azure Site Recovery Monitoring and Alerts: Implement comprehensive monitoring and alerting to detect issues early and respond promptly. Azure Monitor and Application Insights can help you track the health and performance of your applications. Overview of Azure Monitor alerts Application Insights OpenTelemetry overview Steps to Implement a Disaster Recovery Plan Assess Your Current Setup: Identify all the resources your application depends on, including databases, storage accounts, and networking components. Choose a DR Strategy: Based on your business requirements, choose a suitable disaster recovery strategy (e.g., multi-region deployment, active-active configuration). Configure Backups: Set up regular backups for your application data and configurations. Test Your DR Plan: Regularly test your disaster recovery plan to ensure it works as expected. Simulate failover scenarios to validate that your applications can recover quickly. Document and Train: Document your disaster recovery procedures and train your team to execute them effectively. Conclusion While the upcoming change in Azure App Service’s disaster recovery policy may seem daunting, it also presents an opportunity to enhance the resilience of your web applications. By implementing robust disaster recovery techniques, you can ensure that your applications remain available and your data remains secure, no matter what challenges come your way. Start planning today to stay ahead of the curve and keep your applications running smoothly. Recover from region-wide failure - Azure App Service Reliability in Azure App Service Multi-Region App Service App Approaches for Disaster Recovery Feel free to share your thoughts or ask questions in the comments below. Let's build a resilient future together! 🚀Announcing General Availability: Azure Logic Apps Standard Custom Code with .NET 8
We’re excited to announce the General Availability (GA) of Custom Code support in Azure Logic Apps Standard with .NET 8. This release marks a significant step forward in enabling developers to build more powerful, flexible, and maintainable integration workflows using familiar .NET tools and practices. With this capability, developers can now embed custom .NET 8 code directly within their Logic Apps Standard workflows. This unlocks advanced logic scenarios, promotes code reuse, and allows seamless integration with existing .NET libraries and services—making it easier than ever to build enterprise-grade solutions on Azure. What’s New in GA This GA release introduces several key enhancements that improve the development experience and expand the capabilities of custom code in Logic Apps: Bring Your Own Packages Developers can now include and manage their own NuGet packages within custom code projects without having to resolve conflicts with the dependencies used by the language worker host. The update includes the ability to load the assembly dependencies of the custom code project into a separate Assembly context allowing you to bring any NET8 compatible dependent assembly versions that your project need. There are only three exceptions to this rule: Microsoft.Extensions.Logging.Abstractions Microsoft.Extensions.DependencyInjection.Abstractions Microsoft.Azure.Functions.Extensions.Workflows.Abstractions Dependency Injection Native Support Custom code now supports native Dependency Injection (DI), enabling better separation of concerns and more testable, maintainable code. This aligns with modern .NET development patterns and simplifies service management within your custom logic. To enable Dependency Injection, developers will need to provide a StartupConfiguration class, defining the list of dependencies: using Microsoft.Azure.Functions.Extensions.Workflows; using Microsoft.Extensions.DependencyInjection; public class StartupConfiguration : IConfigureStartup { /// <summary> /// Configures services for the Azure Functions application. /// </summary> /// <param name="services">The service collection to configure.</param> public void Configure(IServiceCollection services) { // Register the routing service with dependency injection services.AddSingleton<IRoutingService, OrderRoutingService>(); services.AddSingleton<IDiscountService, DiscountService>(); } } You will also need to initialize those register those services during your custom code class constructor: public class MySampleFunction { private readonly ILogger<MySampleFunction> logger; private readonly IRoutingService routingService; private readonly IDiscountService discountService; public MySampleFunction(ILoggerFactory loggerFactory, IRoutingService routingService, IDiscountService discountService) { this.logger = loggerFactory.CreateLogger<MySampleFunction>(); this.routingService = routingService; this.discountService = discountService; } // your function logic here } Improved Authoring Experience The development experience has been significantly enhanced with improved tooling and