Resource Guide: Making Physical AI Practical for Real‑World Industrial Operations
Microsoft’s adaptive cloud approach enables organizations to turn operational technology (OT) data into intelligent actions, autonomously, without requiring everything to live in the cloud by unifying cloud-to-edge management plane, data plane, and intelligence platform. At the center of this approach are key foundational technologies: Key Purpose Offering Direct-to-cloud device management + telemetry ingestion Azure IoT Hub Industrial connectivity + edge data plane Azure IoT Operations Unified analytics + real-time intelligence Microsoft Fabric On-device AI inferencing runtime Foundry Local Microsoft Azure IoT Gartner winner: Microsoft named a Leader in the 2025 Gartner® Magic Quadrant™ for Global Industrial IoT Platforms This blog walks through where to get started with each: 1. Manage Cloud-Connected Devices and Telemetry with Azure IoT Hub Azure IoT Hub is a fully managed cloud service that enables secure bidirectional communication, device-to-cloud telemetry ingestion, cloud-to-device command execution, per-device authentication, remote management and more. Telemetry from IoT Hub can also be routed downstream into analytics platforms like Microsoft Fabric for visualization or AI modeling. Recommended Usage: Devices that utilize IoT Hub are distributed, stand-alone devices with fixed-functions. These devices typically do not require cloud-managed containerized workloads or cloud-managed proximal industrial protocol connectivity. Examples of appropriate device-to-cloud IoT Hub endpoint devices include water monitoring stations, vehicle telematics, distributed fluid level sensors, etc. Resources Current in-market services overview: IoT Hub: What is Azure IoT Hub? - Azure IoT Hub DPS: Overview of Azure IoT Hub Device Provisioning Service - Azure IoT Hub Device Provisioning Service ADU: Introduction to Device Update for Azure IoT Hub Building scalable solutions with Azure IoT platform: Best practices for large-scale IoT deployments - Azure IoT Hub Device Provisioning Service Scale Out an Azure IoT Hub-based Solution to Support Millions of Devices - Azure Architecture Center Azure IoT Hub scaling Try out our preview of new IoT Hub capabilities (integration with Azure Device Registry and Certificate Management) Learn more about these capabilities on our blog post: Azure IoT Hub + Azure Device Registry (Preview Refresh): Device Trust and Management at Fleet Scale… Integration with Azure Device Registry (preview): Integration with Azure Device Registry (preview) - Azure IoT Hub Microsoft-backed X.509 certificate management (preview): What is Microsoft-backed X.509 Certificate Management (Preview)? - Azure IoT Hub How to start with the preview: Deploy IoT Hub with ADR integration and certificate management (Preview) - Azure IoT Hub 2. Connect Industrial Assets with Azure IoT Operations Azure IoT Operations provides a unified data plane for the edge that runs on Azure Arc–enabled Kubernetes clusters and supports open industrial standards. It allows organizations to connect and capture equipment telemetry, normalize OT data locally, route hot-path signals to real-time analytics, securely manage layered industrial networks, and more. Edge‑processed data can then be sent upstream to Microsoft Fabric for AI‑driven analysis. Recommended Usage: Azure IoT Operations is intended to be the data plane for an adaptive cloud deployment extending the management, data, and AI capabilities of the Microsoft cloud to an on-prem device. This device binds to these cloud planes providing a platform for local data processing and intermittent connectivity. The target for these devices range from a small-gateway-style PC to a full data center. Azure IoT Operations endpoints enable cloud-managed containerized workloads and cloud-managed proximal industrial protocol connectivity. Examples of appropriate adaptive cloud and Azure IoT Operations endpoints include, on-robot computers, industrial machine controllers, retail store sensor/vision processing, and top-of-factory site infrastructure for line of business applications. Resources Azure IoT Operations Overview Azure IoT Operations Documentation Hub Quickstart: explore-iot-operations/quickstart at main · Azure-Samples/explore-iot-operations Open-source framework for scaling robotics from simulation to production on Azure + NVIDIA: microsoft/physical-ai-toolchain How we built the demo: explore-iot-operations/quickstart at main · Azure-Samples/explore-iot-operations Edge-AI: microsoft/edge-ai: Production-ready Infrastructure as Code, applications, pluggable components, and… Latest Announcements & Blogs Making Physical AI Practical for Real-World Industrial Operations: Part 1 | Microsoft Community Hub Making Physical AI Practical for Real-World Industrial Operations: Part 2 | Microsoft Community Hub Unlock Industrial Intelligence | Microsoft Hannover Messe 2026 From pilots to production: How Microsoft and partners are accelerating intelligent operations 3. Advanced Analytics with Microsoft Fabric Microsoft Fabric delivers a unified, end‑to‑end analytics platform that transforms streaming OT telemetry into real‑time insights and live dashboards. Fabric Operations Agents monitor industrial signals to recommend targeted actions, while Fabric IQ provides a shared semantic foundation that enables AI agents to reason over enterprise data with business context. Together, Fabric turns live industrial data into AI‑powered operational intelligence. Get Started Get Started with Microsoft Fabric Learning Path Fabric Real-Time Intelligence documentation - Microsoft Fabric | Microsoft Learn Create and Configure Operations Agents - Microsoft Fabric | Microsoft Learn Fabric IQ documentation - Microsoft Fabric | Microsoft Learn 4.Run AI Models On‑Device with Foundry Local Foundry Local extends on‑device AI to Arc‑enabled Kubernetes edge clusters, providing a Microsoft‑validated inferencing layer for running AI models in industrial, disconnected or sovereign environments. Get Started Foundry Local on Azure Local Documentation - link Participate in Foundry Local on Azure Local preview form - link Foundry Local on Azure Local: HELM deployment Demo - link Customer Stories Chevron: Chevron plans facilities of the future with Azure IoT Operations Husqvarna: Husqvarna Group Boosts Operational Efficiency with Azure Adaptive Cloud Ecopetrol: Azure IoT Operations and Azure IoT for energy help Ecopetrol optimize energy distribution while lowering operational costs P&G: Procter & Gamble cuts model deployment time up to 90% with Azure IoT Operations Toyota: Toyota Industries innovates its paint shop processes with Azure industrial AI and Azure IoT HubAdvancing Firmware Security: Fleet Visibility and New Capabilities in Firmware Analysis
When we announced general availability of firmware analysis enabled by Azure Arc last October, our goal was clear: help organizations gain deep visibility into the security of the firmware that powers their IoT, OT, and network devices. Since then, adoption has continued to grow as customers use firmware analysis to uncover vulnerabilities, inventory software components, and secure their software supply chain. Leading into the Hannover Messe (HMI) 2026 conference, we’re excited to share the next wave of firmware analysis capabilities, delivering enhancements that help customers connect firmware risk to real-world fleet impact, prioritize vulnerabilities more effectively, scale to larger and more complex firmware images, and expand security analysis for UEFI-based platforms. These updates are driven directly by customer feedback and by the rapidly evolving threat landscape facing embedded and edge devices. Connecting Firmware Risk to Your Deployed Fleet with Azure Device Registry (Preview) Securing connected devices doesn’t stop at identifying vulnerabilities in firmware—it requires understanding where those vulnerabilities exist in your deployed fleet and which devices are affected. We’re excited to announce a new preview integration between firmware analysis enabled by Azure Arc and Azure Device Registry, bringing fleet-level visibility of IoT and OT devices directly into the firmware analysis experience. This helps customers quickly understand how many devices and assets are running a given firmware image, and which ones may be exposed to known security issues. From firmware insights to fleet impact Firmware analysis helps customers uncover security risks hidden deep inside the firmware running IoT, OT, and network devices—risks such as known CVEs, outdated open-source components, weak cryptography, and insecure configurations. Until now, these insights were primarily scoped to the firmware image itself. With this new preview integration, firmware analysis now connects directly to Azure Device Registry, allowing customers to: See how many devices from IoT Hub integration with ADR (preview) and assets from Azure IoT Operations are associated with a specific analyzed firmware image Understand the real-world blast radius of vulnerabilities discovered in firmware Quickly identify which devices may require patching, mitigation, or isolation This preview bridges an important gap between security analysis and operational decision-making. What’s included in this preview With this release, we’re introducing new fleet-level