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Agent Factory: Creating a blueprint for safe and secure AI agents

This blog post is the sixth out of a six-part blog series called Agent Factory which shares best practices, design patterns, and tools to help guide you through adopting and building agentic AI.

Trust as the next frontier

Trust is rapidly becoming the defining challenge for enterprise AI. If observability is about seeing, then security is about steering. As agents move from clever prototypes to core business systems, enterprises are asking a harder question: how do we keep agents safe, secure, and under control as they scale?

The answer is not a patchwork of point fixes. It is a blueprint. A layered approach that puts trust first by combining identity, guardrails, evaluations, adversarial testing, data protection, monitoring, and governance.

Learn more about building trust with Azure AI Foundry

Why enterprises need to create their blueprint now

Across industries, we hear the same concerns:

CISOs worry about agent sprawl and unclear ownership.

Security teams need guardrails that connect to their existing workflows.

Developers want safety built in from day one, not added at the end.

These pressures are driving the shift left phenomenon. Security, safety, and governance responsibilities are moving earlier into the developer workflow. Teams cannot wait until deployment to secure agents. They need built-in protections, evaluations, and policy integration from the start.

Data leakage, prompt injection, and regulatory uncertainty remain the top blockers to AI adoption. For enterprises, trust is now a key deciding factor in whether agents move from pilot to production.

What safe and secure agents look like

From enterprise adoption, five qualities stand out:

Unique identity: Every agent is known and tracked across its lifecycle.

Data protection by design: Sensitive information is classified and governed to reduce oversharing.

Built-in controls: Harm and risk filters, threat mitigations, and groundedness checks reduce unsafe outcomes.

Evaluated against threats: Agents are tested with automated safety evaluations and adversarial prompts before deployment and throughout production.

Continuous oversight: Telemetry connects to enterprise security and compliance tools for investigation and response.

These qualities do not guarantee absolute safety, but they are essential for building trustworthy agents that meet enterprise standards. Baking these into our products reflects Microsoft’s approach to trustworthy AI. Protections are layered across the model, system, policy, and user experience levels, continuously improved as agents evolve.

How Azure AI Foundry supports this blueprint

Azure AI Foundry brings together security, safety, and governance capabilities in a layered process enterprises can follow to build trust in their agents.

Entra Agent IDComing soon, every agent created in Foundry will be assigned a unique Entra Agent ID, giving organizations visibility into all active agents across a tenant and helping to reduce shadow agents.

Agent controlsFoundry offers industry first agent controls that are both comprehensive and built in. It is the only AI platform with a cross-prompt injection classifier that scans not just prompt documents but also tool responses, email triggers, and other untrusted sources to flag, block, and neutralize malicious instructions. Foundry also provides controls to prevent misaligned tool calls, high risk actions, and sensitive data loss, along with harm and risk filters, groundedness checks, and protected material detection.

Risk and safety evaluationsEvaluations provide a feedback loop across the lifecycle. Teams can run harm and risk checks, groundedness scoring, and protected material scans both before deployment and in production. The Azure AI Red Teaming Agent and PyRIT toolkit simulate adversarial prompts at scale to probe behavior, surface vulnerabilities, and strengthen resilience before incidents reach production.

Data control with your own resourcesStandard agent setup in Azure AI Foundry Agent Service allows enterprises to bring their own Azure resources. This includes file storage, search, and conversation history storage. With this setup, data processed by Foundry agents remains within the tenant’s boundary under the organization’s own security, compliance, and governance controls.

Network isolationFoundry Agent Service supports private network isolation with custom virtual networks and subnet delegation. This configuration ensures that agents operate within a tightly scoped network boundary and interact securely with sensitive customer data under enterprise terms.

Microsoft PurviewMicrosoft Purview helps extend data security and compliance to AI workloads. Agents in Foundry can honor Purview sensitivity labels and DLP policies, so protections applied to data carry through into agent outputs. Compliance teams can also use Purview Compliance Manager and related tools to assess alignment with frameworks like the EU AI Act and NIST AI RMF, and securely interact with your sensitive customer data under your terms.

