Authorization and Governance for AI Agents: Runtime Authorization Beyond Identity at Scale
Designing Authorization‑Aware AI Agents at Scale Enforcing Runtime RBAC + ABAC with Approval Injection (JIT) Microsoft Entra Agent Identity enables organizations to govern and manage AI agent identities in Copilot Studio, improving visibility and identity-level control. However, as enterprises deploy multiple autonomous AI agents, identity and OAuth permissions alone cannot answer a more critical question: “Should this action be executed now, by this agent, for this user, under the current business and regulatory context?” This post introduces a reusable Authorization Fabric—combining a Policy Enforcement Point (PEP) and Policy Decision Point (PDP)—implemented as a Microsoft Entra‑protected endpoint using Azure Functions/App Service authentication. Every AI agent (Copilot Studio or AI Foundry/Semantic Kernel) calls this fabric before tool execution, receiving a deterministic runtime decision: ALLOW / DENY / REQUIRE_APPROVAL / MASK Who this is for Anyone building AI agents (Copilot Studio, AI Foundry/Semantic Kernel) that call tools, workflows, or APIs Organizations scaling to multiple agents and needing consistent runtime controls Teams operating in regulated or security‑sensitive environments, where decisions must be deterministic and auditable Why a V2? Identity is necessary—runtime authorization is missing Entra Agent Identity (preview) integrates Copilot Studio agents with Microsoft Entra so that newly created agents automatically get an Entra agent identity, manageable in the Entra admin center, and identity activity is logged in Entra. That solves who the agent is and improves identity governance visibility. But multi-agent deployments introduce a new risk class: Autonomous execution sprawl — many agents, operating with delegated privileges, invoking the same backends independently. OAuth and API permissions answer “can the agent call this API?” They do not answer “should the agent execute this action under business policy, compliance constraints, data boundaries, and approval thresholds?” This is where a runtime authorization decision plane becomes essential. The pattern: Microsoft Entra‑Protected Authorization Fabric (PEP + PDP) Instead of embedding RBAC logic independently inside every agent, use a shared fabric: PEP (Policy Enforcement Point): Gatekeeper invoked before any tool/action PDP (Policy Decision Point): Evaluates RBAC + ABAC + approval policies Decision output: ALLOW / DENY / REQUIRE_APPROVAL / MASK This Authorization Fabric functions as a shared enterprise control plane, decoupling authorization logic from individual agents and enforcing policies consistently across all autonomous execution paths. Architecture (POC reference architecture) Use a single runtime decision plane that sits between agents and tools. What’s important here Every agent (Copilot Studio or AI Foundry/SK) calls the Authorization Fabric API first The fabric is a protected endpoint (Microsoft Entra‑protected endpoint required) Tools (Graph/ERP/CRM/custom APIs) are invoked only after an ALLOW decision (or approval) Trust boundaries enforced by this architecture Agents never call business tools directly without a prior authorization decision The Authorization Fabric validates caller identity via Microsoft Entra Authorization decisions are centralized, consistent, and auditable Approval workflows act as a runtime “break-glass” control for high-impact actions This ensures identity, intent, and execution are independently enforced, rather than implicitly trusted. Runtime flow (Decision → Approval → Execution) Here is the runtime sequence as a simple flow (you can keep your Mermaid diagram too). ```mermaid flowchart TD START(["START"]) --> S1["[1] User Request"] S1 --> S2["[2] Agent Extracts Intent\n(action, resource, attributes)"] S2 --> S3["[3] Call /authorize\n(Entra protected)"] S3 --> S4 subgraph S4["[4] PDP Evaluation"] ABAC["ABAC: Tenant · Region · Data Sensitivity"] RBAC["RBAC: Entitlement Check"] Threshold["Approval Threshold"] ABAC --> RBAC --> Threshold end S4 --> Decision{"[5] Decision?"