context directly into the firmware analysis experience: A new Devices + Assets count column in the firmware analysis workspace showing how many Azure Device Registry devices and assets are running each analyzed firmware image A click-through experience that lets users view the list of affected devices and assets in Azure Device Registry Visibility spanning both: Devices connected via IoT Hub Assets managed through Azure IoT Operations This information is derived by correlating firmware metadata with device and asset inventory in Azure Device Registry, giving customers immediate insight into deployment exposure. Key use cases Identify vulnerable devices at scale: When critical CVEs are discovered in a firmware image, customers can immediately see how many deployed devices are impacted—without manually correlating spreadsheets, tools, or inventories. Prioritize remediation actions: With fleet visibility, teams can decide whether to patch devices, temporarily isolate affected devices from the network, or disable devices that pose unacceptable risk. Bridge security and operations teams: Security teams gain clear insight into where vulnerabilities exist, while operations teams can quickly act on specific devices and assets—all within the Azure portal. This integration is especially valuable in environments where downtime, safety, or regulatory compliance matter—such as manufacturing, energy, telecommunications, and critical infrastructure. Prioritizing Vulnerabilities with Enhanced CVE Metadata (Preview) The number of publicly disclosed vulnerabilities continues to rise year over year, making it increasingly difficult for security teams to determine which CVEs truly require urgent action. Simply knowing that a vulnerability exists is no longer enough—teams need context to prioritize remediation efforts. With this release, firmware analysis now provides richer metadata for each discovered CVE, helping customers focus on vulnerabilities that pose the greatest real-world risk. New CVE metadata includes: CISA Known Exploited Vulnerabilities (KEV) status – Indicates whether a CVE is listed in the CISA KEV catalog, signaling that the vulnerability is actively exploited in the wild. EPSS score (Exploit Prediction Scoring System) – A data-driven probability score that estimates the likelihood of a vulnerability being exploited in the next 30 days, complementing traditional severity metrics by focusing on exploitation likelihood rather than impact alone. Additional vulnerability context, including CVSS vectors and base scores, CWE classifications, and expanded metadata to support filtering and analysis. Together, these enhancements make it easier to triage findings, align remediation with risk, and communicate priorities across security, engineering, and product teams. Faster Performance for Large and Complex Firmware Images As firmware analysis adoption has grown, we’ve seen customers analyze increasingly large and complex firmware images—particularly in domains like networking equipment, where a single image can generate thousands of findings. To support these scenarios, we’ve made architectural enhancements to the service that significantly improve performance when working with large result sets. Key improvements include: Up to 90% reduction in load times of analysis results, especially for firmware images producing 10,000+ findings More responsive filtering and exploration of results These changes ensure that firmware analysis remains fast and usable at scale, even for complex network and infrastructure firmware images. Expanding UEFI Firmware Analysis (Preview) Modern devices increasingly rely on UEFI firmware as a foundational security boundary. In this release, we’re expanding our UEFI analysis capabilities to provide deeper visibility into UEFI executables and components. New UEFI-focused capabilities include: Detection of OpenSSL libraries and related CVEs within UEFI firmware Binary hardening analysis for UEFI executables, including detection of proper configuration of Data Execution Prevention (DEP) memory protection Continued support for discovering cryptographic material in UEFI images, including embedded certificates and keys This preview allows customers to evaluate the new capabilities, provide feedback, and help shape future enhancements in this area. Note: UEFI SBOM and binary analysis features are currently in preview and intended for evaluation and feedback. Bulk Export of Analysis Results for Supply Chain Collaboration We also recently released a highly requested feature that makes it easier to share firmware analysis results with partners and suppliers. Customers can now: Bulk download analysis results across one or more firmware images Export results as CSV files packaged into a ZIP archive This capability simplifies workflows such as sharing findings with device manufacturers or firmware suppliers, integrating results into downstream analysis or reporting pipelines, and supporting software supply chain security and compliance processes. Looking Ahead We’re excited about the progress we’ve made with this release and what it means for customers securing IoT, OT, and network devices. From connecting firmware risk to fleet-level impact with Azure Device Registry, to richer vulnerability prioritization, improved scalability, and deeper UEFI analysis—these enhancements reinforce firmware analysis as a critical tool for addressing some of the most challenging blind spots in modern infrastructure security. Firmware security is foundational to trustworthy systems—especially as edge devices continue to play a central role in industrial operations, networking, and data collection. If you’re already using firmware analysis and Azure Device Registry, the ADR integration preview will appear directly within the firmware analysis experience as it rolls out. We look forward to your feedback as we continue building secure, observable, and manageable digital operations with Azure. As always, we value your feedback, so please let us know what you think.Azure 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.Microsoft Industrial AI Partner Guide: Choosing the Right Data Expertise for Every Stage
As organizations scale Industrial AI, the challenge shifts from technology selection to deciding who should lead which part of the journey -- and when. Which partners should establish secure connectivity? Who enables production grade, AI ready industrial data? When do systems integrators step in to scale globally? This Partner Guide helps customers navigate these decisions with clarity and confidence: Identify which partners align to their current digital transformation and Industrial AI scenarios leveraging Azure IoT and Azure IoT Operations Confidently combine partners over time as they evolve from connectivity to intelligence to autonomous operations This guide focuses on the Industrial AI data plane – the partners and capabilities that extract, contextualize, and operationalize industrial data so it can reliably power AI at scale. It does not attempt to catalog or prescribe end‑to‑end Industrial AI applications or cloud‑hosted AI solutions. Instead, it helps customers understand how industrial partners create the trusted, contextualized data foundation upon which AI solutions can be built. Common Customer Journey Steps 1. Modernize Connectivity & Edge Foundations The industrial transformation journey starts with securely accessing operational data without touching deterministic control loops. Customers connect automation systems to a scalable, standards-based data foundation that modernizes operations while preserving safety, uptime and control. Outcomes customers realize Standardized OT data access across plants and sites Faster onboarding of legacy and new assets Clear OT–IT boundaries that protect safety and uptime Partner strengths at this stage Industrial hardware and edge infrastructure providers Protocol translation and OT connectivity Automation and edge platforms aligned with Azure IoT Operations 2. Accelerate Insights with Industrial AI With a consistent edge-to-cloud data plane in place, customers move beyond dashboards to repeatable, production-grade Industrial AI use cases. Customers rely on expert partners to turn standardized operational data into AI‑ready signals that can be consumed by analytics and AI solutions at scale across assets, lines, and sites. Outcomes customers realize Improved Operational efficiency and performance Adaptive facilities and production quality intelligence Energy, safety, and defect detection at scale Partner strengths at this stage Industrial data services that contextualize and standardize OT signals for AI consumption Domain-specific acceleration for common Industrial AI scenarios Data pipelines integrated with Azure IoT Operations and Microsoft Fabric 3. Prepare for Autonomous Operations As organizations advance toward closed‑loop optimization, the focus shifts to safe, scalable autonomy. Customers depend on partners to align data, infrastructure, and operational interfaces, while ensuring ongoing monitoring, governance, and lifecycle management across the full operational estate. Outcomes customers realize Proven reference architectures deployed across plants AI‑ready data foundations that adapt as operations scale Coordinated interaction between OT systems, AI models, and cloud