Effective AI Governance with Azure
Why is AI Governance needed? As organizations increasingly adopt AI in their cloud environments, effective governance is essential to ensure sustainability, security, and operational excellence. Without proper oversight, AI workloads can escalate costs, expose vulnerabilities, and struggle with resiliency under dynamic conditions. AI Governance provides a structured approach to managing AI investments, securing sensitive data, optimizing performance, and ensuring compliance with evolving regulations. By implementing governance best practices, enterprises can balance innovation with control, enabling AI-driven solutions to scale efficiently and responsibly. This blog explores key areas of AI Governance, including cost management, security, resiliency, operational optimization, and model oversight. Five Pillars of AI Governance Manage AI Costs Choose the right billing model: For unpredictable usage, the Pay-as-you-go model works best, while predictable workloads benefit from Provisioned Throughput Units (PTUs). Mixing PTU endpoints with consumption-based endpoints helps save money, as PTUs take care of the main tasks while consumption-based endpoints deal with any extra demand. Choose the right model: Opting for an AI model should balance performance requirements with cost considerations. Select less expensive models unless the use case demands a higher-cost option. During fine-tuning, ensure maximum utilization of time within each billing period to prevent incurring additional charges. Reservations: By committing to a reservation for Provisioned Throughput Units (PTUs) over a period of one month or one year, you can realize savings. Most OpenAI models offer reservations, with discounts typically ranging from 30% to 60%. Track and control token usage: The Generative AI Gateway helps manage costs by tracking and throttling token usage, applying circuit breakers, and routing requests to multiple AI endpoints. Incorporating a semantic cache can further optimize both performance and expenses when using LLMs. Additionally, setting model-based provisioning quotas ensures better cost control by preventing unnecessary usage. Policies to shut down unused instances: Establish a policy requiring AI resources to enable the automatic shutdown feature on virtual machines and compute instances in Azure AI Foundry and Azure Machine Learning. This requirement applies to nonproduction environments and production workloads that can be taken offline periodically. Secure AI Workloads AI threat protection: Defender for Cloud provides real-time monitoring of Gen AI applications to detect security vulnerabilities. AI threat protection works with Azure AI content safety prompt shields and Microsoft’s threat intelligence to identify risks such as data leakage, data poisoning, jailbreak attempts, and credential threats. Integration with Defender XDR enables security teams to centralize alerts for AI workloads within the Defender XDR portal. Access and identity controls: Grant the minimum necessary user access to centralized AI resources. Leverage managed identities across supported Azure AI services and restrict access to essential AI model endpoints only. Implement just-in-time access to enable temporary elevation of permissions when required. Disable local authentication as needed. Key management: Azure AI services provide two API keys for each resource to facilitate secret rotation, enhancing security by enabling regular key updates. This feature protects service privacy in case of key leakage. It is recommended to store all keys securely in Azure Key Vault. Regulatory compliance: AI regulatory compliance involves utilizing industry-specific initiatives in Azure Policy and applying relevant policies for services like Azure AI Foundry and Azure Machine Learning. Compliance checklists designed for specific industries and locations, along with standards like ISO/IEC 23053:2022, assist in reviewing and confirming that AI workloads meet regulatory requirements. Network security: Azure AI services use a layered security model to restrict access to specific networks. Configuring network rules ensures that only applications from designated networks can access the account. Access can be further filtered by IP addresses, ranges, or Azure Virtual Network subnets. When network rules are in effect, applications must be authorized using Microsoft Entra ID credentials or a valid API key. Data security: Maintain strict data security boundaries by cataloging data to avoid feeding sensitive information to public-facing AI endpoints. Use legally licensed data for AI model grounding or training, and implement tools like Protected Material Detection to prevent copyright infringement. Establish version control for grounding data to track and revert changes, ensuring consistency and compliance across deployments. Regularly review outputs for intellectual property adherence. Tag sensitive information using Azure Information Protection. Risk scenario Risk impact Resiliency mitigation example Cyberattacks Ransomware, distributed denial of service (DDoS), or unauthorized access. To reduce impact, include robust security measures, including an appropriate backup and recovery process, in your adoption strategy and plan. System failures Hardware or software malfunctions. Design for quick recovery and data integrity restoration. Handle transient faults in your applications, and provide redundancy in your infrastructure, such