} Decision -->|"ALLOW"| Exec["Execute Tool / API"] Decision -->|"MASK"| Masked["Execute with Masked Data"] Decision -->|"DENY"| Block["Block Request"] Decision -->|"REQUIRE_APPROVAL"| Approve{"[6] Approval Flow"} Approve -->|"Approved"| Exec Approve -->|"Rejected"| Block Exec --> Audit["[7] Audit & Telemetry"] Masked --> Audit Block --> Audit Audit --> ENDNODE(["END"]) style START fill:#4A90D9,stroke:#333,color:#fff style ENDNODE fill:#4A90D9,stroke:#333,color:#fff style S1 fill:#5B5FC7,stroke:#333,color:#fff style S2 fill:#5B5FC7,stroke:#333,color:#fff style S3 fill:#E8A838,stroke:#333,color:#fff style S4 fill:#FFF3E0,stroke:#E8A838,stroke-width:2px style ABAC fill:#FCE4B2,stroke:#999 style RBAC fill:#FCE4B2,stroke:#999 style Threshold fill:#FCE4B2,stroke:#999 style Decision fill:#fff,stroke:#333 style Exec fill:#2ECC71,stroke:#333,color:#fff style Masked fill:#27AE60,stroke:#333,color:#fff style Block fill:#C0392B,stroke:#333,color:#fff style Approve fill:#F39C12,stroke:#333,color:#fff style Audit fill:#3498DB,stroke:#333,color:#fff ``` Design principle: No tool execution occurs until the Authorization Fabric returns ALLOW or REQUIRE_APPROVAL is satisfied via an approval workflow. Where Power Automate fits (important for readers) In most Copilot Studio implementations, Agents calls Power Automate (agent flows), is the practical integration layer that calls enterprise services and APIs. Copilot Studio supports “agent flows” as a way to extend agent capabilities with low-code workflows. For this pattern, Power Automate typically: acquires/uses the right identity context for the call (depending on your tenant setup), and calls the /authorize endpoint of the Authorization Fabric, returns the decision payload to the agent for branching. Copilot Studio also supports calling REST endpoints directly using the HTTP Request node, including passing headers such as Authorization: Bearer <token>. Protected endpoint only: Securing the Authorization Fabric with Microsoft Entra For this V2 pattern, the Authorization Fabric must be protected using Microsoft Entra‑protected endpoint on Azure Functions/App Service (built‑in auth). Microsoft Learn provides the configuration guidance for enabling Microsoft Entra as the authentication provider for Azure App Service / Azure Functions. Step 1 — Create the Authorization Fabric API (Azure Function) Expose an authorization endpoint: HTTP Step 2 — Enable Microsoft Entra‑protected endpoint on the Function App In Azure Portal: Function App → Authentication Add identity provider → Microsoft Choose Workforce configuration (enterprise tenant) Set Require authentication for all requests This ensures the Authorization Fabric is not callable without a valid Entra token. Step 3 — Optional hardening (recommended) Depending on enterprise posture, layer: IP restrictions / Private endpoints APIM in front of the Function for rate limiting, request normalization, centralized logging (For a POC, keep it minimal—add hardening incrementally.) Externalizing policy (so governance scales) To make this pattern reusable across multiple agents, policies should not be hardcoded inside each agent. Instead, store policy definitions in a central policy store such as Cosmos DB (or equivalent configuration store), and have the PDP load/evaluate policies at runtime. Why this matters: Policy changes apply across all agents instantly (no agent republish) Central governance + versioning + rollback becomes possible Audit and reporting become consistent across environments (For the POC, a single JSON document per policy pack in Cosmos DB is sufficient. For production, add versioning and staged rollout.) Store one PolicyPack JSON document per environment (dev/test/prod). Include version, effectiveFrom, priority for safe rollout/rollback. Minimal decision contract (standard request / response) To keep the fabric reusable across agents, standardize the request payload. Request payload (example) Decision response (deterministic) Example scenario (1 minute to understand) Scenario: A user asks a Finance agent to create a Purchase Order for 70,000. Even if the user has API permission and the agent can technically call the ERP API, runtime policy should return: REQUIRE_APPROVAL (threshold exceeded) trigger an approval workflow execute only after approval is granted This is the difference between API access and authorized business execution. Sample Policy Model (RBAC + ABAC + Approval) This POC policy model intentionally stays simple while demonstrating both coarse and fine-grained governance. 