intelligence Partner strengths at this stage Industrial automation leadership and control system expertise Edge infrastructure optimized and ready for Industrial AI scale Systems integrators enabling end‑to‑end implementation and repeatability Data Intelligence Plane of Industrial AI - Partner Matrix This matrix highlights which partners have the deepest expertise in accessing, contextualizing, and operationalizing industrial data so it can reliably power AI at scale. The matrix is not a catalog of end‑to‑end Industrial AI applications; it shows how specialized partners contribute data, infrastructure, and integration capabilities on a shared Azure foundation as organizations progress from connectivity to insight to autonomous operations. How to use this matrix: Start with your scenario → identify primary partner types → layer complementary partners as you scale. Partner Type Adaptive Cloud Primary Solution Example Scenarios Geography Advantech Industrial Hardware, Industrial Connectivity LoRaWAN gateway integration + Azure IoT Operations Industrial edge platforms with built in connectivity, industrial compute, LoRaWAN, sensor networks Global Accenture GSI Industrial AI, Digital Transformation, Modernization OEE, predictive maintenance, real-time defect detection, optimize supply chains, intelligent automation and robotics, energy efficiency Global Avanade GSI Factory Agents and Analytics based on Manufacturing Data Solutions Yield / Quality optimization, OEE, Agentic Root Cause Analysis and process optimization; Unified ISA-95 Manufacturing Data estate on MS Fabric Global Capgemini GSI The new AI imperative in manufacturing OEE, maintenance, defect detection, energy, robotics Global DXC GSI Intelligent Boost AI and IoT Analytics Platform 5G Industrial Connectivity, Defect detection, OEE, safety, energy monitoring Global Innominds SI Intelligent Connected Edge Platform Predictive maintenance, AI on edge, asset tracking North America, EMEA Litmus Automation Industrial Connectivity, Industrial Data Ops Litmus Edge + Azure IoT Operations Edge Data, Smart manufacturing, IIoT deployments at scale Global, North America Mesh Systems GSI & ISV Azure IoT & Azure IoT Operations implementation services and solutions (including Azure IoT Operations-aligned connector patterns) Device connectivity and management, data platforms, visualization, AI agents, and security North America, EMEA Nortal GSI Data-driven Industry Solutions IT/OT Connectivity, Unified Namespace, Digital Twins, Optimization, Edge, Industrial Data, Real‑Time Analytics & AI EMEA, North America & LATAM NVIDIA Technology Partner Accelerated AI Infrastructure; Open libraries, models, frameworks, and blueprints for AI development and deployment. Cross industry digitalization and AI development and deployment: Generative AI, Agentic AI, Physical AI, Robotics Global Oracle ISV Oracle Fusion Cloud SCM + Azure IoT Operations Real-time manufacturing Intelligence, AI powered insights, and automated production workflows Global Rockwell Automation Industrial Automation FactoryTalk Optix + Azure IoT Operations Factory modernization, visualization, edge orchestration, DataOps with connectivity context at scale, AI ops and services, physical equipment, MES Global Schneider Electric Industrial Automation Industrial Edge Physical equipment, Device modernization, energy, grid Global Siemens Industrial Automation & Software Industrial Edge + Azure IoT Operations reference architecture Industrial edge infrastructure at scale, OT/IT convergence, DataOps, Industrial AI suite, virtualized automation. Global Sight Machine ISV Integrated Industrial AI Stack Industrial AI, bottling, process optimization Global Softing Industrial Industrial Connectivity edgeConnector + Azure IoT Operations OT connectivity, multi-vendor PLC- and machine data integration, OPC UA information model deployment EMEA, Global TCS GSI Sensor to cloud intelligence Operations optimization, healthcare digital twin experiences, supply chain monitoring Global This Ecosystem Model enables Industrial AI solutions to scale through clear roles, respected boundaries and composable systems: Control systems continue to be driven by automation leaders Safety‑critical, deterministic control stays with industrial automation partners who manage real‑time operations and plant safety. Customers modernize analytics and AI while preserving uptime, reliability, and operational integrity. Data, AI, and analytics scale independently A consistent edge to cloud data plane supports cloud scale analytics and AI, accelerating insight delivery without entangling control systems or slowing operational change. This separation allows customers and software providers to build AI solutions on top of a stable, industrial‑grade data foundation without redefining control system responsibilities. Specialized partners align solutions across the estate Partners contribute focused expertise across connectivity, analytics, security, and operations, assembling solutions that reduce integration risk, shorten deployment cycles, and speed time to value across the operational estate. From vision to production Industrial AI at scale depends on turning operational data into trusted, contextualized intelligence safely, repeatably, and across the enterprise. This guide shows how industrial partners, aligned on a shared Azure foundation, create the data plane that enables AI solutions to succeed in production. When data is ready, intelligence scales. Call to action: Use this guide to identify the partners and capabilities that best align to your current Industrial AI needs and take the next step toward production‑ready outcomes on Azure.
Bridging the Digital and Physical Worlds with Azure IoT Hub and Azure IoT Operations
Operational excellence starts with people. Empowering those people with the most up to date insights and recommendations requires bridging the gap between the physical and digital worlds to generate the best possible outcomes for real time decision making. Creating this bridge transforms data into insights, insights into intelligent actions, and actions into real-world results. Digital Operations, integrated with AI insights, help make this possible by combining data from connected assets across a variety of physical locations and deployment topologies, and transforming that data into insights and decisions that scale using AI and Analytics. At Microsoft Ignite, we’re extending this vision with new Azure IoT Hub and Azure IoT Operations capabilities to manage connected assets at scale, unify digital operations, and realize AI-enabled outcomes across your enterprise. Connected Operations in Action Azure IoT Hub and Azure IoT Operations form the backbone of connected operations, where every asset, sensor, and system contributes to a continuous loop of intelligence by moving data to Microsoft Fabric for real-time analytics, and for use with AI agents. This pattern applies to nearly every sector of the economy. In manufacturing, these capabilities allow production engineers to predict and avoid equipment failures by analyzing vibration and temperature data at the edge before costly downtime occurs. In energy and utilities, distributed sensors can provide data to control points that help balance load, optimize grid efficiency, and ensure safe operations even in remote areas. In transportation and logistics, connected fleets use edge AI models to detect safety risks in real time, while cloud-based analytics optimize routing and fuel efficiency across entire regions. Across industries, this edge-to-cloud collaboration enables the ability for intelligent systems to sense, reason, and act in the physical world with speed, safety, and precision. From Data to Intelligent Action Organizations today must capture and act on data from both geographically dispersed and tightly collocated assets. That data needs to be processed close to where it’s generated, at the edge, to enable real-time decision-making, reduce latency, and enhance security. At the same time, the cloud remains vital for contextualizing operational data with enterprise systems, training AI models, and managing a consistent identity and security framework across all assets. AI models trained in the cloud can then be deployed back to the edge, where they act on events in real time. Operators can work with AI agents to reason over this data whether it’s structured or unstructured, organized in silos, or contained in free-text fields, to provide results to a mixed team of human and AI operational assets. We have a portfolio of products uniquely designed to make this continuum, from edge to cloud, more intelligent, secure, and repeatable. Together with our partners, we help bridge Operational Technology (OT) with Information Technology (IT) to deliver better business outcomes. New at Ignite: Accelerating Digital Operations We’re excited to share our latest set of investments at Ignite across our portfolio of services. A few key announcements: Azure IoT Hub New Features (Preview): Simplifying Secure Connectivity at Scale Azure IoT Hub empowers organizations to securely and reliably manage connected assets across the globe, providing real-time visibility and control over diverse operations. With proven scalability, broad device support, and robust management tools, IoT Hub delivers a unified platform for