as multiple replicas with automatic failover. Configuration issues Deployment errors or misconfigurations. Treat configuration changes as code changes by using infrastructure as code (IaC). Use continuous integration/continuous deployment (CI/CD) pipelines, canary deployments, and rollback mechanisms to minimize the impact of faulty updates or deployments. Demand spikes or overload Performance degradation during peak usage or spikes in traffic. Use elastic scalability to ensure that systems automatically scale to handle an increased demand without disruption to service. Compliance failures Breaches of regulatory standards. Adopt compliance tools like Microsoft Purview and use Azure Policy to enforce compliance requirements. Natural disasters Datacenter outages caused by earthquakes, floods, or storms. Plan for failover, high availability, and disaster recovery by using availability zones, multiple regions, or even multicloud approaches. Resilience for AI Platforms Deploy AI landing zones: AI landing zones provide pre-designed, scalable environments that provide a structured foundation for deploying AI workloads in Azure. They integrate various Azure services to ensure governance, compliance, security, and operational efficiency. ALZ’s help streamline AI deployments while maintaining best practices for scalability and performance. Reliable scaling strategy: AI applications require effective scaling strategies, such as auto scaling and automatic scaling mechanisms. While auto-scaling operates based on predefined threshold rules, automatic scaling leverages intelligent algorithms to adaptively scale resources by analyzing learned usage patterns. Disaster recovery planning: A critical component of business continuity that requires the development of techniques for High Availability (HA) and Disaster Recovery (DR) for your AI endpoints and AI Data. This involves deploying zonal services within a region to ensure HA and provisioning instances in a secondary region to enable effective DR. Building global resilience: Global deployment optimizes capacity utilization and throughput for generative AI by accessing distributed pools across regions. Intelligent routing prioritizes less busy instances, ensuring processing efficiency and reliability. Azure API Management (APIM) with premium SKU supports resilient global deployments, maintaining a single endpoint for seamless failover and enhanced scalability without burdening applications. Optimizing AI Operations Latency: With generative AI, inferencing time far outweighs network latency, making network time negligible in overall operations. A global deployment, leveraging intelligent routing to identify less busy capacity pools worldwide, ensures faster processing by utilizing idle resources effectively. This approach transforms traditional latency considerations, emphasizing the scalability and efficiency of globally distributed models over proximity. Additionally, seasonal differences across regions further enhance the potential for optimized performance. Capacity and throughput: Global deployments optimize capacity and throughput by accessing larger pools and leveraging intelligent routing to direct requests to less busy instances, ensuring faster processing and quota fulfillment. Data Zones balance broader capacity access with compliance for regions with sovereignty needs, while Provisioned Throughput Units (PTUs) can further improve utilization by dynamically managing token distribution across pools for maximum efficiency. Standard options remain limited and may restrict throughput under heavy demand. AI observability: GenAI observability encompasses monitoring model performance, capacity utilization, token throughput, and compliance across distributed systems. It tracks token utilization to ensure efficient distribution and optimize throughput, supported by tools like PTU for dynamic management. General observability features include latency tracking, resource allocation insights, error rate monitoring, and proactive alerting, enabling seamless operations and adherence to data sovereignty requirements while maximizing performance and efficiency. Azure OpenAI observability metrics Category Metric Unit Dimensions Aggregation Description HTTP Requests Total Request Count Count Endpoint, API Operation, Region Sum Tracks the total number of HTTP requests made to the Azure OpenAI endpoints. Failed Requests Count Status Code, Region, API Operation Sum Monitors the count of requests resulting in errors (e.g., 4xx, 5xx response codes). Request Rate Requests/second Endpoint, Region Average Measures the rate of incoming requests to analyze traffic patterns. Latency Request Latency Milliseconds (ms) Endpoint, Region, API Operation Average, Percentiles (50th, 90th, 99th) Captures the average response time of requests, broken down by endpoint or API call. Response Time Percentiles Milliseconds (ms) Endpoint, Region, API Operation Percentiles (50th, 90th, 99th) Identifies outliers or slow responses in terms of latency across different percentiles. Usage Token Utilization Tokens API Key, Region, Instance Type Sum, Average Tracks the number of tokens processed (prompt and completion) to monitor quota usage. Throttled Requests Count API Key, Region Sum Counts requests delayed or rejected due to throttling or quota limits. Actions Cache Hits/Misses Count Cache Type, Region, Endpoint Ratio (Hits vs Misses), Sum Monitors the efficiency of semantic or prompt caching to