1) Coarse‑grained RBAC (roles → actions) FinanceAnalyst CreatePO up to 50,000 ViewVendor FinanceManager CreatePO up to 100,000 and/or approve higher spend 2) Fine‑grained ABAC (conditions at runtime) ABAC evaluates context such as region, classification, tenant boundary, and risk: 3) Approval injection (Agent‑level JIT execution) For higher-risk/high-impact actions, the fabric returns REQUIRE_APPROVAL rather than hard deny (when appropriate): How policies should be evaluated (deterministic order) To ensure predictable and auditable behavior, evaluate in a deterministic order: Tenant isolation & residency (ABAC hard deny first) Classification rules (deny or mask) RBAC entitlement validation Threshold/risk evaluation Approval injection (JIT step-up) This prevents approval workflows from bypassing foundational security boundaries such as tenant isolation or data sovereignty. Copilot Studio integration (enforcing runtime authorization) Copilot Studio can call external REST APIs using the HTTP Request node, including passing headers such as Authorization: Bearer <token> and binding response schema for branching logic. Copilot Studio also supports using flows with agents (“agent flows”) to extend capabilities and orchestrate actions. Option A (Recommended): Copilot Studio → Agent Flow (Power Automate) → Authorization Fabric Why: Flows are a practical place to handle token acquisition patterns, approval orchestration, and standardized logging. Topic flow: Extract user intent + parameters Call an agent flow that: calls /authorize returns decision payload Branch in the topic: If ALLOW → proceed to tool call If REQUIRE_APPROVAL → trigger approval flow; proceed only if approved If DENY → stop and explain policy reason Important: Tool execution must never be reachable through an alternate topic path that bypasses the authorization check. Option B: Direct HTTP Request node to Authorization Fabric Use the Send HTTP request node to call the authorization endpoint and branch using the response schema. This approach is clean, but token acquisition and secure secretless authentication are often simpler when handled via a managed integration layer (flow + connector). AI Foundry / Semantic Kernel integration (tool invocation gate) For Foundry/SK agents, the integration point is before tool execution. Semantic Kernel supports Azure AI agent patterns and tool integration, making it a natural place to enforce a pre-tool authorization check. Pseudo-pattern: Agent extracts intent + context Calls Authorization Fabric Enforces decision Executes tool only when allowed (or after approval) Telemetry & audit (what Security Architects will ask for) Even the best policy engine is incomplete without audit trails. At minimum, log: agentId, userUPN, action, resource decision + reason + policyIds approval outcome (if any) correlationId for downstream tool execution Why it matters: you now have a defensible answer to: “Why did an autonomous agent execute this action?” Security signal bonus: Denials, unusual approval rates, and repeated policy mismatches can also indicate prompt injection attempts, mis-scoped agents, or governance drift. What this enables (and why it scales) With a shared Authorization Fabric: Avoid duplicating authorization logic across agents Standardize decisions across Copilot Studio + Foundry agents Update governance once (policy change) and apply everywhere Make autonomy safer without blocking productivity Closing: Identity gets you who. Runtime authorization gets you whether/when/how. Copilot Studio can automatically create Entra agent identities (preview), improving identity governance and visibility for agents. But safe autonomy requires a runtime decision plane. Securing that plane as an Entra-protected endpoint is foundational for enterprise deployments. In enterprise environments, autonomous execution without runtime authorization is equivalent to privileged access without PIM—powerful, fast, and operationally risky.Microsoft BizTalk Server Product Lifecycle Update