developing and operating IoT solutions. As customers evolve, Azure IoT Hub continues to advance, deepening its integration with the Azure ecosystem and enabling AI-driven, connected operations for the next generation of applications. The next generation of Azure IoT Hub investments makes it easier and more secure than ever to connect and manage distributed assets. At Ignite, we’re previewing: New certificate management capabilities that simplify device onboarding and lifecycle management. Integration with Azure Device Registry (ADR) that brings all devices into a common control plane, enabling unified identity, security, and policy management. ADR enhancements that make it easier to register, classify, and monitor assets, paving the way for consistent governance and operational insight across millions of devices. This deeper Azure integration with ADR standardizes operations, simplifies oversight of edge portfolios including IoT devices, and brings the full power of Azure’s management ecosystem to IoT and Digital Operations workloads. Azure IoT Operations New Features (GA): The Foundation for AI in the Physical World Azure IoT Operations is more than an edge-to-cloud data plane, it’s the foundation for achieving AI in the physical world, enabling intelligent operational systems that can perceive, reason, and act to drive new operational efficiencies. Built on Arc-enabled Kubernetes, Azure IoT Operations unifies operational and business data across distributed environments, eliminating silos and providing a repeatable, scalable foundation for autonomous, adaptive operations. By extending familiar Azure management concepts to physical sites, Azure IoT Operations creates an AI-ready infrastructure that supports autonomous, adaptive operations at scale. Our latest GA release of Azure IoT Operations introduced major enhancements: Wasm-powered data graphs deliver fast, modular analytics helping businesses make near real-time decisions at the edge. Expanded connectors now include OPC UA, ONVIF, REST/HTTP, Server-Sent Events (SSE), and direct MQTT for richer industrial and IT integrations. OpenTelemetry (OTel) endpoint support enables seamless telemetry pipelines and observability. Asset health monitoring to provide unprecedented visibility and control. These capabilities help bridge Information Technology, Operational Technology, and data domains, empowering customers to discover, collect, process, and send data using open standards while laying the groundwork for self-optimizing environments where AI agents and human supervisors collaborate seamlessly. Integration with Fabric IQ and Digital Twin Builder To fully unlock the value of connected data, organizations need to contextualize it, linking operational signals to business meaning. Fabric IQ, a new offering announced at Ignite, and Digital Twin Builder in Fabric make this possible, transforming raw telemetry into AI-ready context. This integration allows companies to model complex systems, run simulations, and create intelligent feedback loops across manufacturing, logistics, and energy environments. Edge AI: Real-Time Intelligence in the Physical World Azure’s AI capabilities for edge environments bring intelligence closer to where it matters most. And, because these services are Arc-enabled, organizations can develop, manage and scale AI workloads across diverse environments using consistent tooling. Today, we are announcing updates to two of our key services that enable AI at the edge: Live Video Analysis features (Public Preview) in Azure AI Video Indexer enabled by Arc: delivers real-time agentic video intelligence to improve safety, quality, and operations. Edge RAG (Retrieval Augmented Generation) Public Preview Refresh enables local generative AI reasoning with contextual awareness - empowering AI agents to act within industrial constraints securely and efficiently. These innovations accelerate time to insight and help organizations deploy AI where milliseconds matter. Partner Innovation: Scaling Real Business Value Last year, we showcased the breadth of Azure IoT Operations’ industrial ecosystem. This year, we’re celebrating how partners are integrating, co-innovating, and scaling real customer outcomes. Our partners are packaging repeatable, scalable solutions that connect operational data to enterprise systems—enabling AI-driven insights and automation across sites, regions, and industries. At this year’s Ignite, we’re highlighting some great new partner innovations: NVIDIA is working with Microsoft to enable factory digital twins using the OpenUSD standard Siemens is enabling adaptive production through AI- and digital-twin-powered solutions supported by the integration of Siemens Industrial Edge with Azure IoT Operations Litmus Edge integrates with Azure IoT Operations via the Akri framework to automatically discover industrial devices, enable secure data flows, and support Arc-enabled deployment. Rockwell Automation is streamlining edge-to-cloud integration with its FactoryTalk Optix platform by delivering contextualized, AI-ready data seamlessly within Microsoft Azure IoT Operations architectures. Sight Machine is driving advanced analytics for quality and efficiency across multi-site operations. Through initiatives like Akri, Co-Innovate, and Co-Sell Readiness, our ecosystem is developing managed applications, packaged solutions, and marketplace offerings that accelerate deployment and unlock new revenue streams. These collaborations show how Azure IoT Operations is not just a platform, but a growth engine for industrial transformation. The Path Forward With these advancements, we’re helping organizations bring AI to the physical world by turning data into intelligence and intelligence into action. Customers like Chevron and Husqvarna are scaling beyond initial pilots, expanding their deployments from single-site to multi-site rollouts, unlocking new use cases from predictive maintenance to worker safety, and proving how adaptive cloud architectures deliver measurable impact across global operations. By connecting assets, empowering partners, and delivering open, scalable platform solutions, Microsoft is helping industries achieve resilient, adaptive operations that drive measurable business value. The digital and physical worlds are coming together with solutions that are secure, observable, AI-ready, and built to scale from a single site to global operations. Together, we’re creating a smarter, more connected future. Learn More Learn more about Azure IoT Hub and Azure IoT Operations here: Azure IoT – Internet of Things Platform | Microsoft Azure Learn more about new IoT Hub public preview features here: Azure IoT Hub documentation Discover Partner Solutions: Learn how Litmus and Sight Machine are advancing industrial analytics and integration with Azure IoT Operations. Explore Rockwell Automation and Siemens for more on adaptive cloud architectures and shop floor intelligence. Going to Ignite? If you’re at Ignite this week, you can learn more about how Microsoft enables Industrial Transformation at the following sessions: The New Industrial Frontier Reshaping Digital Operations with AI from Cloud and Edge Or come visit us on the show floor at the Azure Arc Expert Meet Up Focus Area in the Cloud and AI Platforms neighborhoodSolving the Data Challenge for Manufacturers with Sight Machine & Azure IoT Operations
Delivering Industrial AI: From Data to Results As manufacturers accelerate their digital transformation, the ability to unify and leverage operational data is the difference between incremental improvement and competitive advantage. Today, we’re launching a joint solution with Sight Machine, purpose-built to solve the OT data challenge and deliver the full Industrial AI stack in weeks, not months: Sight Machine and Microsoft Integrated Industrial AI Stack on Azure This offering is proven in the field, already driving measurable productivity gains for customers in automotive, food, and other sectors with rapid POC cycles and commercial-scale deployments. By integrating Sight Machine’s industrial AI platform with Azure IoT Operations and Microsoft Fabric, we standardize and contextualize machine data at scale, enabling analytics, automation, and actionable insights across the enterprise. What Sets This Solution Apart Fast Deployment: Get the full Industrial AI stack up and running in weeks, not months. End-to-End Integration: Sight Machine’s industrial AI platform works seamlessly with Azure IoT Operations and Microsoft Fabric, standardizing OT data for enterprise-wide use. Real Results: Customers in automotive, food, and other industries are already seeing measurable productivity gains and faster decision cycles. Scalable & Secure: Built on Azure’s adaptive cloud and zero-trust security, with SI partners ready to support commercial scale. Delivering a unified Industrial AI stack Today marks a pivotal moment for manufacturers: the launch of a fully integrated Industrial AI solution, jointly delivered by Microsoft and Sight Machine. This offering brings together the entire Industrial AI stack spanning cloud, edge, and on-premises, enabling organizations to unlock transformative business value. The integrated solution enables customers to transform data into business value by seamlessly contextualizing and moving data from the Edge using Sight Machine and