optimize token usage. Request Routing Efficiency Percentage (%) Region, Capacity Pool Average Tracks the accuracy of routing requests to the least busy capacity pool for better processing. Throughput Tokens/second Endpoint, Region Sum, Average Measures successfully processed tokens or requests per second to ensure capacity optimization. Govern AI Models Control the models: Azure Policy can be used to control which models teams are permitted to deploy from the Azure AI Foundry catalog. Organizations are advised to start with audit mode, which monitors model usage without restricting deployments. Transitioning to deny mode should only occur after thoroughly understanding workload teams’ development needs to avoid unnecessary disruption. It’s important to note that deny mode does not automatically remove noncompliant models already deployed, and these must be addressed manually. Evaluating models: Evaluation is a critical aspect of the generative AI lifecycle, ensuring models meet accuracy, performance, security, and ethical standards while mitigating biases and validating robustness before deployment. It plays a role at every stage, from selecting the base model to pre-production validation and post-production monitoring. Azure provides several tools to support systematic evaluation, including Azure AI Foundry, which offers built-in metrics for assessing AI model performance. The Evaluation API in Azure OpenAI Service enables automated quality checks by integrating evaluations into CI/CD pipelines. Additionally, organizations can leverage Azure DevOps and GitHub Actions to conduct bulk evaluations, ensuring AI models remain compliant, optimized, and trustworthy throughout their lifecycle. Content filters for models: Organizations are advised to define baseline content filters for generative AI models using Azure AI Content Safety. This system evaluates both prompts and completions through classification models that identify and mitigate harmful content across various categories. Key features include prompt shields, groundedness detection, and protected material text scanning for both images and text. Establishing a process for application teams to communicate governance needs ensures alignment and comprehensive oversight of safety measures. Ground AI models: To effectively manage generative AI output, utilize system messages and the retrieval augmented generation (RAG) pattern to ensure responses are grounded and reliable. Test grounding techniques using tools like prompt flow for structured workflows or the open-source red teaming framework PyRIT to identify potential vulnerabilities. These strategies help refine model behavior and maintain alignment with governance requirements.Integrate Custom Azure AI Agents with CoPilot Studio and M365 CoPilot
Integrating Custom Agents with Copilot Studio and M365 Copilot In today's fast-paced digital world, integrating custom agents with Copilot Studio and M365 Copilot can significantly enhance your company's digital presence and extend your CoPilot platform to your enterprise applications and data. This blog will guide you through the integration steps of bringing your custom Azure AI Agent Service within an Azure Function App, into a Copilot Studio solution and publishing it to M365 and Teams Applications. When Might This Be Necessary: Integrating custom agents with Copilot Studio and M365 Copilot is necessary when you want to extend customization to automate tasks, streamline processes, and provide better user experience for your end-users. This integration is particularly useful for organizations looking to streamline their AI Platform, extend out-of-the-box functionality, and leverage existing enterprise data and applications to optimize their operations. Custom agents built on Azure allow you to achieve greater customization and flexibility than using Copilot Studio agents alone. What You Will Need: To get started, you will need the following: Azure AI Foundry Azure OpenAI Service Copilot Studio Developer License Microsoft Teams Enterprise License M365 Copilot License Steps to Integrate Custom Agents: Create a Project in Azure AI Foundry: Navigate to Azure AI Foundry and create a project. Select 'Agents' from the 'Build and Customize' menu pane on the left side of the screen and click the blue button to create a new agent. Customize Your Agent: Your agent will automatically be assigned an Agent ID. Give your agent a name and assign the model your agent will use. Customize your agent with instructions: Add your knowledge source: You can connect to Azure AI Search, load files directly to your agent, link to Microsoft Fabric, or connect to third-party sources like Tripadvisor. In our example, we are only testing the CoPilot integration steps of the AI Agent, so we did not build out additional options of providing grounding knowledge or function calling here. Test Your Agent: Once you have created your agent, test it in the playground. If you are happy with it, you are ready to call the agent in an Azure Function. Create and Publish an Azure Function: Use the sample function code from the GitHub repository to call the Azure AI Project and Agent. Publish your Azure Function to make it available for integration. azure-ai-foundry-agent/function_app.py at main · azure-data-ai-hub/azure-ai-foundry-agent Connect your AI Agent to your Function: update the "AIProjectConnString" value to include your Project connection string from the project overview page of in the AI