For more than 25 years, Microsoft BizTalk Server has supported mission-critical integration workloads for organizations around the world. From business process automation and B2B messaging to connectivity across industries such as financial services, healthcare, manufacturing, and government, BizTalk Server has played a foundational role in enterprise integration strategies. To help customers plan confidently for the future, Microsoft is sharing an update to the BizTalk Server product lifecycle and long-term support timelines. BizTalk Server 2020 will be the final version of BizTalk Server. Guidance to support long-term planning for mission-critical workloads This announcement does not change existing support commitments. Customers can continue to rely on BizTalk Server for many years ahead, with a clear and predictable runway to plan modernization at a pace that aligns with their business and regulatory needs. Lifecycle Phase End Date What’s Included Mainstream Support April 11, 2028 Security + non-security updates and Customer Service & Support (CSS) support Extended Support April 9, 2030 CSS support, Security updates, and paid support for fixes (*) End of Support April 10, 2030 No further updates or support (*) Paid Extended Support will be available for BizTalk Server 2020 between April 2028 and April 2030 for customers requiring hotfixes for non-security updates. CSS will continue providing their typical support. BizTalk Server 2016 is already out of mainstream support, and we recommend those customers evaluate a direct modernization path to Azure Logic Apps. Continued Commitment to Enterprise Integration Microsoft remains fully committed to supporting mission-critical integration, including hybrid connectivity, future-ready orchestration, and B2B/EDI modernization. Azure Logic Apps, part of Azure Integration Services — which includes API Management, Service Bus, and Event Grid — delivers the comprehensive integration platform for the next decade of enterprise connectivity. Host Integration Server: Continued Support for Mainframe Workloads Host Integration Server (HIS) has long provided essential connectivity for organizations with mainframe and midrange systems. To ensure continued support for those workloads, Host Integration Server 2028 will ship as a standalone product with its own lifecycle, decoupled from BizTalk Server. This provides customers with more flexibility and a longer planning horizon. Recognizing Mainframe modernization customers might be looking to integrate with their mainframes from Azure, Microsoft provides Logic Apps connectors for mainframe and midrange systems, and we are keen on adding more connectors in this space. Let us know about your HIS plans, and if you require specific features for Mainframe and midranges integration from Logic Apps at: https://aka.ms/lamainframe Azure Logic Apps: The Successor to BizTalk Server Azure Logic Apps, part of Azure Integration Services, is the modern integration platform that carries forward what customers value in BizTalk while unlocking new innovation, scale, and intelligence. With 1,400+ out-of-box connectors supporting enterprise, SaaS, legacy, and mainframe systems, organizations can reuse existing BizTalk maps, schemas, rules, and custom code to accelerate modernization while preserving prior investments including B2B/EDI and healthcare transactions. Logic Apps delivers elastic scalability, enterprise-grade security and compliance, and built-in cost efficiency without the overhead of managing infrastructure. Modern DevOps tooling, Visual Studio Code support, and infrastructure-as-code (ARM/Bicep) ensure consistent, governed deployments with end-to-end observability using Azure Monitor and OpenTelemetry. Modernizing Logic Apps also unlocks agentic business processes, enabling AI-driven routing, predictive insights, and context-aware automation without redesigning existing integrations. Logic Apps adapts to business and regulatory needs, running fully managed in Azure, hybrid via Arc-enabled Kubernetes, or evaluated for air-gapped environments. Throughout this lifecycle transition, customers can continue to rely on the BizTalk investments they have made while moving toward a platform ready for the next decade of integration and AI-driven business. Charting Your Modernization Path Microsoft remains fully committed to supporting customers through this transition. We recognize that BizTalk systems support highly customized and mission-critical business operations. Modernization requires time, planning, and precision. We hope to provide: Proven guidance and recommended design patterns A growing ecosystem of tooling supporting artifact reuse Unified Support engagements for deep migration assistance A strong partner ecosystem specializing in BizTalk modernization Potential incentive programs to help facilitate migration for eligible customers (details forthcoming) Customers can take a phased approach — starting with new workloads while incrementally modernizing existing BizTalk deployments. We’re Here to Help Migration resources are available today: Overview: https://aka.ms/btmig Best practices: https://aka.ms/BizTalkServerMigrationResources Video series: https://aka.ms/btmigvideo Feature request survey: https://aka.ms/logicappsneeds Reactor session: Modernizing BizTalk: Accelerate Migration with Logic Apps - YouTube Migration Agent (Complete refactoring from BizTalk to Logic Apps): Bringing all your Integration workloads to Logic Apps Standard | Microsoft Community Hub We encourage customers to engage their Microsoft accounts team early to assess readiness, identify modernization opportunities, and explore assistance programs. Your Modernization Journey Starts Now BizTalk Server has played a foundational role in enterprise integration success for more than two decades. As you plan ahead, Microsoft is here to partner with you every step of the way, ensuring operational continuity today while unlocking innovation tomorrow. To begin your transition, please contact your Microsoft account team or visit our migration hub. Thank you for your continued trust in Microsoft and BizTalk Server. We look forward to partnering closely with you as you plan the future of your integration platforms. Frequently Asked Questions Do I need to migrate now? No. BizTalk Server 2020 is fully supported through April 11, 2028, with paid Extended Support available through April 9, 2030, for non-security hotfixes. CSS will continue providing their typical support. You have a long and predictable runway to plan your transition. Will there be a new BizTalk Server version? No. BizTalk Server 2020 is the final version of the product. What happens after April 9, 2030? BizTalk Server will reach End of Support, and security updates or technical assistance will no longer be provided. Workloads will continue running but without Microsoft servicing. Is paid support available past 2028? Yes. Paid extended support will be available through April 2030 for BizTalk Server 2020 customers looking for non-security hotfixes. CSS will continue to provide the typical support. What is the end of sale date for BizTalk Server? We will announce an end of sale date for BizTalk Server on July 2026. What about BizTalk Server 2016 or earlier versions? Those versions are already out of mainstream support. We strongly encourage moving directly to Logic Apps rather than upgrading to BizTalk Server 2020. Will Host Integration Server continue? Yes. Host Integration Server (HIS) 2028 will be released as a standalone product with its own lifecycle and support commitments. Can I reuse BizTalk Server artifacts in Logic Apps? Yes. Most of BizTalk maps, schemas, rules, assemblies, and custom code can be reused with minimal effort using Microsoft and partner migration tooling. We welcome feature requests here: https://aka.ms/logicappsneeds Does modernization require moving fully to the cloud? No. Logic Apps supports hybrid deployments for scenarios requiring local processing or regulatory compliance, and fully disconnected environments are under evaluation. More information of the Hybrid deployment model here: https://aka.ms/lahybrid. Does modernization unlock AI capabilities? Yes. Logic Apps enables AI-driven automations through Agent Loop, improving routing, decisioning, and operational intelligence. Where do I get planning support? Your Microsoft account team can assist with assessment and planning. Migration resources are also linked in this announcement to help you get started. Microsoft Corporation
Docker Engine v29 on Linux: Why data-root No Longer Prevents OS Disk Growth (and How to Fix It)
Scope Applies to Linux hosts only Does not apply to Windows or Docker Desktop Problem Summary After upgrading to Docker Engine v29 or reimaging Linux nodes with this version, you may observe unexpected growth on the OS disk, even when Docker is configured with a custom data-root pointing to a mounted data disk. This commonly affects cloud environments (VMSS, Azure Batch, self‑managed Linux VMs) where the OS disk is intentionally kept small and container data is expected to reside on a separate data disk. What Changed in Docker Engine v29 (Linux) Starting with Docker Engine 29.0, containerd’s image store becomes the default storage backend on fresh installations. Docker explicitly documents this behavior: “The containerd image store is the default storage backend for Docker Engine 29.0 and later on fresh installations.” Docker containerd image store documentation Key points on Linux: Docker now