Azure IoT Operations to Microsoft Fabric. Within Microsoft Fabric, the data can be further contextualized and enriched to support AI agents and can be extended to visualize 3D digital twins using NVIDIA Omniverse. The integrated solution has following key components: Azure IoT Operations Streams secure, real-time telemetry from industrial assets to the cloud, enabling visibility and control across edge and enterprise environments. Microsoft Fabric Provides a single analytics and governance platform, merging IT and OT data for enterprise-wide insights. Sight Machine Industrial AI Platform Refines data into “gold-level” quality, fully contextualized and structured for AI, predictive maintenance, and process optimization. M365 Copilot & Agentic Intelligence Surfaces actionable insights directly in familiar tools like Teams and Excel, empowering operators and managers to make informed decisions instantly. NVIDIA Omniverse Integration Extends capabilities into immersive 3D digital twins and physics-based simulations, enabling manufacturers to visualize live operations and test changes virtually before implementing them. Customer Impact Manufacturing is the world’s largest sector, generating twice as much data as any other industry. Yet, the complexity and fragmentation of OT (Operational Technology) data have long limited the adoption of AI at scale. Sight Machine solves this challenge by integrating with every level of the Azure stack, structuring raw OT data into high-quality, contextualized “gold” data, ready for advanced analytics and AI. This integrated offering removes barriers to AI adoption. Manufacturers can connect assets, contextualize data, and deliver actionable insights directly to teams, whether in Teams, Excel, or immersive 3D digital twins. The result: higher productivity, smarter operations, and continuous improvement. Take the Next Step Ready to accelerate your digital transformation? Explore the Sight Machine + Azure IoT Operations solution in the Marketplace. Start your journey to smarter manufacturing today: Sight Machine on AzureMicrosoft and Rockwell Automation: Transforming Industrial AI Together
Unlocking the Future of Connected Operations In today’s rapidly evolving industrial landscape, manufacturers face mounting pressure to increase agility, optimize operations, and harness data-driven insights across every level of production. The collaboration between Microsoft and Rockwell Automation represents a pivotal step toward achieving these goals. By combining Rockwell’s deep expertise in operational technology (OT) with Microsoft’s adaptive cloud approach, this partnership bridges the gap between OT and IT, creating a unified, intelligent ecosystem that empowers manufacturers to innovate at scale. Together, we enable seamless connectivity, advanced analytics, and AI-driven optimization across the factory floor from edge and cloud environments. Connected Operations powered by Microsoft and Rockwell Rockwell Automation’s FactoryTalk Optix and Microsoft’s Azure IoT Operations together deliver a powerful foundation for industrial transformation. FactoryTalk Optix provides a modern, flexible visualization platform for real-time monitoring and control of OT systems. FactoryTalk Optix supports numerous industrial protocols for secure interoperability and “smart-object” data modeling to provide analytics-ready data. Paired with Azure IoT Operations, a unified, adaptive cloud solution built on open standards and powered by Azure Arc, manufacturers gain seamless connectivity across the factory floor enabling edge to cloud orchestration. With support for protocols like OPC UA and MQTT, camera and third-party integration through Akri and WASM connectors, and Copilot-driven automation for observability and deployment, this partnership bridges OT and IT to unlock advanced analytics, AI-driven optimization, and predictive maintenance at scale. A Partnership That Delivers Scalable Innovation Customers can start utilizing FactoryTalk Optix with Azure IoT Operations as a scalable physical to digital foundation for transforming how they manufacture, design, and operate going forward. In partnership with Rockwell, there is a published GitHub sample that demonstrates how FactoryTalk Optix native IIoT connectivity protocols unlock contextualized data from industrial assets into Azure IoT Operations. With the 2510 Azure IoT Operations release , OPC Write capability is now available as well, creating a true read/write path for richer interoperability. The synergy between these technologies is a game-changer for manufacturers, unlocking advanced analytics, and AI-driven use cases. This collaboration delivers: Improved efficiency and reduced downtime through real-time connectivity and predictive maintenance Scalable edge-to-cloud architecture leveraging OPC UA and MQTT standards for unified OT/IT data Highly replicable, scalable deployments across hybrid and multicloud environments Proactive optimization with AI-driven design and analytics Democratized automation via Copilot capabilities for observability and deployment Unified IT management and centralized monitoring for streamlined operations Robust security and reduced integration complexity for faster time-to-value From the Shop Floor to the Boardroom By combining Rockwell’s industrial expertise with Microsoft’s cloud innovation, manufacturers can break down data silos, unify operations, and drive continuous optimization. AI-powered insights become accessible at every level, helping organizations anticipate change, improve safety and efficiency, and maintain a competitive edge in the digital era. Join Us at Rockwell Automation Fair Visit the Microsoft booth at Automation Fair to experience end-to-end demonstrations, explore customer stories, and see firsthand how the Rockwell–Microsoft ecosystem accelerates your digital transformation journey. Join live sessions at the Discovery Theatre – o Tuesday Nov 18th, 11:15am – 11:45am → The new industrial frontier - Using AI to scale faster, work smarter and unlock new value o Tuesday Nov 18 th 2pm – 3pm, and Thursday Nov 20 th at 10:00am – 11:00am → Bringing AI to the Factory Floor o Wednesday Nov 19 th , 1:45pm – 2:15pm → Start with Secure Solutions From Edge to Cloud Visit us at the Expo at Booth #1931 – For demos and conversations to see what we have to offer. Explore the products Learn more about Azure IoT Operations → https://azure.microsoft.com/en-us/products/iot-operations Explore FactoryTalk Optix → https://www.rockwellautomation.com/en-us/products/software/factorytalk/optix.html Hear more about our integration story at Microsoft Ignite → The new industrial frontierFirmware Analysis now Generally Available
Back in June, we announced the public preview of firmware analysis, a new capability available through Azure Arc to help organizations gain visibility into the security of their Internet of Things (IoT), Operational Technology (OT), and network devices. Today, we are excited to announce that firmware analysis is generally available (GA) for all Azure customers. In modern industrial environments, firmware security is a foundational requirement. IoT sensors and smart devices collect the data fueling AI-driven insights; if those devices aren’t secure, your data and operational continuity are at risk. During the preview, we heard from many customers who used firmware analysis to shine a light into their device software and address hidden vulnerabilities before attackers or downtime could strike. With general availability, firmware analysis is ready to help organizations fortify the “blind spots” in their infrastructure – from factory-floor sensors to branch office routers – by analyzing the software that runs on those devices. What Firmware Analysis Does for You Firmware analysis examines the low-level software (firmware) that powers IoT, OT and network devices, with no agent required on the device. You can upload a firmware image (for example, an extracted embedded Linux image), and the cloud service performs an automated security inspection. Key features include: Software inventory & vulnerability scanning: The service builds a Software Bill of Materials (SBOM) of components within the firmware and checks each component against known CVEs (Common Vulnerabilities and Exposures). This quickly surfaces any known vulnerabilities in your device’s software stack so you can prioritize patching those issues. Security configuration and hardening check: Firmware analysis evaluates how the firmware binaries are built, looking for security hardening measures (e.g. stack protections, ASLR) or dangerous configurations. If certain best practices are missing, the firmware might be easier to exploit – the tool flags this to inform the device manufacturer or your security team. Credential and secrets discovery: The analysis finds any hard-coded credentials (user accounts/password hashes) present in the firmware, as well as embedded cryptographic material like SSL/TLS certificates or keys. These could pose serious risks – for instance, default passwords that attackers could exploit (recall the Mirai botnet using factory-default creds) are identified so you can mitigate them. Any discovered certificates or keys can indicate potentially insecure design if left in production firmware. Comprehensive report: All security findings – from the