templates. Whether you're using Visual Studio or Visual Studio Code, you’ll benefit from streamlined scaffolding, local debugging, and deployment workflows that make building and managing custom code faster and more intuitive. The following user experience improvements were added: Local functions metadata are kept between VS Code sessions, so you don't receive validation errors when editing workflows that depend on the local functions. Projects are also built when designer starts, so you don't have to manually update references. New context menu gestures, allowing you to create new local functions or build your functions project directly from the explorer area Unified debugging experience, making it easer for you to debug. We have now a single task for debugging custom code and logic apps, which makes starting a new debug session as easy as pressing F5. Learn More To get started with custom code in Azure Logic Apps Standard, visit the official Microsoft Learn documentation: Create and run custom code in Azure Logic Apps Standard You can also find example code for Dependency injection wsilveiranz/CustomCode-Dependency-Injection
How to Ingest Microsoft Intune Logs into Microsoft Sentinel
For many organizations using Microsoft Intune to manage devices, integrating Intune logs into Microsoft Sentinel is an essential for security operations (Incorporate the device into the SEIM). By routing Intune’s device management and compliance data into your central SIEM, you gain a unified view of endpoint events and can set up alerts on critical Intune activities e.g. devices falling out of compliance or policy changes. This unified monitoring helps security and IT teams detect issues faster, correlate Intune events with other security logs for threat hunting and improve compliance reporting. We’re publishing these best practices to help unblock common customer challenges in configuring Intune log ingestion. In this step-by-step guide, you’ll learn how to successfully send Intune logs to Microsoft Sentinel, so you can fully leverage Intune data for enhanced security and compliance visibility. Prerequisites and Overview Before configuring log ingestion, ensure the following prerequisites are in place: Microsoft Sentinel Enabled Workspace: A Log Analytics Workspace with Microsoft Sentinel enabled; For information regarding setting up a workspace and onboarding Microsoft Sentinel, see: Onboard Microsoft Sentinel - Log Analytics workspace overview. Microsoft Sentinel is now available in the Defender Portal, connect your Microsoft Sentinel Workspace to the Defender Portal: Connect Microsoft Sentinel to the Microsoft Defender portal - Unified security operations. Intune Administrator permissions: You need appropriate rights to configure Intune Diagnostic Settings. For information, see: Microsoft Entra built-in roles - Intune Administrator. Log Analytics Contributor role: The account configuring diagnostics should have permission to write to the Log Analytics workspace. For more information on the different roles, and what they can do, go to Manage access to log data and workspaces in Azure Monitor. Intune diagnostic logging enabled: Ensure that Intune diagnostic settings are configured to send logs to Azure Monitor / Log Analytics, and that devices and users are enrolled in Intune so that relevant management and compliance events are generated. For more information, see: Send Intune log data to Azure Storage, Event Hubs, or Log Analytics. Configure Intune to Send Logs to Microsoft Sentinel Sign in to the Microsoft Intune admin center. Select Reports > Diagnostics settings. If it’s the first time here, you may be prompted to “Turn on” diagnostic settings for Intune; enable it if so. Then click “+ Add diagnostic setting” to create a new setting: Select Intune Log Categories. In the “Diagnostic setting” configuration page, give the setting a name (e.g. “Microsoft Sentinel Intune Logs Demo”). Under Logs to send, you’ll see checkboxes for each Intune log category. Select the categories you want to forward. For comprehensive monitoring, check AuditLogs, OperationalLogs, DeviceComplianceOrg, and Devices. The selected log categories will be sent to a table in the Microsoft Sentinel Workspace. Configure Destination Details – Microsoft Sentinel Workspace. Under Destination details on the same page, select your Azure Subscription then select the Microsoft Sentinel workspace. Save the Diagnostic Setting. After you click save, the Microsoft Intune Logs will will be streamed to 4 tables which are in the Analytics Tier. For pricing on the analytic tier check here: Plan costs and understand pricing and billing. Verify Data in Microsoft Sentinel. After configuring Intune to send diagnostic data to a Microsoft Sentinel Workspace, it’s crucial to verify that the Intune logs are successfully flowing into Microsoft Sentinel. You can do this by checking specific Intune log tables both in the Microsoft 