Microsoft DefenderFoundry surfaces alerts and recommendations from Microsoft Defender directly in the agent environment, giving developers and administrators visibility into issues such as prompt injection attempts, risky tool calls, or unusual behavior. This same telemetry also streams into Microsoft Defender XDR, where security operations center teams can investigate incidents alongside other enterprise alerts using their established workflows.

Governance collaboratorsFoundry connects with governance collaborators such as Credo AI and Saidot. These integrations allow organizations to map evaluation results to frameworks including the EU AI Act and the NIST AI Risk Management Framework, making it easier to demonstrate responsible AI practices and regulatory alignment.

Blueprint in action

From enterprise adoption, these practices stand out:

Start with identity. Assign Entra Agent IDs to establish visibility and prevent sprawl.

Built-in controls. Use Prompt Shields, harm and risk filters, groundedness checks, and protected material detection.

Continuously evaluate. Run harm and risk checks, groundedness scoring, protected material scans, and adversarial testing with the Red Teaming Agent and PyRIT before deployment and throughout production.

Protect sensitive data. Apply Purview labels and DLP so protections are honored in agent outputs.

Monitor with enterprise tools. Stream telemetry into Defender XDR and use Foundry observability for oversight.

Connect governance to regulation. Use governance collaborators to map evaluation data to frameworks like the EU AI Act and NIST AI RMF.

Proof points from our customers

Enterprises are already creating security blueprints with Azure AI Foundry:

EY uses Azure AI Foundry’s leaderboards and evaluations to compare models by quality, cost, and safety, helping scale solutions with greater confidence.

Accenture is testing the Microsoft AI Red Teaming Agent to simulate adversarial prompts at scale. This allows their teams to validate not just individual responses, but full multi-agent workflows under attack conditions before going live.

Learn more

Create with Azure AI Foundry.

Join us at Microsoft Secure on September 30 to learn about our newest capabilities and how Azure AI Foundry integrates with Microsoft Security to help you build safe and secure agents, with speakers including Vasu Jakkal, Sarah Bird, and Herain Oberoi.

Implement a responsible generative AI solution in Azure AI Foundry.

Did you miss these posts in the Agent Factory series?

The new era of agentic AI—common use cases and design patterns

Building your first AI agent with the tools to deliver real-world outcomes

Top 5 agent observability best practices for reliable AI

From prototype to production—developer tools and rapid agent development

Connecting agents, apps, and data with new open standards like MCP and A2A

Azure AI Foundry
Build trustworthy AI agents that automate tasks, enhance user experiences, and deliver results.

Learn more

The post Agent Factory: Creating a blueprint for safe and secure AI agents appeared first on Microsoft Azure Blog.
Quelle: Azure

Microsoft named a Leader in the 2025 Gartner® Magic Quadrant™ for Global Industrial IoT Platforms 

We’re proud to share that Microsoft has been recognized as a Leader in the 2025 Gartner Magic Quadrant for Global Industrial IoT (IIoT) Platforms. We believe this recognition underscores our commitment to empowering industries with intelligent, secure, and scalable solutions that drive real-world impact. 

As industrial organizations continue to modernize their operations, Azure’s adaptive cloud approach, which includes Azure IoT, Azure Arc, and more, can help manufacturing, energy, and logistics organizations to enhance efficiency, optimize performance, and drive secure innovation at scale.

Accelerate digital transformation with Azure IoT

Recognized in industrial IoT

In today’s data-driven industrial landscape, Internet of Things (IoT) technology already serves as a strategic driver of operational excellence and competitiveness. Connected sensors and edge devices capture machines and process data that can then be integrated with other key enterprise systems such as Manufacturing Execution System (MES), Enterprise Resource Planning (ERP), and analytics in digital twin models to predict failures, optimize asset utilization, and reduce downtime. These capabilities result in business value by helping organizations improve key performance indicators (KPIs) such as quality assurance, energy efficiency, and supply chain traceability.