Software Bill of Materials (SBOM), list of vulnerabilities to hardening recommendations and exposed secrets – are provided in a detailed report for each firmware image analyzed. This gives device makers and operators actionable intelligence to improve their device security posture. In short, firmware analysis provides deep insights into the contents and security quality of device firmware. It turns opaque firmware into transparent data, helping you answer, “What’s really inside my device software?” so you can address weaknesses proactively. What’s New and Licensing We’ve been hard at work making firmware analysis even better as we move to GA. Based on preview feedback, we’ve addressed bugs, implemented usability suggestions and improved the firmware analysis SDKs, CLI and PowerShell extensions. A new Azure resource called “firmware workspace” now stores analyzed firmware images. Firmware analysis workspaces are currently available as a Free Firmware Analysis Workspace SKU with capacity limits. Getting Started If you have IoT, OT and network devices in your environment, use firmware analysis to test just how secure your devices are. Getting started is easy: access firmware analysis by searching “firmware analysis” in the Azure portal, or access using this link. Onboard your subscription and then upload firmware images for analysis. For a step-by-step tutorial, visit our official documentation. The service currently supports embedded Linux-based images up to 1GB in size. We want to thank all the preview participants who tested firmware analysis and provided feedback. You helped us refine the service for GA and we’re thrilled to make this powerful tool broadly available to help secure IoT, OT and network devices around the world. We can’t wait to see how you put it to work. As always, we value your feedback, so please let us know what you think.Announcing the Firmware Analysis Public Preview
Consider an organization with thousands of smart sensors, IoT/OT and network equipment deployed on factory floors. Most of these devices are running full operating systems, but unlike traditional IT endpoints which often run security agents, IoT/OT and network devices frequently function as “black boxes”: you have little visibility into what software they’re running, which patches are applied, or what vulnerabilities might exist within them. This is the challenge many organizations face with IoT/OT and networking equipment - when a critical vulnerability is disclosed, how do you know which devices are at risk? To help address this challenge, we are excited to announce the public preview of firmware analysis, a new capability available through Azure Arc. This extends the firmware analysis feature we introduced in Microsoft Defender for IoT, making it available to a broader range of customers and scenarios through Azure. Our goal is to provide deeper visibility into IoT/OT and network devices by analyzing the foundational software (firmware) they run. Firmware analysis will also help companies that build firmware for devices better meet emerging cybersecurity regulations on their products. In this post, we’ll explain how the service works, its key features, and how it helps secure the sensors and edge devices that feed data into AI-driven industrial transformation. Securing Edge Devices to Power AI-Driven Industrial Transformation In modern industrial environments, data is king. Organizations are embracing Industry 4.0 and AI-driven solutions to optimize operations, leveraging advanced analytics and machine learning. The path to AI-driven industrial transformation is fueled by data – and much of that data comes from sensors and smart devices at the edge of the network. These edge devices measure temperature, pressure, vibration, and dozens of other parameters on the factory floor or in remote sites, feeding streams of information to cloud platforms where AI models turn data into insights. In fact, sensors are the frontline data collectors in systems like predictive maintenance, continuously monitoring equipment and generating the raw data that powers AI predictions. However, if those edge devices, sensors, and networking equipment are not secure and become compromised, the quality and reliability of the data (and thus the AI insights) cannot be guaranteed. Vulnerable devices can also be used by attackers to establish a foothold in the network, allowing them to move laterally to compromise other critical systems. In an industrial setting this could mean safety hazards, unplanned downtime, or costly inefficiencies. This is why securing the smart devices and networking equipment at the foundation of your industrial IoT data pipeline is so critical to digital transformation initiatives. By using firmware analysis on the devices’ firmware before deployment (and regularly as firmware updates roll out), the manufacturer and plant operators gain visibility into the security posture of their environment. For example, they might discover that a particular device model’s firmware contains an outdated open-source library with a known critical vulnerability. With that insight, they can work with the vendor to get a patched firmware update before any exploit occurs in the field. Or the analysis might reveal a hard-coded passwords for maintenance account in the device; the ops team can then ensure those credentials are changed or the device is isolated in a network segment with additional monitoring. In short, firmware analysis provides actionable intelligence to fortify each link in the chain of devices that your industrial systems depend on. The result is a more secure, resilient data foundation for your AI-driven transformation efforts – leading to reliable insights and safer, smarter operations on the plant floor. Firmware analysis is also a key tool used by device builders – by analyzing device firmware images before they are delivered to customers, builders can make sure that new releases and firmware updates meet their and their customers’ security standards. Firmware analysis is a key component to address emerging cybersecurity regulations such as the EU Cyber Resilience Act and the U.S. Cyber Trust Mark. How Firmware Analysis Works and Key Features Firmware analysis takes a binary firmware image (the low-level software running on an IoT/OT and network device) and conducts an automated security analysis. You can upload an unencrypted, embedded Linux-based firmware image to the firmware analysis portal. The service unpacks the image, inspects its file system, and identifies potential hidden threat vectors – all without needing any agent on the device. Here are the main capabilities of the firmware analysis service: Identifying software components and vulnerabilities: The first thing the analysis does is produce an inventory of software components found inside the firmware, generating a Software Bill of Materials (SBOM). This inventory focuses especially on open-source packages used in the firmware. Using this SBOM, the service then scans for known vulnerabilities by checking the identified components against public Common Vulnerabilities and Exposures (CVEs) databases. This surfaces any known security flaws in the device’s software stack, allowing device manufacturers and operators to prioritize patches for those issues. Analyzing binaries for security hardening: Beyond known vulnerabilities, our firmware analysis examines how the firmware’s binaries were built and whether they follow security best practices. For example, it checks for protections like stack canaries, ASLR (Address Space Layout Randomization), and other compile-time defenses. This “binary hardening” assessment indicates how resistant the device’s software might be to exploitation. If the firmware lacks certain protections, it suggests the device could be easier to exploit and highlights a need for improved secure development practices by the manufacturer. In short, this feature acts as a gauge of the device’s overall security hygiene in its compiled code. Finding weak credentials and embedded secrets: Another critical aspect of the analysis is identifying hard-coded user accounts or credentials in the firmware. Hard-coded or default passwords are a well-known weakness in IoT devices – for instance, the Mirai botnet famously leveraged a list of over 60 factory-default usernames and passwords to hijack IoT devices for DDoS attacks. Firmware analysis will flag any built-in user accounts and the password hash algorithms used, so manufacturers can remove or strengthen them, and enterprise security teams can avoid deploying devices with known default credentials. Additionally, the firmware analysis looks for cryptographic materials embedded in the image. It will detect things like expired or self-signed TLS/SSL certificates, which could jeopardize secure communications from a device. It also searches for any public or private cryptographic keys left inside the firmware – secrets that, if found by adversaries, could grant unauthorized access to the device or associated cloud services. By uncovering these hidden secrets, the service helps eliminate serious risks that might otherwise go unnoticed in the device’s software. All these insights – from software inventory and CVEs to hardening checks and secret material detection – are provided in a detailed report for each firmware image you