365 Defender portal and in the Azure Portal. The key tables to verify are: IntuneAuditLogs IntuneOperationalLogs IntuneDeviceComplianceOrg IntuneDevices Microsoft 365 Defender Portal (Unified) Azure Portal (Microsoft Sentinel) 1. Open Advanced Hunting: Sign in to the https://security.microsoft.com (the unified portal). Navigate to Advanced Hunting. – This opens the unified query editor where you can search across Microsoft Defender data and any connected Sentinel data. 2. Find Intune Tables: In the Advanced hunting Schema pane (on the left side of the query editor), scroll down past the Microsoft Sentinel Tables. Under the LogManagement Section Look for IntuneAuditLogs, IntuneOperationalLogs, IntuneDeviceComplianceOrg, and IntuneDevices in the list. Microsoft Sentinel in Defender Portal – Tables 1. Navigate to Logs: Sign in to the https://portal.azure.com and open Microsoft Sentinel. Select your Sentinel workspace, then click Logs (under General). 2. Find Intune Tables: In the Logs query editor that opens, you’ll see a Schema or tables list on the left. If it’s collapsed, click >> to expand it. Scroll down to find LogManagement and expand it; look for these Intune-related tables: IntuneAuditLogs, IntuneOperationalLogs, IntuneDeviceComplianceOrg, and IntuneDevices Microsoft Sentinel in Azure Portal – Tables Querying Intune Log Tables in Sentinel – Once the tables are present, use Kusto Query Language (KQL) in either portal to view and analyze Intune data: Microsoft 365 Defender Portal (Unified) Azure Portal (Microsoft Sentinel) In the Advanced Hunting page, ensure the query editor is visible (select New query if needed). Run a simple KQL query such as: IntuneDevice | take 5 Click Run query to display sample Intune device records. If results are returned, it confirms that Intune data is being ingested successfully. Note that querying across Microsoft Sentinel data in the unified Advanced Hunting view requires at least the Microsoft Sentinel Reader role. In the Azure Logs blade, use the query editor to run a simple KQL query such as: IntuneDevice | take 5 Select Run to view the results in a table showing sample Intune device data. If results appear, it confirms that your Intune logs are being collected successfully. You can select any record to view full event details and use KQL to further explore or filter the data - for example, by querying IntuneDeviceComplianceOrg to identify devices that are not compliant and adjust the query as needed. Once Microsoft Intune logs are flowing into Microsoft Sentinel, the real value comes from transforming that raw device and audit data into actionable security signals. To achieve this, you should set up detection rules that continuously analyze the Intune logs and automatically flag any risky or suspicious behavior. In practice, this means creating custom detection rules in the Microsoft Defender portal (part of the unified XDR experience) see [https://learn.microsoft.com/en-us/defender-xdr/custom-detection-rules] and scheduled analytics rules in Microsoft Sentinel (in either the Azure Portal or the unified Defender portal interface) see:[Create scheduled analytics rules in Microsoft Sentinel | Microsoft Learn]. These detection rules will continuously monitor your Intune telemetry – tracking device compliance status, enrollment activity, and administrative actions – and will raise alerts whenever they detect suspicious or out-of-policy events. For example, you can be alerted if a large number of devices fall out of compliance, if an unusual spike in enrollment failures occurs, or if an Intune policy is modified by an unexpected account. Each alert generated by these rules becomes an incident in Microsoft Sentinel (and in the XDR Defender portal’s unified incident queue), enabling your security team to investigate and respond through the standard SOC workflow. In turn, this converts raw Intune log data into high-value security insights: you’ll achieve proactive detection of potential issues, faster investigation by pivoting on the enriched Intune data in each incident, and even automated response across your endpoints (for instance, by triggering playbooks or other automated remediation actions when an alert fires). Use this Detection Logic to Create a detection Rule IntuneDeviceComplianceOrg | where TimeGenerated > ago(24h) | where ComplianceState != "Compliant" | summarize NonCompliantCount = count() by DeviceName, TimeGenerated | where NonCompliantCount > 3 Additional Tips: After confirming data ingestion and setting up alerts, you can leverage other Microsoft Sentinel features to get more value from your Intune logs. For example: Workbooks for Visualization: Create custom workbooks to build dashboards for Intune data (or check if community-contributed Intune workbooks are available). This can help you monitor device compliance trends and Intune activities visually. Hunting and Queries: Use advanced hunting (KQL queries) to