As the backbone for AI, IoT provides high frequency telemetry and controls the pathways needed for advanced analytics and machine learning. This synergy powers additional industrial use cases like self-dispatching field services, vision-based quality inspection, process optimization, energy load balancing, and intelligent operator assistance. Industrial IoT, especially when deployed with an adaptive cloud approach, can transform AI from isolated pilots into scalable, production-grade capabilities that could boost yield, throughput, and sustainability. These capabilities can help unlock a new type of organization, what we call the Frontier Industrial firm—industrial companies operating at the leading edge of digitalization to pursue superior productivity. 

We continue to help industrial organizations transform by focusing on the key areas our customers prioritize: 

Comprehensive platform for industrial needs: Microsoft’s industrial IoT platform enables a wide variety of industrial use cases through its ability to bring data from distributed and collocated devices into a common data foundation for analysis and action. Key capabilities include Azure IoT Hub, Azure Digital Twins, Microsoft Defender for IoT, Azure IoT Operations, and Microsoft Fabric. Together, these tools help empower organizations to make data-driven decisions, boost operational efficiency, and scale AI across varied deployment environments.  

Industrial data acquisition and management: Microsoft’s focus on standards, ecosystem partnerships, and helping customers take advantage of existing investments are key pillars of its industrial data management strategy. Industrial data acquisition and management are challenging today due to the complexity of industrial environments. To help with this, Azure IoT Operations natively integrates with brownfield environments and enables high-velocity operational technology (OT)/IoT data collection and contextualization using Akri connectors. Once gathered, the data is stored in OneLake using Microsoft Fabric, allowing for unified modeling in a central location.   

Azure Arc-enabled Kubernetes extends these capabilities with open-standard APIs that integrate seamlessly with Azure’s cloud management graph (ARM graph), ensuring consistent security, auditing, and policy enforcement. Meanwhile, Azure Device Registry unifies asset management by representing edge assets as Azure resources which lays the groundwork for scalable application deployments. 

Real-time intelligence for smarter decisions: Microsoft’s strength in industrial data management is more than just technical, it is transformational. By cleaning, contextualizing, and curating OT/IoT data at the edge, Azure IoT Operations builds a solid foundation for real-time intelligence. Integrated with Microsoft Fabric and Azure Digital Twin Builder, this enables AI-enhanced decision making that helps customers optimize production quality, improve equipment reliability, and support sustainable operations.  

Integration with Microsoft Copilot in Azure: Microsoft is redefining intelligence with the integration of Copilot and generative AI capabilities across its IoT platform, especially Azure IoT Operations and Microsoft Fabric. With Copilot in Azure you can retrieve intelligent recommendations for operations management, advanced data analysis, and visualization. This empowers industrial teams to make faster and smarter decisions, whether optimizing workflows, interpreting complex datasets, or managing supervisory tasks.  

Cloud-to-edge integration with the adaptive cloud approach: Microsoft’s IoT platform enabled by Azure Arc’s adaptive cloud approach unifies hybrid, multicloud, edge, and IoT environments. This provides a consistent unified control plane for applications, data, and governance that meets industrial needs for scalability and operational efficiency.  

Secure by design, intelligent by default: Azure IoT Operations is designed with security at its core. This proactive approach is intended to reduce operational burden for users. Microsoft also has a fully integrated security suite, including Microsoft Defender for IoT, Microsoft Sentinel and Microsoft Entra. 

Shaping the future of digital operations 

Our roadmap will focus on expanding AI capabilities—including agentic and generative AI—across the Azure stack. We are committed to helping customers harness the full potential of their data, streamline operations, and innovate faster. We remain focused on evolving our platform to meet various industrial needs. As we continue to innovate, our priority is making it easier for customers and partners to build confidently on Azure. 

Microsoft has a robust partner ecosystem that can help ensure local expertise and tailored solutions for every industry, to unlock new opportunities and deliver even greater impact. Whether it is co-innovating on industry-specific solutions or scaling AI adoption globally, our partners are essential to helping customers build confidently on Azure.  