analyze. Firmware analysis provides deep insights, clear visibility, and actionable intelligence into your devices' security posture, enabling you to confidently operate your industrial environments in the era of AI-driven industrial transformation. Getting Started and What’s Next If you have IoT/OT and network devices in your environment, use firmware analysis to test just how secure your devices are. Getting started is easy: access firmware analysis public preview by searching on “firmware analysis” in the Azure portal, or access using this link. In the future, firmware analysis will be more tightly integrated into the Azure portal. Onboard your subscription to the preview and then upload firmware images for analysis - here is a step-by-step tutorial. The service currently supports embedded Linux-based images up to 1GB in size. In this preview phase, there is no cost to analyze your firmware – our goal is to gather feedback. We are excited to share this capability with you, as it provides a powerful new tool for securing IoT/OT and network devices at scale. By shedding light on the hidden risks in device firmware, firmware analysis helps you protect the very devices that enable your AI and digital transformation initiatives. Firmware is no longer just low-level code—it’s a high-stakes surface for attack, and one that demands visibility and control. Firmware analysis equips security teams, engineers, and plant operators with the intelligence needed to act decisively—before vulnerabilities become headlines, and before attackers get a foothold. Please give the firmware analysis preview a try and let us know what you think.Scaling industrial transformation with a robust partner ecosystem
In recent years, manufacturers have been on a journey to incorporate intelligent technologies like AI into their business processes. These exciting advancements are happening within an extended ecosystem, encompassing everything from planning and manufacturing to distribution and servicing of goods. A defining aspect of many such business processes is their continuous generation of data, which, when effectively contextualized and analyzed, can unlock critical business outcomes, including minimizing downtime, reducing waste, enhancing quality, improving sustainability, and boosting worker productivity. In addition to analytics, a comprehensive data governance strategy is fundamental as it supports the ability to embrace ecosystem-driven collaboration, a key component to unlock the full potential of AI-driven manufacturing. Challenges in meeting the promise of IT and OT integration With AI only as good as the data behind it, the ability to harness data across an ecosystem is paramount. However, the inherent complexities within industrial environments create digital transformation barriers. Each factory has its own unique mix of automation equipment and software configurations based on site-specific production processes. Management and data handling are also system and site specific. When organizations try to scale transformation efforts across different sites, these complexities multiply, with individual IT management systems adding permutations. Due to the variety of source and configuration combinations, pulling the right data, semantics, and contextualization into an external analysis platform becomes incredibly difficult and cost prohibitive. As a result, the ability to scale an outcome through the use of a digital feedback loop is completely out of reach. How an adaptive cloud approach supports operational transformation To overcome these challenges, organizations can benefit from a consistent approach to industrial data value realization that is repeatable across sites. Azure’s adaptive cloud approach enables organizations to secure, manage, and scale industrial operations by unifying data, applications, and infrastructure across edge and cloud environments. By leveraging the adaptive cloud approach, businesses can create a unified data foundation, breaking down operational silos to drive AI-driven insights and improved collaboration between IT and OT teams. Azure IoT Operations, enabled by Arc empowers customers to easily move machine and process data between the edge and cloud in a highly unified and repeatable way. Under the hood, Azure IoT Operations is a full-stack data plane that runs in on-premises Arc-enabled Kubernetes clusters. It enables customers to discover Assets via Akri and collect data. Then, customers can process and send data from the edge to the cloud using open standards and open protocols that are managed and supported by Microsoft. This solution helps enable unified data flow from facilities to natively integrated cloud destinations, including Microsoft Fabric, Azure Event Hubs, and Azure Event Grid's MQTT broker which provides real-time insights and AI-driven decision-making. Azure IoT Operations leverages Azure Arc to extend the cloud management pattern down to the physical site, using the same cloud deployment and management controls as Azure to enable unique advantages in repeatability and scalability across the enterprise. While Azure's adaptive cloud approach can provide a foundation to simplify everything from data collection to scaling AI initiatives, Microsoft is a platform company, and our partners are essential to success in the complex industrial market. Why a partner ecosystem is critical for enabling customer success Achieving business outcomes from industrial data requires navigating the complexity of interconnected technology landscapes, where diverse technologies and systems must cohesively integrate. The siloed IT, OT, and ET data that results from these diverse systems can slow AI adoption, limiting manufacturers’ ability to extract real-time insights. A collaborative vendor network can help address these challenges by enabling streamlined data exchange, enhanced automation, and increased operational intelligence. The transformation enabled by this network demands a collective approach, bringing together industrial automation partners offering industry-specific AI and analytics solutions, system integrators collaboratively engineering IT-OT solutions, OEMs modernizing production lines, and ISVs to develop industry-specific solutions that drive efficiency and scalability. A multi-cloud, open, and interoperable approach can allow businesses to connect engineering, production, and supply chain workflows into AI-driven digital infrastructure from cloud to edge. Manufacturers operate in complex multi-vendor environments that demand flexibility and interoperability. Choosing to adopt an open and collaborative partner network approach offers the opportunity to extend the life of investments and adopt AI and automation gradually. In addition, unlike closed models that often lead to vendor lock in, open ecosystems enhance security and governance through consistent policy enforcement, interoperability, and real-time visibility across multi-cloud, edge, and on-prem environments. For instance, a solution like Azure Arc offers centralized security controls, automated compliance and third-party tool integration. Industrial enterprises desire a unified, scalable AI-cloud-edge strategy to optimize engineering, production, and supply chain workflows. To make outcomes from Industrial AI initiatives a reality, organizations — including traditional competitors —should consider embracing partnerships, open standards and an adaptive cloud approach to enable easier connectivity and interoperability. Microsoft’s open, scalable, and multi-cloud ecosystem helps enable more efficient integration of Azure solutions with third-party platforms (public and private clouds) and open industry standards that enable data interoperability across IoT, AI, and automation solutions. Learn more about how Microsoft, along with partners, is reimagining how intelligent digital threads and AI agents will transform the manufacturing industry here. Join Us - Industrial AI in Action at Hannover Messe 2025 Join us at the Microsoft booth in Digital Ecosystems Hall 17 to explore the latest innovations in our partner ecosystem supporting the transformation of industrial operations. Experience live demonstrations showcasing how AI-driven manufacturing, real-time data insights, and an adaptive cloud approach drive efficiency, flexibility, and innovation. See firsthand how Microsoft and its partners mentioned below are enabling intelligent automation, predictive quality control, and improved IT/OT integration to accelerate digital transformation. Avanade Avanade excels in IT/OT integration and advanced manufacturing solutions, with specialized expertise in integrating PLM, ERP, and MES systems for digital continuity across design, manufacturing, supply chain, and service processes. Avanade offers dynamic sourcing for flexible procurement and supplier collaboration, process flexibility for diverse product variants, and human-machine collaboration to meet new product requirements. At HMI 2025, Avanade and Microsoft will showcase advanced closed-loop manufacturing demos using AI machine vision for quality control, integrated with Azure IoT Operations—which leverages MQTT and OPC UA protocols to streamline data transport and connectivity. Visit the Microsoft booth to explore how seamless system integration, dynamic sourcing, and human-machine