proactively search through Intune logs for suspicious activities or trends. The unified Defender portal’s Advanced Hunting page can query both Sentinel (Intune logs) and Defender data together, enabling correlation across Intune and other security data. For instance, you might join IntuneDevices data with Azure AD sign-in logs to investigate a device associated with risky sign-ins. Incident Management: Leverage Sentinel’s Incidents view (in Azure portal) or the unified Incidents queue in Defender to investigate alerts triggered by your new rules. Incidents in Sentinel (whether created in Azure or Defender portal) will appear in the connected portal, allowing your security operations team to manage Intune-related alerts just like any other security incident. Built-in Rules & Content: Remember that Microsoft Sentinel provides many built-in Analytics Rule templates and Content Hub solutions. While there isn’t a native pre-built Intune content pack as of now, you can use general Sentinel features to monitor Intune data. Frequently Asked Questions If you’ve set everything up but don’t see logs in Sentinel, run through these checks: Check Diagnostic Settings Go to the Microsoft Intune admin center → Reports → Diagnostic settings. Make sure the setting is turned ON and sending the right log categories to the correct Microsoft Sentinel workspace. Confirm the Right Workspace Double-check that the Azure subscription and Microsoft Sentinel workspace are selected. If you have multiple tenants/directories, make sure you’re in the right one. Verify Permissions Make Sure Logs Are Being Generated If no devices are enrolled or no actions have been taken, there may be nothing to log yet. Try enrolling a device or changing a policy to trigger logs. Check Your Queries Make sure you’re querying the correct workspace and time range in Microsoft Sentinel. Try a direct query like: IntuneAuditLogs | take 5 Still Nothing? Try deleting and re-adding the diagnostic setting. Most issues come down to permissions or selecting the wrong workspace. How long are Intune logs retained, and how can I keep them longer? The analytics tier keeps data in the interactive retention state for 90 days by default, extensible for up to two years. This interactive state, while expensive, allows you to query your data in unlimited fashion, with high performance, at no charge per query: Log retention tiers in Microsoft Sentinel. We hope this helps you to successfully connect your resources and end-to-end ingest Intune logs into Microsoft Sentinel. If you have any questions, leave a comment below or reach out to us on X @MSFTSecSuppTeam!
Excited to share my latest open-source project: KubeCost Guardian
After seeing how many DevOps teams struggle with Kubernetes cost visibility on Azure, I built a full-stack cost optimization platform from scratch. 𝗪𝗵𝗮𝘁 𝗶𝘁 𝗱𝗼𝗲𝘀: ✅ Real-time AKS cluster monitoring via Azure SDK ✅ Cost breakdown per namespace, node, and pod ✅ AI-powered recommendations generated from actual cluster state ✅ One-click optimization actions ✅ JWT-secured dashboard with full REST API 𝗧𝗲𝗰𝗵 𝗦𝘁𝗮𝗰𝗸: - React 18 + TypeScript + Vite - Tailwind CSS + shadcn/ui + Recharts - Node.js + Express + TypeScript - Azure SDK (@azure/arm-containerservice) - JWT Authentication + Azure Service Principal 𝗪𝗵𝗮𝘁 𝗺𝗮𝗸𝗲𝘀 𝗶𝘁 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁: Most cost tools show you generic estimates. KubeCost Guardian reads your actual VM size, node count, and cluster configuration to generate recommendations that are specific to your infrastructure not averages. For example, if your cluster has only 2 nodes with no autoscaler enabled, it immediately flags the HA risk and calculates exactly how much you'd save by switching to Spot instances based on your actual VM size. This project is fully open-source and built for the DevOps community. ⭐ GitHub: https://github.com/HlaliMedAmine/kubecost-guardian This project represents hours of hard work, and passion. I decided to make it open-source so everyone can benefit from it 🤝 ,If you find it useful, I’d really appreciate your support . Your support motivates me to keep building and sharing more powerful projects 👌. More exciting ideas are coming soon… stay tuned! 🔥.
Building a Production-Ready Azure Lighthouse Deployment Pipeline with EPAC
Recently I worked on an interesting project for an end-to-end Azure Lighthouse implementation. What really stood out to me was the combination of Azure Lighthouse, EPAC, DevOps, and workload identity federation. The deployment model was so compelling that I decided to build and validate the full solution hands-on in my own personal Azure tenants. The result is a detailed article that documents the entire journey, including pipeline design, implementation steps, and the scripts I prepared along the way. You can read the full article herePipeline Intelligence is live and open-source real-time Azure DevOps monitoring powered by AI .