Foundry. Role Based Access Controls: We have to add a role for the function app on OpenAI service. Role-based access control for Azure OpenAI - Azure AI services | Microsoft Learn Enable Managed Identity on the Function App Grant "Cognitive Services OpenAI Contributor" role to the System-assigned managed identity to the Function App in the Azure OpenAI resource Grant "Azure AI Developer" role to the System-assigned managed identity for your Function App in the Azure AI Project resource from the AI Foundry Build a Flow in Power Platform: Before you begin, make sure you are working in the same environment you will use to create your CoPilot Studio agent. To get started, navigate to the Power Platform (https://make.powerapps.com) to build out a flow that connects your Copilot Studio solution to your Azure Function App. When creating a new flow, select 'Build an instant cloud flow' and trigger the flow using 'Run a flow from Copilot'. Add an HTTP action to call the Function using the URL and pass the message prompt from the end user with your URL. The output of your function is plain text, so you can pass the response from your Azure AI Agent directly to your Copilot Studio solution. Create Your Copilot Studio Agent: Navigate to Microsoft Copilot Studio and select 'Agents', then 'New Agent'. Make sure you are in the same environment you used to create your cloud flow. Now select ‘Create’ button at the top of the screen From the top menu, navigate to ‘Topics’ and ‘System’. We will open up the ‘Conversation boosting’ topic. When you first open the Conversation boosting topic, you will see a template of connected nodes. Delete all but the initial ‘Trigger’ node. Now we will rebuild the conversation boosting agent to call the Flow you built in the previous step. Select 'Add an Action' and then select the option for existing Power Automate flow. Pass the response from your Custom Agent to the end user and end the current topic. My existing Cloud Flow: Add action to connect to existing Cloud Flow: When this menu pops up, you should see the option to Run the flow you created. Here, mine does not have a very unique name, but you see my flow 'Run a flow from Copilot' as a Basic action menu item. If you do not see your cloud flow here add the flow to the default solution in the environment. Go to Solutions > select the All pill > Default Solution > then add the Cloud Flow you created to the solution. Then go back to Copilot Studio, refresh and the flow will be listed there. Now complete building out the conversation boosting topic: Make Agent Available in M365 Copilot: Navigate to the 'Channels' menu and select 'Teams + Microsoft 365'. Be sure to select the box to 'Make agent available in M365 Copilot'. Save and re-publish your Copilot Agent. It may take up to 24 hours for the Copilot Agent to appear in M365 Teams agents list. Once it has loaded, select the 'Get Agents' option from the side menu of Copilot and pin your Copilot Studio Agent to your featured agent list Now, you can chat with your custom Azure AI Agent, directly from M365 Copilot! Conclusion: By following these steps, you can successfully integrate custom Azure AI Agents with Copilot Studio and M365 Copilot, enhancing you’re the utility of your existing platform and improving operational efficiency. This integration allows you to automate tasks, streamline processes, and provide better user experience for your end-users. Give it a try! Curious of how to bring custom models from your AI Foundry to your CoPilot Studio solutions? Check out this blogThe Future of AI: Harnessing AI agents for Customer Engagements
Discover how AI-powered agents are revolutionizing customer engagement—enhancing real-time support, automating workflows, and empowering human professionals with intelligent orchestration. Explore the future of AI-driven service, including Customer Assist created with Azure AI Foundry.The Future of AI: Autonomous Agents for Identifying the Root Cause of Cloud Service Incidents
Discover how Microsoft is transforming cloud service incident management with autonomous AI agents. Learn how AI-enhanced troubleshooting guides and agentic workflows are reducing downtime and empowering on-call engineers.Build recap: new Azure AI Foundry resource, Developer APIs and Tools
At Microsoft Build 2025, we introduced Azure AI Foundry resource, Azure AI Foundry API, and supporting tools to streamline the end-to-end development lifecycle of AI agents and applications. These capabilities are designed to help developers accelerate time-to-market; support production-scale workloads with scale and central governance; and support administrators with a self-serve capability to enable their teams’ experimentation with AI in a controlled environment. The Azure AI Foundry resource type unifies agents, models and tools under a single management grouping, equipped with built-in enterprise-readiness capabilities — such as tracing & monitoring, agent and model-specific evaluation capabilities, and customizable enterprise setup configurations tailored to your organizational policies like using your own virtual networks. This launch represents our commitment to providing organizations with a consistent, efficient and centrally governable environment for building and operating the AI agents and applications of today, and tomorrow. New platform capabilities The new Foundry resource type evolves our vision for Azure AI Foundry as a unified Azure platform-as-a-service offering, enabling