collaboration can help produce superior products faster with less waste. Learn more about Avanade at HMI 2025. Capgemini Microsoft and Capgemini are driving the next era of smart manufacturing by embracing the adaptive cloud approach to accelerate digital transformation. Through Capgemini’s Intelligent Industry offerings worker performance and operational efficiencies can be improved through AI-driven processes—empowering manufacturers to move beyond manual workflows and unlock new levels of productivity. Capgemini integrates across edge to cloud environments using services like Azure IoT Operations, Azure AI, and Microsoft Fabric to optimize quality, and overall equipment effectiveness (OEE) for manufacturers. Join us at Capgemini’s Theatre Talk at HMI on Thursday, April 3 at 10:00am, where industry leaders will share how AI, when paired with edge to cloud technologies, can unlock the full potential of smart factories. Be part of the conversation—see what’s next in digital manufacturing! Celebal Technologies The Operational Technology (OT) Data Liberator by Celebal Technologies extracts, processes, and integrates OT data into a centralized Lakehouse, ensuring metadata synchronization, real-time streaming, historical data retrieval, and a resilient data pipeline—all while maintaining full data governance and simplified infrastructure management within the customer’s network. Deployed as a Kubernetes workload at the edge, the Liberator streams MQTT data directly into Azure IoT Operations and can be configured to leverage Akri-enabled connectors for protocol translation, eliminating traditional data silos and accelerating digital transformation. Powered by Azure IoT Operations, the OT Data Liberator delivers secure, scalable connectivity across legacy and modern OT systems, enabling data transformation and management. From manufacturing and energy to utilities and resources, this collaboration empowers industries to optimize operations, enhance security, and scale digital transformation with confidence. Learn more here. Litmus Litmus, a leader in Industrial Data Operations, has partnered with Microsoft to accelerate industrial transformation by integrating Litmus Edge with Azure IoT Operations. This collaboration enables seamless connectivity through the Akri Litmus connector, supporting data processing and management of factory edge devices while bridging legacy OT systems with Microsoft’s edge to cloud technologies, including Azure Arc and Microsoft Fabric. The joint solution delivers zero-code protocol integration, centralized device orchestration, and real-time insights, simplifying edge-to-cloud data operations. Key outcomes include faster AI deployment, reduced downtime, improved product quality, and enhanced operational agility across industrial environments. Together, Litmus and Microsoft offer a unified scalable platform that empowers manufacturers to modernize operations and easily replicate lines and sites to unlock the full potential of their industrial data. Visit the Microsoft booth to see a live demo of this powerful edge-to-cloud solution in action and learn more here. Loopr.ai Loopr delivers real-time, AI-driven visual inspection for complex assemblies, performing over 400,000 inspections annually to enhance quality consistency, workforce efficiency, and cost reduction. With Azure IoT Operations, Loopr efficiently integrates with on-premise factory systems, enterprise ERP, and cloud analytics like Microsoft Fabric, enabling manufacturers to deploy AI-driven quality control within their existing Azure infrastructure. Loopr powered by Azure IoT Operations enables customers to overcome scaling challenges, optimize workflows and streamline edge-to-cloud data transport, enabling real-time analytics and enterprise-wide deployment. For example, a North American automotive manufacturer recently integrated Loopr's AI-powered visual inspection system to automate their final quality checks. This implementation led to improved precision on the production line and a reduction in defect rates. MTEK MTEK Industry AB is transforming digitalization of discrete manufacturing with its Digital Production System and advanced integration platforms. Through collaboration with Microsoft, MTEK has successfully deployed MBrain and the Manufacturing Integration Platform (Mint) in production facilities. Utilizing the full Microsoft stack, including Azure IoT Operations, Dynamics 365, Microsoft Fabric and Teams (to name a few), MTEK achieves IT/OT/human convergence, optimizing operations while reducing environmental impact. MBrain integrates into Azure IoT Operations supporting MQTT and OPC UA, enabling immediate data monitoring and management. Together, Microsoft and MTEK deliver easily integrated data exchange between edge devices and the cloud by supporting real-time analytics and decision-making. Join us at Hannover Messe 2025 to discover how MBrain's real-time data analytics and IT/OT/human convergence empower manufacturers to achieve total value capture. Schneider Electric Schneider Electric enables digital transformation by integrating world-leading automation and energy technologies, endpoint to cloud connecting products, controls, software and services, across the entire lifecycle, enabling integrated company management, for homes, buildings, data centers, infrastructure, and industries. Schneider Electric is partnering with Microsoft to transform manufacturing into an AI-powered, open, software-defined industry. Microsoft's AI, Edge & Cloud patterns are combined with Schneider Electric's advanced, secure, and user-friendly industrial automation edge solution. Join us at HMI 2025 to experience this direct-to-cloud, secure interface that empowers innovative, data-driven approaches to modernize processes and products using AI agents and digital twin solutions with real-time simulation. Siemens Siemens develops technologies that power progress across industrial automation, infrastructure, transportation, and healthcare, with a strong emphasis on digital solutions and sustainability globally. The collaboration with Siemens leverages Siemens Industrial Edge and Microsoft Azure IoT Operations to create integrated, data-driven production environments that address customer pain points. This partnership helps ensure data flow from the shop floor to the cloud, empowering manufacturers to harness advanced technologies like AI and digital twins to streamline their production processes. Learn more about how Siemens and Microsoft are partnering to accelerate IT and OT integration at HMI 2025. Sight Machine Sight Machine’s industrial AI data platform, now deployable at the edge with Azure IoT Operations, unifies real-time production data, enhancing data accessibility and productivity. At the Microsoft booth come and discover how Sight Machine and Microsoft are revolutionizing beverage bottling operations by reducing downtime and increasing availability through real-time plant data, AI-driven insights, and collaboration tools, all powered by Microsoft’s secure, scalable cloud infrastructure. Microsoft fosters collaborative innovation, empowering partners to drive industrial transformation. At Hannover Messe 2025, Sight Machine will also demonstrate its integration with NVIDIA Omniverse, offering real-time 3D visualization, rapid troubleshooting, and root cause analysis. Co-developed by Microsoft, NVIDIA, and Sight Machine, this solution enhances manufacturing performance. Visit the NVIDIA booth to learn more. Symphony AI SymphonyAI revolutionizes the Intelligent Factory with Predictive, Generative and Agentic AI solutions for industrial verticals across manufacturing, consumer goods and energy. Their software drives end-to-end digital transformation from edge to cloud, integrating data sources, contextualizing information, and powering AI-driven applications. At HMI, discover how SymphonyAI’s IRIS Foundry Industrial DataOps platform is extending capabilities to the edge to help manufacturers leverage factory data to expedite AI drive value in maintenance, quality, process optimization, closed-loop operations, and overall plant performance. The new edge capabilities easily and securely connect to factory systems, store and transform data, automate workflows and leverage Azure IoT Operations Dataflows and MQTT Broker to smoothly transport data to IRIS Foundry, unlocking actionable AI for factory operations. Don't miss this opportunity to see how we can transform your operations—join us at HMI for the demo. Learn more AI and the adaptive cloud approach are transforming how industries design, build and operate, driving the next wave of efficiency, agility, and innovation. To fully harness this potential, organizations should embrace a collaborative ecosystem that fosters AI-driven insights, simplified data integration, and secure digital transformation. The future of manufacturing is intelligent, interconnected, and AI-powered—and success depends on a strong partner network, a flexible cloud strategy, and a commitment to open, multi-cloud innovation. By working together, we can accelerate industrial transformation, overcome complex challenges, and unlock the full power of smart manufacturing. Learn more about the adaptive cloud approach and explore comprehensive cloud-to-edge scenarios designed for specific industry needs with Arc Jumpstart Agora.