Every DevOps team I've worked with had the same problem: Slow pipelines. Zero visibility. No idea where to start. So I stopped complaining and built the solution. So I built something about it. ⚡ Pipeline Intelligence is a full-stack Azure DevOps monitoring dashboard that: ✅ Connects to your real Azure DevOps organization via REST API ✅ Detects bottlenecks across all your pipelines automatically ✅ Calculates exactly how much time your team is wasting per month ✅ Uses Gemini AI to generate prioritized fixes with ready-to-paste YAML solutions ✅ JWT-secured, Docker-ready, and fully open-source Tech Stack: → React 18 + Vite + Tailwind CSS → Node.js + Express + Azure DevOps API v7 → Google Gemini 1.5 Flash → JWT Authentication + Docker 𝗪𝗵𝗮𝘁 𝗺𝗮𝗸𝗲𝘀 𝗶𝘁 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁? Most tools show you generic estimates. Pipeline Intelligence reads your actual cluster config, node count, and pipeline structure and gives you recommendations specific to your infrastructure. 🎯 This year, I set myself a personal challenge: Build and open-source a series of production-grade tools exclusively focused on Azure services tools that solve real problems for real DevOps teams. This project represents weeks of research, architecture decisions, and late-night debugging sessions. I'm sharing it with the community because I believe great tooling should be accessible to everyone not locked behind enterprise paywalls. If this resonates with you, I have one simple ask: 👉 A like, a comment, or a share takes 3 seconds but it helps this reach the DevOps engineers who need it most. Your support is what keeps me building. ❤️ GitHub: https://github.com/HlaliMedAmine/pipeline-intelligenceDriving AI‑Powered Healthcare: A Data & AI Webinar and Workshop Series
Across these sessions, you’ll learn how healthcare organizations are using Microsoft Fabric, advanced analytics, and AI to unify fragmented data, modernize analytics, and enable intelligent, scalable solutions, from enterprise reporting to AI‑powered use cases. Whether you’re just getting started or looking to accelerate adoption, these sessions offer practical guidance, real‑world examples, and hands‑on learning to help you build a strong data foundation for AI in healthcare. Date Topic Details Location Registration Link May 6 Webinar: Microsoft Fabric Foundations - A Simple Path to Modern Analytics and AI Discover how Microsoft Fabric consolidates fragmented analytics into a single integrated data platform, making it easier to deliver trusted insights and adopt AI without added complexity. Virtual Register May 13 Webinar: Reduce BI Sprawl, Cut Cost and Build an AI-Ready Analytics Foundation Learn how Power BI enables enterprise BI consolidation, consistent metrics, and secure, scalable analytics that support both operational reporting and emerging AI use cases. Virtual Register May 19-20 In Person Workshop: Driving AI‑Powered Healthcare: Advanced Analytics, AI, and Real‑World Impact Attend this two‑day, in‑person event to learn how healthcare organizations use Microsoft Fabric to unify data, accelerate AI adoption, and deliver measurable clinical and operational value. Day 1 focuses on strategy, architecture, and real‑world healthcare use cases, while Day 2 offers hands‑on workshops to apply those concepts through guided labs and agent‑powered solutions. Chicago Register May 27 Webinar: Unified Data Foundation for AI & Analytics - Leveraging OneLake and Microsoft Fabric This session shows how organizations can simplify fragmented data architectures by using Microsoft Fabric and OneLake as a single, governed foundation for analytics and AI. Virtual Register May 27-28 In Person Workshop: Driving AI‑Powered Healthcare: Advanced Analytics, AI, and Real‑World Impact Attend this two‑day, in‑person event to learn how healthcare organizations use Microsoft Fabric to unify data, accelerate AI adoption, and deliver measurable clinical and operational value. Day 1 focuses on strategy, architecture, and real‑world healthcare use cases, while Day 2 offers hands‑on workshops to apply those concepts through guided labs and agent‑powered solutions. Silicon Valley Register June 2 Webinar: Delivering Personalized Patient Experiences at Scale with Microsoft Fabric and Adobe Learn how healthcare organizations can improve patient engagement by unifying trusted data in Microsoft Fabric and activating it through Adobe’s personalization platform. Virtual Register June 3-4 In Person Workshop: Driving AI‑Powered Healthcare: Advanced Analytics, AI, and Real‑World Impact Attend this two‑day, in‑person event to learn how healthcare organizations use Microsoft Fabric to unify data, accelerate AI adoption, and deliver measurable clinical and operational value. Day 1 focuses on strategy, architecture, and real‑world healthcare use cases, while Day 2 offers hands‑on workshops to apply those concepts through guided labs and agent‑powered solutions. New York Register June 10 