delegates image and snapshot storage to containerd containerd uses its own content store and snapshotters Docker’s traditional data-root setting no longer controls all container storage Docker Engine v29 was released on 11 November 2025, and this behavior is by design, not a regression. Where Disk Usage Goes on Linux Docker’s daemon documentation clarifies the split: Legacy storage (pre‑v29 or upgraded installs): All data under /var/lib/docker Docker Engine v29 (containerd image store enabled): Images & snapshots → /var/lib/containerd Other Docker data (volumes, configs, metadata) → /var/lib/docker Crucially: “The data-root option does not affect image and container data stored in /var/lib/containerd when using the containerd image store.” Docker daemon data directory documentation This explains why OS disk usage continues to grow even when data-root is set to a data disk. Why the Old Configuration Worked Before On earlier Docker versions, Docker fully managed image and snapshot storage. Configuring: { "data-root": "/mnt/docker-data" } Was sufficient to redirect all container storage off the OS disk. With Docker Engine v29: containerd owns image and snapshot storage data-root only affects Docker‑managed data OS disk growth after upgrades or reimages is expected behavior This aligns fully with Docker’s documented design changes. Linux Workaround: Redirect containerd Storage To restore the intended behavior on Linux, keeping both Docker and containerd storage on the mounted data disk, containerd’s storage path must also be redirected. A practical workaround is to relocate /var/lib/containerd using a symbolic link. Example (Linux) sudo systemctl stop docker.socket docker containerd || true; sudo mkdir -p /mnt/docker-data /mnt/containerd; sudo rm -rf /var/lib/containerd; sudo ln -s /mnt/containerd /var/lib/containerd; echo "{\"data-root\": \"/mnt/docker-data\"}" | sudo tee /etc/docker/daemon.json; sudo systemctl daemon-reload; sudo systemctl start containerd docker' What This Does Stops Docker and containerd Creates container storage directories on the mounted data disk Redirects /var/lib/containerd → /mnt/containerd Keeps Docker’s data-root at /mnt/docker-data Restarts services with a unified storage layout This workaround is effective because it explicitly accounts for containerd‑managed paths introduced in Docker Engine v29, restoring the behavior that existed prior to the change. Key Takeaways Docker Engine v29 introduces a fundamental storage architecture change on Linux data-root alone is no longer sufficient OS disk growth after upgrades or reimages is expected containerd storage must also be redirected The workaround aligns with Docker’s official documentation and design References Docker daemon data directory https://docs.docker.com/engine/daemon/ containerd image store (Docker Engine v29) https://docs.docker.com/engine/storage/containerd/ Docker Engine v29 release notes https://docs.docker.com/engine/release-notes/29/AI Gateway tier of API Management now in public preview
Today, we are introducing the AI Gateway tier of Azure API Management, now in public preview. It gives platform teams a purpose-built experience built specifically for AI workloads - publishing and governing models and MCP servers. Controls are configured through policy cards rather than XML and expressions, and the portal experience and control plane are structured around models, MCP servers, and tools rather than APIs. (For brevity, we refer to the AI Gateway tier as AI Gateway throughout the rest of this article.) AI Gateway is built on Azure API Management, bringing proven operational capabilities to AI workloads. The resource runs in your subscription, uses your Entra tenant, and sends telemetry to destinations you control. The operating model will be familiar to existing API Management customers, but the interface is built around AI workloads. The AI Gateway tier is intended for teams that want this focused experience; other API Management tiers remain the right choice when organizations also need general-purpose API management or capabilities not included in the AI Gateway experience. A practical model for platform teams The AI Gateway gives platform teams a shared place to manage models, MCP servers, policies, and observability destinations, with access controlled through Azure RBAC. For example, a central platform group can connect a set of approved models and tools and publish them for application