developers to focus on building applications rather than managing infrastructure, while taking advantage of native Azure platform capabilities like Azure Data and Microsoft Defender. Previously, Azure AI Foundry portal’s capabilities required the management of multiple Azure resources and SDKs to build an end-to-end application. New capabilities include: Foundry resource type enables administrators with a consistent way of managing security and access to Agents, Models, Projects, and Azure tooling Integration. With this change, Azure Role Based Access Control, Networking and Policies are administered under a single Azure resource provider namespace, for streamlined management. ‘Azure AI Foundry’ is a renaming of the former ‘Azure AI Services’ resource type, with access to new capabilities. While Azure AI Foundry still supports bring-your-own Azure resources, we now default to a fully Microsoft-managed experience, making it faster and easier to get started. Foundry projects are folders that enable developers to independently create new environments for exploring new ideas and building prototypes, while managing data in isolation. Projects are child resources; they may be assigned their own admin controls but by default share common settings such as networking or connected resource access from their parent resource. This principle aims to take IT admins out of the day-to-day loop once security and governance are established at the resource level, enabling developers to self-serve confidently within their projects. Azure AI Foundry API is designed from the ground up, to build and evaluate API-first agentic applications, and lets you work across model providers agnostically with a consistent contract. Azure AI Foundry SDK wraps the Foundry API making it easy to integrate capabilities into code whether your application is built in Python, C#, JavaScript/TypeScript or Java. Azure AI Foundry for VS Code Extension complements your workflow with capabilities to help you explore models, and develop agents and is now supported with the new Foundry project type. New built-in RBAC roles provide up-to-date role definitions to help admins differentiate access between Administrator, Project Manager and Project users. Foundry RBAC actions follow strict control- and data plane separation, making it easier to implement the principle of least privilege. Why we built these new platform capabilities If you are already building with Azure AI Foundry -- these capabilities are meant to simplify platform management, enhance workflows that span multiple models and tools, and reinforce governance capabilities, as we see AI workloads grow more complex. The emergence of generative AI fundamentally changed how customers build AI solutions, requiring capabilities that span multiple traditional domains. We launched Azure AI Foundry to provide a comprehensive toolkit for exploring, building and evaluating this new wave of GenAI solutions. Initially, this experience was backed by two core Azure services -- Azure AI Services for accessing models including those from OpenAI, and Azure Machine Learning’s hub, to access tools for orchestration and customization. With the emergence of AI agents composing models and tools; and production workloads demanding the enforcement of central governance across those, we are investing to bring the management of agents, models and their tooling integration layer together to best serve these workload’s requirements. The Azure AI Foundry resource and Foundry API are purposefully designed to unify and simplify the composition and management of core building blocks of AI applications: Models Agents & their tools Observability, Security, and Trust In this new era of AI, there is no one-size-fits-all approach to building AI agents and applications. That's why we designed the new platform as a comprehensive AI factory with modular, extensible, and interoperable components. Foundry Project vs Hub-Based Project Going forward, new agents and model-centric capabilities will only land on the new Foundry project type. This includes access to Foundry Agent Service in GA and Foundry API. While we are transitioning to Azure AI Foundry as a managed platform service, hub-based project type remains accessible in Azure AI Foundry portal for GenAI capabilities that are not yet supported by the new resource type. Hub-based projects will continue to support use cases for custom model training in Azure Machine Learning Studio, CLI and SDK. For a full overview of capabilities supported by each project type, see this support matrix. Azure AI Foundry Agent Service The Azure AI Foundry Agent Service experience, now generally available, is powered by the new Foundry project. Existing customers exploring the GA experience will need the new AI Foundry resource. All new investments in the Azure AI Foundry Agent Service are focused on the Foundry project experience. Foundry projects act as secure units of isolation and collaboration — agents within a project share: File storage Thread storage (i.e. conversation history) Search indexes You can also bring your own Azure resources (e.g., storage, bring-your-own virtual network) to support compliance and control over sensitive data. Start Building with Foundry Azure AI Foundry is your foundation for scalable, secure, and production-grade AI development. Whether you're building your first agent or deploying a multi-agent workforce at Scale, Azure AI Foundry is ready for what’s next.