Webinar: From Data to Decisions: How AI Data Agents in Microsoft Fabric Redefine Analytics Join us to learn how Fabric Data Agents enable users to interact with enterprise data through AI‑powered, governed agents that understand both data and business context. Virtual Register June 17 Webinar: Building the Intelligent Factory: A Unified Data and AI Approach to Life Science Manufacturing Discover how life science & MedTech manufacturers use Microsoft Fabric to integrate operational, quality, and enterprise data and apply AI‑powered analytics for smarter, faster manufacturing decisions. Virtual Register June 23-24 In Person Workshop: Driving AI‑Powered Healthcare: Advanced Analytics, AI, and Real‑World Impact Attend this two‑day, in‑person event to learn how healthcare organizations use Microsoft Fabric to unify data, accelerate AI adoption, and deliver measurable clinical and operational value. Day 1 focuses on strategy, architecture, and real‑world healthcare use cases, while Day 2 offers hands‑on workshops to apply those concepts through guided labs and agent‑powered solutions. Dallas RegisterAzure IoT Operations 2603 is now available: Powering the next era of Physical AI
Industrial AI is entering a new phase. For years, AI innovation has largely lived in dashboards, analytics, and digital decision support. Today, that intelligence is moving into the real world, onto factory floors, oil fields, and production lines, where AI systems don’t just analyze data, but sense, reason, and act in physical environments. This shift is increasingly described as Physical AI: intelligence that operates reliably where safety, latency, and real‑world constraints matter most. With the Azure IoT Operations 2603 (v1.3.38) release, Microsoft is delivering one of its most significant updates to date, strengthening the platform foundation required to build, deploy, and operate Physical AI systems at industrial scale. Why Physical AI needs a new kind of platform Physical AI systems are fundamentally different from digital‑only AI. They require: Real‑time, low‑latency decision‑making at the edge Tight integration across devices, assets, and OT systems End‑to‑end observability, health, and lifecycle management Secure cloud‑to‑edge control planes with governance built in Industry leaders and researchers increasingly agree that success in Physical AI depends less on isolated models, and more on software platforms that orchestrate data, assets, actions, and AI workloads across the physical world. Azure IoT Operations was built for exactly this challenge. What’s new in Azure IoT Operations 2603 The 2603 release delivers major advancements across data pipelines, connectivity, reliability, and operational control, enabling customers to move faster from experimentation to production‑grade Physical AI. Cloud‑to‑edge management actions Cloud‑to‑edge management actions enable teams to securely execute control and configuration operations on on‑premises assets, such as invoking methods, writing values, or adjusting settings, using Azure Resource Manager and Event Grid–based MQTT messaging. This capability extends the Azure control plane beyond the cloud, allowing intent, policy, and actions to be delivered reliably to physical systems while remaining decoupled from protocol and device specifics. For Physical AI, this closes the loop between perception and action: insights and decisions derived from models can be translated into governed, auditable changes in the physical world, even when assets operate in distributed or intermittently connected environments. Built‑in RBAC, managed identity, and activity logs ensure every action is authorized, traceable, and compliant, preserving safety, accountability, and human oversight as intelligence increasingly moves from observation to autonomous execution at the edge. No‑code dataflow graphs Azure IoT Operations makes it easier to build real‑time data pipelines at the edge without writing custom code. No‑code data flow graphs let teams design visual processing pipelines using built‑in transforms, with improved reliability, validation, and observability. Visual Editor – Build multi-stage data processing systems in the Operations Experience canvas. Drag and connect sources, transforms, and destinations visually. Configure map rules, filter conditions, and window durations inline. Deploy directly from the browser or define in Bicep/YAML for GitOps. Composable Transforms, Any Order – Chain map, filter, branch, concatenate, and window transforms in any sequence. Branch splits messages down parallel paths based on conditions. Concatenate merges them back. Route messages to different MQTT topics based on content. No fixed pipeline shape. Expressions, Enrichment, and Aggregation – Unit conversions, math, string operations, regex, conditionals, and last-known-value lookups, all built into the expression language. Enrich messages with external data from a state store. Aggregate high-frequency