Learn more

Discover how Microsoft’s IoT offerings can enhance your operations. Explore the resources below for more information-

Explore Azure IoT portfolio.

Explore our unified data platform, Microsoft Fabric, and learn more about Digital Twin Builder in Microsoft Fabric.

Check out our IoT Partner Ecosystem.

Gartner, Magic Quadrant for Global Industrial IoT Platforms, By Scot Kim, Sudip Pattanayak, Emil Berthelsen, Sushovan Mukhopadhyay, Wam Voster, Akhil Singh, September 8, 2025.

Gartner does not endorse any vendor, product or service depicted in its research publications and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s Research & Advisory organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally, Magic Quadrant is a registered trademark of Gartner, Inc. and/or its affiliates and is used herein with permission. All rights reserved.

This graphic was published by Gartner, Inc. as part of a larger research document and should be evaluated in the context of the entire document. The Gartner document is available upon request from here.
The post Microsoft named a Leader in the 2025 Gartner® Magic Quadrant™ for Global Industrial IoT Platforms  appeared first on Microsoft Azure Blog.
Quelle: Azure

Inside the world’s most powerful AI datacenter

This week we have introduced a wave of purpose-built datacenters and infrastructure investments we are making around the world to support the global adoption of cutting-edge AI workloads and cloud services.

Today in Wisconsin we introduced Fairwater, our newest US AI datacenter, the largest and most sophisticated AI factory we’ve built yet. In addition to our Fairwater datacenter in Wisconsin, we also have multiple identical Fairwater datacenters under construction in other locations across the US.

In Narvik, Norway, Microsoft announced plans with nScale and Aker JV to develop a new hyperscale AI datacenter.

In Loughton, UK, we announced a partnership with nScale to build the UK’s largest supercomputer to support services in the UK.

These AI datacenters are significant capital projects, representing tens of billions of dollars of investments and hundreds of thousands of cutting-edge AI chips, and will seamlessly connect with our global Microsoft Cloud of over 400 datacenters in 70 regions around the world. Through innovation that can enable us to link these AI datacenters in a distributed network, we multiply the efficiency and compute in an exponential way to further democratize access to AI services globally.

So what is an AI datacenter?

The AI datacenter: the new factory of the AI era

Aerial view of Microsoft’s new AI datacenter campus in Mt Pleasant, Wisconsin.

An AI datacenter is a unique, purpose-built facility designed specifically for AI training as well as running large-scale artificial intelligence models and applications. Microsoft’s AI datacenters power OpenAI, Microsoft AI, our Copilot capabilities and many more leading AI workloads.

The new Fairwater AI datacenter in Wisconsin stands as a remarkable feat of engineering, covering 315 acres and housing three massive buildings with a combined 1.2 million square feet under roofs. Constructing this facility required 46.6 miles of deep foundation piles, 26.5 million pounds of structural steel, 120 miles of medium-voltage underground cable and 72.6 miles of mechanical piping.

Unlike typical cloud datacenters, which are optimized to run many smaller, independent workloads such as hosting websites, email or business applications, this datacenter is built to work as one massive AI supercomputer using a single flat networking interconnecting hundreds of thousands of the latest NVIDIA GPUs. In fact, it will deliver 10X the performance of the world’s fastest supercomputer today, enabling AI training and inference workloads at a level never before seen.

The role of our AI datacenters – powering frontier AI

Effective AI models rely on thousands of computers working together, powered by GPUs, or specialized AI accelerators, to process massive concurrent mathematical computations. They’re interconnected with extremely fast networks so they can share results instantly, and all of this is supported by enormous storage systems that hold the data (like text, images or video) broken down into tokens, the small units of information the AI learns from. The goal is to keep these chips busy all the time, because if the data or the network can’t keep up, everything slows down.