The Future of AI: Developing Code Assist – a Multi-Agent Tool
Discover how Code Assist, created with Azure AI Foundry Agent Service, uses AI agents to automate code documentation, generate business-ready slides, and detect security risks in large codebases—boosting developer productivity and project clarity.Start your Trustworthy AI Development with Safety Leaderboards in Azure AI Foundry
Selecting the right model for your AI application is more than a technical decision—it’s a foundational step in ensuring trust, compliance, and governance in AI. Today, we are excited to announce the public preview of safety leaderboards within Foundry model leaderboards, helping customers incorporate model safety as a first-class criterion alongside quality, cost, and throughput. This feature introduces three key components to support responsible AI development: A dedicated safety leaderboard highlighting the safest models; A quality–safety trade-off chart to balance performance and risk; Five new scenario-specific leaderboards supporting diverse responsible AI scenarios. Prioritize safety with the new leaderboard The safety leaderboard ranks the top models based on their robustness against generating harmful content. This is especially valuable in regulated or high-risk domains—such as healthcare, education, or financial services—where model outputs must meet high safety standards. To ensure benchmark rigor and relevance, we apply a structured filtering and validation process to select benchmarks. A benchmark qualifies for onboarding if it addresses high-priority risks. For safety and responsible AI leaderboards, we look at different benchmarks that can be considered reliable enough to provide some signals on the targeted areas of interest as they relate to safety. Our current safety leaderboard uses the HarmBench benchmark which includes prompts to illicit harmful behaviors from models. The benchmark covers 7 semantic categories of behaviors: Cybercrime & Unauthorized Intrusion Chemical & Biological Weapons/Drugs Copyright Violations Misinformation & Disinformation Harassment & Bullying Illegal Activities General Harm These 7 categories are organized into three broader functional groupings: Standard Harmful Behaviors Contextual Harmful Behaviors Copyright Violations Each grouping is featured in a separate responsible AI scenario leaderboard. We use the prompts evaluators from HarmBench to calculate Attack Success Rate (ASR) and aggregate them across the functional groupings to proxy model safety. Lower ASR values means that a model is more robust against attacks to illicit harmful content. We understand and acknowledge that model safety is a complex topic and has several dimensions. No single current open-source benchmark can test or represent the full spectrum of model safety in different scenarios. Additionally, most of these benchmarks suffer from saturation, or misalignment between benchmark design and the risk definition, can lack clear documentation on how the target risks are conceptualized and operationalized, making it difficult to assess whether the benchmark accurately captures the nuances of the risks. This can lead to either overestimating or underestimating model performance in real-world safety scenarios. While HarmBench dataset covers a limited set of harmful topics, it can still provide a high-level understanding of safety trends. Navigate trade-offs with the quality-safety chart Model selection often involves compromise across multiple criteria. Our new quality–safety trade-off chart helps you make informed decisions by comparing models based on their performance in safety and quality. You can: Identify the safest model measured by Attack Success Rate (lower is better) at a given level of quality performance; Or choose the highest-performing model in quality (higher is better) that still meets a defined safety threshold. Together with the quality-cost trade-off chart, you would be able to find the best trade-off between quality, safety, and cost in selecting a model: Scenario-based responsible AI leaderboards To support customers' diverse responsible AI scenarios, we have added 5 new leaderboards to rank the top models in safety and broader responsibility AI scenarios. Each leaderboard is powered by industry-standard public benchmarks covering: Model robustness against harmful behaviors using HarmBench in 3 scenarios, targeting