sensor data over tumbling time windows to compute averages, min/max, and counts. Open and Extensible – Connect to MQTT, Kafka, and OpenTelemetry (OTel) endpoints with built-in security through Azure Key Vault and managed identities. Need logic beyond what no-code covers? Drop a custom Wasm module (even embed and run ONNX AI ML models) into the middle of any graph alongside built-in transforms. You're never locked into declarative configuration. Together, these capabilities allow teams to move from raw telemetry to actionable signals directly at the edge without custom code or fragile glue logic. Expanded, production‑ready connectivity The MQTT connector enables customers to onboard MQTT devices as assets and route data to downstream workloads using familiar MQTT topics, with the flexibility to support unified namespace (UNS) patterns when desired. By leveraging MQTT’s lightweight publish/subscribe model, teams can simplify connectivity and share data across consumers without tight coupling between producers and applications. This is especially important for Physical AI, where intelligent systems must continuously sense state changes in the physical world and react quickly based on a consistent, authoritative operational context rather than fragmented data pipelines. Alongside MQTT, Azure IoT Operations continues to deliver broad, industrial‑grade connectivity across OPC UA, ONVIF, Media, REST/HTTP, and other connectors, with improved asset discovery, payload transformation, and lifecycle stability, providing the dependable connectivity layer Physical AI systems rely on to understand and respond to real‑world conditions. Unified health and observability Physical AI systems must be trustworthy. Azure IoT Operations 2603 introduces unified health status reporting across brokers, dataflows, assets, connectors, and endpoints, using consistent states and surfaced through both Kubernetes and Azure Resource Manager. This enables operators to see—not guess—when systems are ready to act in the physical world. Optional OPC UA connector deployment Azure IoT Operations 2603 introduces optional OPC UA connector deployment, reinforcing a design goal to keep deployments as streamlined as possible for scenarios that don’t require OPC UA from day one. The OPC UA connector is a discrete, native component of Azure IoT Operations that can be included during initial instance creation or added later as needs evolve, allowing teams to avoid unnecessary footprint and complexity in MQTT‑only or non‑OPC deployments. This reflects the broader architectural principle behind Azure IoT Operations: a platform built for composability and decomposability, where capabilities are assembled based on scenario requirements rather than assumed defaults, supporting faster onboarding, lower resource consumption, and cleaner production rollouts without limiting future expansion. Broker reliability and platform hardening The 2603 release significantly improves broker reliability through graceful upgrades, idempotent replication, persistence correctness, and backpressure isolation—capabilities essential for always‑on Physical AI systems operating in production environments. Physical AI in action: What customers are achieving today Azure IoT Operations is already powering real‑world Physical AI across industries, helping customers move beyond pilots to repeatable, scalable execution. Procter & Gamble Consumer goods leader P&G continually looks for ways to drive manufacturing efficiency and improve overall equipment effectiveness—a KPI encompassing availability, performance, and quality that’s tracked in P&G facilities around the world. P&G deployed Azure IoT Operations, enabled by Azure Arc, to capture real-time data from equipment at the edge, analyze it in the cloud, and deploy predictive models that enhance manufacturing efficiency and reduce unplanned downtime. Using Azure IoT Operations and Azure Arc, P&G is extrapolating insights and correlating them across plants to improve efficiency, reduce loss, and continue to drive global manufacturing technology forward. More info. Husqvarna Husqvarna Group faced increasing pressure to modernize its fragmented global infrastructure, gain real-time operational insights, and improve efficiency across its supply chain to stay competitive in a rapidly evolving digital and manufacturing landscape. Husqvarna Group implemented a suite of Microsoft Azure solutions—including Azure Arc, Azure IoT Operations, and Azure OpenAI—to unify cloud and on-premises systems, enable real-time data insights, and drive innovation across global manufacturing operations. With Azure, Husqvarna Group achieved 98% faster data deployment and 50% lower infrastructure imaging costs, while improving productivity, reducing downtime, and enabling real-time insights across a growing network of smart, connected factories. More info. Chevron With its Facilities and Operations of the Future initiative, Chevron is reimagining