teams. The application teams can test those assets in the test console and build against them without routing every change through the central group. The platform group still owns the shared guardrails and can see how the assets are being used. After an asset is published, developers can create a named runtime key and begin calling the gateway immediately. Bring the models and tools you already use Most organizations don't standardize on a single model provider. Different models are selected based on quality, latency, cost, geography, or specialized capabilities. The preview supports models from Microsoft Foundry including OpenAI, Anthropic, Mistral, and other Foundry hosted models, as well as models hosted in AWS Bedrock, Google Vertex AI, OpenAI, and Anthropic. A guided wizard simplifies importing models from Microsoft Foundry. Other providers can be added by configuring a connection, with backend authentication configured as part of that connection. All published models are available under the same stable endpoint. Applications continue to use supported API formats such as OpenAI Chat Completions and Responses or Anthropic Messages directly or via SDKs. The AI Gateway extends governance beyond models to the MCP servers and tools agents use to interact with enterprise systems. You can expose an existing MCP server over SSE or Streamable HTTP, turn all or selected operations from a REST API into an MCP server by uploading its OpenAPI specification, or use more than 1,400 connector-backed tools from the Power Platform and Logic Apps library. You can also federate multiple MCP servers behind a single server, so an agent connects once and sees the tools across those servers. Backend authentication supports an API key, OAuth client credentials, managed identity, or mTLS. Governance that's built in Organizations need consistent governance across models and MCP servers without requiring every application team to implement those capabilities independently. The AI Gateway portal presents governance policies through an intuitive card-based experience rather than requiring policy XML. The same policies are expressed as JSON properties, making them easy to manage as infrastructure as code and to audit and enforce across a fleet with Azure Policy. In the public preview, those cards cover request and token rate limits, token quotas, Azure AI Content Safety, and fallback to a secondary model. Policies are applied per asset, making it clear which controls protect each model or MCP server. OpenTelemetry-based token metrics The AI Gateway emits token-usage metrics through OpenTelemetry, with attributes following GenAI and cloud semantic conventions. Metrics can be sent to Application Insights, Datadog, Splunk, Grafana Cloud, or another OTLP endpoint. The portal provides a monitoring view over Application Insights data. Better together: Microsoft Foundry and AI Gateway With AI Gateway, teams can extend the same governance controls, for example token rate limits and quotas, across models hosted in Microsoft Foundry and models hosted elsewhere. Foundry and non-Foundry models are published through gateway-managed endpoints, giving applications and agents a consistent way to access governed models regardless of where they are hosted. Foundry-hosted agents can consume curated sets of tools from Foundry toolboxes, with access to the underlying MCP servers and APIs governed through AI Gateway. Together, Microsoft Foundry and AI Gateway cover the enterprise application lifecycle: Foundry for building and running AI applications, and AI Gateway for publishing, governing, and observing models, tools, and MCP servers across your AI estate. The new AI Gateway tier will soon be available through the gateway experience in Microsoft Foundry portal. We are working toward a seamless, integrated AI Gateway experience within Foundry portal and will share more about that work separately. Available today in public preview The AI Gateway tier is available today at no cost in public preview in East US 2 and Sweden Central. Pricing will be shared separately. To provision a resource, add a model or MCP server, and make a first call click this to go to the AI Gateway tier portal and try it. If you prefer to start from code, use a sample to deploy all the required resources for a Foundry-hosted agent configured to access its model and tools through AI Gateway. We look forward to your feedback as we continue to rapidly evolve AI Gateway.Unable to Uninstall Security Update (KB5094128) on Azure Hosted Windows Server 2022 VM