standard harmful behaviors, contextually harmful behaviors, and copyright violations: Consistent with the safety leaderboard, lower ASR scores for a model mean better robustness against generating harmful content. Model ability to detect toxic content using the Toxigen benchmark: This benchmark targets adversarial and implicit hate speech detection. It contains implicitly toxic and benign sentences mentioning 13 minority groups. Higher accuracy based on F1-score for a model means its better ability to detect toxic content. Model knowledge of sensitive domains including cybersecurity, biosecurity, and chemical security, using the Weapons of Mass Destruction Proxy benchmark (WMDP): A higher accuracy score for a model denotes more knowledge of dangerous capabilities. These scenario leaderboards allow developers, compliance teams, and AI governance stakeholders to align model selection with organizational risk tolerance and regulatory expectations. Building Trustworthy AI Starts with the Right Tools With safety leaderboards now available in public preview, Foundry model leaderboards offer a unified, transparent, and data-driven foundation for selecting models that align with your safety requirements. This addition empowers teams to move from ad hoc evaluation to principled model selection—anchored in industry-standard benchmarks and responsible AI practices. To learn more, explore the methodology documentation and start building AI solutions you—and your stakeholders—can trust.DeepSeek-R1-0528 is now available on Azure AI Foundry
We’re excited to announce that DeepSeek-R1-0528, the latest evolution in the DeepSeek R1 open-source series of reasoning-optimized models, is now available on the Azure AI Foundry. According to DeepSeek, the R1-0528 model brings improved depth of reasoning and inferencing capabilities, and has demonstrated outstanding performance across various benchmark evaluations, approaching leading models such as OpenAI o3 and Gemini 2.5 Pro. In less than 36 hours, we’ve seen 4x growth in deployments of DeepSeek-R1-0528 compared to DeepSeek R1. Building on the foundation of DeepSeek-R1, this new release continues to push the boundaries of advanced reasoning and task decomposition. DeepSeek-R1-0528 integrates enhancements in chain-of-thought prompting, reinforcement learning fine-tuning, and broader multilingual understanding, making it a powerful tool for developers building intelligent agents, copilots, and research applications. Available within Azure AI Foundry, DeepSeek-R1-0528 is accessible on a trusted, scalable, and enterprise-ready platform, enabling businesses to seamlessly integrate advanced AI while meeting SLAs, security, and responsible AI commitments -all backed by Microsoft’s reliability and innovation. What’s new in DeepSeek-R1-0528? While maintaining the core strengths of its predecessor, DeepSeek-R1-0528 introduces: Improved reasoning depth through refined CoT (Chain-of-Thought) strategies. Expanded dataset coverage for better generalization across domains. Optimized inference performance for faster response times in production environments. New algorithmic optimization mechanisms during post-training. DeepSeek-R1-0528 is joining other direct from Azure models and it will be hosted and sold by Azure. Build Trustworthy AI Solutions with Azure AI Foundry As part of our ongoing commitment to help customers use and build AI that is trustworthy, meaning AI that is secure, safe and private, DeepSeek-R1-0528 has undergone Azure’s safety evaluations, including assessments of model behavior and automated security reviews to mitigate potential risks. With Azure AI Content Safety, built-in content filtering is available by default, with opt-out options for flexibility. We suggest using Azure AI Content Safety and conducting independent evaluations in production, as researchers have found DeepSeek-R1-0528 scoring lower than other models—though in line with DeepSeek-R1—on safety and jailbreak benchmarks. Get started today You can explore and deploy DeepSeek-R1-0528 directly from the Azure AI Foundry model catalog or integrate it into your workflows using the Azure AI SDK. The model is also available for experimentation via GitHub. Whether you're building a domain-specific assistant, a research prototype, or a production-grade AI system, DeepSeek-R1-0528 offers a robust foundation for your next breakthrough.