The AI training itself is a cycle: the AI processes tokens in sequence, makes predictions about the next one, checks them against the right answers and adjusts itself. This repeats trillions of times until the system gets better at whatever it’s being trained to do. Think of it like a professional football team’s practice. Each GPU is a player running a drill, the tokens are the plays being executed step by step, and the network is the coaching staff, shouting instructions and keeping everyone in sync. The team repeats plays over and over, correcting mistakes until they can execute them perfectly. By the end, the AI model, like the team, has mastered its strategy and is ready to perform under real game conditions.

AI infrastructure at frontier scale

Purpose-built infrastructure is critical to being able to power AI efficiently. To compute the token math at this trillion-parameter scale of leading AI models, the core of the AI datacenter is made up of dedicated AI accelerators (such as GPUs) mounted on server boards alongside CPUs, memory and storage. A single server hosts multiple GPU accelerators, connected for high-bandwidth communication. These servers are then installed into a rack, with top-of-rack (ToR) switches providing low-latency networking between them. Every rack in the datacenter is interconnected, creating a tightly coupled cluster. From the outside, this architecture looks like many independent servers, but at scale it functions as a single supercomputer where hundreds of thousands of accelerators can train a single model in parallel.

This datacenter runs a single, massive cluster of interconnected NVIDIA GB200 servers and millions of compute cores and exabytes of storage, all engineered for the most demanding AI workloads. Azure was the first cloud provider to bring online the NVIDIA GB200 server, rack and full datacenter clusters. Each rack packs 72 NVIDIA Blackwell GPUs, tied together in a single NVLink domain that delivers 1.8 terabytes of GPU-to-GPU bandwidth and gives every GPU access to 14 terabytes of pooled memory. Rather than behaving like dozens of separate chips, the rack operates as a single, giant accelerator, capable of processing an astonishing 865,000 tokens per second, the highest throughput of any cloud platform available today. The Norway and UK AI datacenters will use similar clusters, and take advantage of NVIDIAs next AI chip design (GB300) which offers even more pooled memory per rack.

The challenge in establishing supercomputing scale, particularly as AI training requirements continue to require breakthrough scales of computing, is getting the networking topology just right. To ensure low latency communication across multiple layers in a cloud environment, Microsoft needed to extend performance beyond a single rack. For the latest NVIDIA GB200 and GB300 deployments globally, at the rack level these GPUs communicate over NVLink and NVSwitch at terabytes per second, collapsing memory and bandwidth barriers. Then to connect across multiple racks into a pod, Azure uses both InfiniBand and Ethernet fabrics that deliver 800 Gbps, in a full fat tree non-blocking architecture to ensure that every GPU can talk to every other GPU at full line rate without congestion. And across the datacenter, multiple pods of racks are interconnected to reduce hop counts and enable tens of thousands of GPUs to function as one global-scale supercomputer.

When laid out in a traditional datacenter hallway, physical distance between racks introduces latency into the system. To address this, the racks in the Wisconsin AI datacenter are laid out in a two-story datacenter configuration, so in addition to racks networked to adjacent racks, they are networked to additional racks above or below them.

This layered approach sets Azure apart. Microsoft Azure was not just the first cloud to bring GB200 online at rack and datacenter scale; we’re doing it at massive scale with customers today. By co-engineering the full stack with the best from our industry partners coupled with our own purpose-built systems, Microsoft has built the most powerful, tightly coupled AI supercomputer in the world, purpose-built for frontier models.

High density cluster of AI infrastructure servers in a Microsoft datacenter.

Addressing the environmental impact: closed loop liquid cooling at facility scale

Traditional air cooling can’t handle the density of modern AI hardware. Our datacenters use advanced liquid cooling systems — integrated pipes circulate cold liquid directly into servers, extracting heat efficiently. The closed-loop recirculation ensures zero water waste, with water only needed to fill up once and then it is continually reused.