the monitoring of its physical operations to support remote and autonomous operations through enhanced capabilities and real-time access to data. Chevron adopted Microsoft Azure IoT Operations, enabled by Azure Arc, to manage and analyze data locally at remote facilities at the edge, while still maintaining a centralized, cloud-based management plane. Real-time insights enhance worker safety while lowering operational costs, empowering staff to focus on complex, higher-value tasks rather than routine inspections. More info. A platform purpose‑built for Physical AI Across manufacturing, energy, and infrastructure, the message is clear: the next wave of AI value will be created where digital intelligence meets the physical world. Azure IoT Operations 2603 strengthens Microsoft’s commitment to that future—providing the secure, observable, cloud‑connected edge platform required to build Physical AI systems that are not only intelligent, but dependable. Get started To explore the full Azure IoT Operations 2603 release, review the public documentation and release notes, and start building Physical AI solutions that operate and scale confidently in the real world.Join us at Microsoft Azure Infra Summit 2026 for deep technical Azure infrastructure content
Microsoft Azure Infra Summit 2026 is a free, engineering-led virtual event created for IT professionals, platform engineers, SREs, and infrastructure teams who want to go deeper on how Azure really works in production. It will take place May 19-21, 2026. This event is built for the people responsible for keeping systems running, making sound architecture decisions, and dealing with the operational realities that show up long after deployment day. Over the past year, one message has come through clearly from the community: infrastructure and operations audiences want more in-depth technical content. They want fewer surface-level overviews and more practical guidance from the engineers and experts who build, run, and support these systems every day. That is exactly what Azure Infra Summit aims to deliver. All content is created AND delivered by engineering, targeting folks working with Azure infrastructure and operating production environments. Who is this for: IT professionals, platform engineers, SREs, and infrastructure teams When: May 19-21, 2026 - 8:00 AM–1:00 PM Pacific Time, all 3 days Where: Online Virtual Cost: Free Level: Most sessions are advanced (L300-400). Register here: https://aka.ms/MAIS-Reg Built for the people who run workloads on Azure Azure Infra Summit is for the people who do more than deploy to Azure. It is for the people who run it. If your day involves uptime, patching, governance, monitoring, reliability, networking, identity, storage, or hybrid infrastructure, this event is for you. Whether you are an IT professional managing enterprise environments, a platform engineer designing landing zones, an Azure administrator, an architect, or an SRE responsible for resilience and operational excellence, you will find content built with your needs in mind. We are intentionally shaping this event around peer-to-peer technical learning. That means engineering-led sessions, practical examples, and candid discussion about architecture, failure modes, operational tradeoffs, and what breaks in production. The promise here is straightforward: less fluff, more infrastructure. What to expect Azure Infra Summit will feature deep technical content in the 300 to 400 level range, with sessions designed by engineering to help you build, operate, and optimize Azure infrastructure more effectively. The event will include a mix of live and pre-recorded sessions and live Q&A. Throughout the three days, we will dig into topics such as: Hybrid operations and management Networking at scale Storage, backup, and disaster recovery Observability, SLOs, and day-2 operations Confidential compute Architecture, automation, governance, and optimization in Azure Core environments And more… The goal is simple: to give you practical guidance you can take back to your environment and apply right away. We want attendees to leave with stronger mental models, a better understanding of how Azure behaves in the real world, and clearer patterns for designing and operating infrastructure with confidence. Why this event matters Infrastructure decisions have a long tail. The choices we make around architecture, operations, governance, and resilience show up later in the form of performance issues, outages, cost, complexity, and recovery challenges. That is why deep technical learning matters, and why events like this matter. Join us I hope you will join us for Microsoft Azure Infra Summit 2026, happening May 19-21, 2026. If you care about how Azure infrastructure behaves in the real world, and you want practical, engineering-led guidance on how to build, operate, and optimize it, this event was built for you. Register here: https://aka.ms/MAIS-Reg Cheers! Pierre Roman