By designing purpose-built AI datacenters, we were able to build liquid cooling infrastructure into the facility directly to get us more rack-density in the datacenter. Fairwater is supported by the second largest water-cooled chiller plant on the planet and will continuously circulate water in its closed loop cooling system. The hot water is then piped out to the cooling “fins” on each side of the datacenter, where 172 20-foot fans chill and recirculate the water back to the datacenter. This system keeps the AI datacenter running efficiently, even at peak loads.

Aerial view of part of the closed loop liquid cooling system.

Over 90% of our datacenter capacity uses this system, requiring water only once during construction and continually reusing it with no evaporation losses. The remaining 10% of traditional servers use outdoor air for cooling, switching to water only during the hottest days, a design that dramatically reduces water usage compared to traditional datacenters.

We’re also using liquid cooling to support AI workloads in many of our existing datacenters; this liquid cooling is accomplished with Heat Exchanger Units (HXUs) that also operate with zero-operational water use.

Storage and compute: Built for AI velocity

Modern datacenters can contain exabytes of storage and millions of CPU compute scores. To support the AI infrastructure cluster, an entirely separate datacenter infrastructure is needed to store and process the data used and generated by the AI cluster. To give you an example of the scale — the Wisconsin AI datacenter’s storage systems are five football fields in length!

Aerial view of a dedicated storage and compute datacenter used to store and process data for the AI datacenter.

We reengineered Azure storage for the most demanding AI workloads, across these massive datacenter deployments for true supercomputing scale. Each Azure Blob Storage account can sustain over 2 million read/write transactions per second, and with millions of accounts available, we can elastically scale to meet virtually any data requirement.

Behind this capability is a fundamentally rearchitected storage foundation that aggregates capacity and bandwidth across thousands of storage nodes and hundreds of thousands of drives. This enables scale to exabyte scale storage, eliminating the need for manual sharding and simplifying operations for even the largest AI and analytics workloads.

Key innovations such as BlobFuse2 deliver high-throughput, low-latency access for GPU node-local training, ensuring that compute resources are never idle and that massive AI training datasets are always available when needed. Multiprotocol support allows seamless integration with diverse data pipelines, while deep integration with analytics engines and AI tools accelerates data preparation and deployment.

Automatic scaling dynamically allocates resources as demand grows, combined with advanced security, resiliency and cost-effective tiered storage, Azure’s storage platform sets the pace for next-generation workloads, delivering the performance, scalability and reliability required.

AI WAN: Connecting multiple datacenters for an even larger AI supercomputer

These new AI datacenters are part of a global network of Azure AI datacenters, interconnected via our Wide Area Network (WAN). This isn’t just about one building, it’s about a distributed, resilient and scalable system that operates as a single, powerful AI machine. Our AI WAN is built with growth capabilities in AI-native bandwidth scales to enable large-scale distributed training across multiple, geographically diverse Azure regions, thus allowing customers to harness the power of a giant AI supercomputer.

This is a fundamental shift in how we think about AI supercomputers. Instead of being limited by the walls of a single facility, we’re building a distributed system where compute, storage and networking resources are seamlessly pooled and orchestrated across datacenter regions. This means greater resiliency, scalability and flexibility for customers.

Bringing it all together

To meet the critical needs of the largest AI challenges, we needed to redesign every layer of our cloud infrastructure stack. This isn’t just about isolated breakthroughs, but composing multiple new approaches across silicon, servers, networks and datacenters, leading to advancements where software and hardware are optimized as one purpose-built system.

Microsoft’s Wisconsin datacenter will play a critical role in the future of AI, built on real technology, real investment and real community impact. As we connect this facility with other regional datacenters, and as every layer of our infrastructure is harmonized as a complete system, we’re unleashing a new era of cloud-powered intelligence, secure, adaptive and ready for what’s next.

To learn more about Microsoft’s datacenter innovations, check out the virtual datacenter tour at datacenters.microsoft.com.

Scott Guthrie is responsible for hyperscale cloud computing solutions and services including Azure, Microsoft’s cloud computing platform, generative AI solutions, data platforms and information and cybersecurity. These platforms and services help organizations worldwide solve urgent challenges and drive long-term transformation.
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Quelle: Azure