Hi All, I'm building up a system on Azure of Windows Server 2022 VMs for a PoC. Each Windows Server 2022 VM has been created using a company hardened golden image. After configuring the VM successfully, I am unable to run a software. Through research, the solution to run the software is to uninstall 'Security Update for Microsoft Windows (KB5094128)'. The issue is that I cannot uninstall the update normally. I have looked up various solutions and the following have been unsuccessful: Attempt #1 Powershell (Admin): sfc /scannow wusa /uninstall /kb:5094128 Result: Error 0x800f0905. Attempt #2 Powershell (Admin): Dism /Online /Cleanup-Image /CheckHealth Dism /Online /Cleanup-Image /ScanHealth Dism /Online /Cleanup-Image /RestoreHealth wusa /uninstall /kb:5094128 Result: No corruption detected, error 0x800f0905. Attempt #3 Powershell (Admin): net stop wuauserv net stop cryptSvc net stop bits net stop msiserver ren C:\Windows\SoftwareDistribution SoftwareDistribution.old ren C:\Windows\System32\catroot2 catroot2.old net start wuauserv net start cryptSvc net start bits net start msiserver wusa /uninstall /kb:5094128 Result: All services started and stopped. Error error 0x800f0905. Attempt #4 Windows Update Troubleshooter: Running the windows update troubleshooter gives the following output. The event viewer is suggesting there is a corrupted file error, but as seen above nothing is wanting to remove that file. Does anyone have any other options they could suggest? Or has anyone had success with this issue before? Is it a lost cause as we have to use company hardened golden images? Any suggestions would be appreciated!Enabling the Compliance Security Profile (CSP) for HIPAA on Azure Databricks
Microsoft Architect's: Aladdin Alchalabi aalchalabi, Kiran Raja KiranRaja, Peter Lenges PeterLenges, Jessica Reece jareece, Benjamin Coughtry bcoughtry, Anishek Kamal anishekkamal, Tayo Akigbogun takigbogun, Eric Kwashie ekwashie, Peter Lo PeterLo and Rafia Aqil Rafia_Aqil Peer Reviewed: Ted Kim tedkim and Arvind Periyasamy ArvindPeriyasamy Purpose and WHY Azure Databricks has put in place controls to meet the unique compliance needs of highly regulated industries. The requirement for the compliance security profile (CSP) is a joint effort between Microsoft and Databricks for Azure-Databricks workspaces. The value proposition of the compliance security profile is that it provides Customers significantly more hardening and security features. Mandatory Deadline: The Compliance Security Profile (CSP) becomes mandatory for processing HIPAA, HITRUST, and IRAP regulated data on Azure-Databricks by September 1, 2026. Enabling CSP on Workspaces These requirements are checked and enforced on new workspaces today, with enforcement on existing workspaces expected in the future; where prerequisites are missing, clusters may fail to start. Prerequisite Requirement Costs There is a 10% cost of the Azure Databricks product spend within each workspace where CSP is enabled. **Review with your account team for any grace period during which the Enhanced Security & Compliance (ESC) add-on is available at no charge. After the grace period ends, a 10% DBU upcharge applies. Enhanced Security & Compliance add-on For existing workspaces: From Azure portal, click the Settings > Security & compliance on an existing Azure Databricks workspace: **Review Note #2 below Azure VNet encryption Azure Virtual Network encryption must be enabled on the Azure Databricks workspace VNet. Infrastructure as code: Update the encryption block on your VNet resource. In Terraform, that's azurerm_virtual_network. Azure portal: Toggle encryption on the VNet (Overview → Properties → Encryption). Command line: Enable it with the Azure CLI or PowerShell. **Review Note #4 below Supported VM instance types Use a VM series that supports VNet encryption and verify compatibility before enabling the profile. **This does not apply to serverless compute. NOTE: Confirm your workspace is using Premium Pricing tier. The profile can be enabled when a workspace is created or on an existing workspace, through the Azure portal, the Azure CLI, PowerShell, an ARM template, or Terraform. Only the Public Preview, Private Preview, and Beta features listed in this section are supported for workspaces with the compliance security profile enabled: Compliance security profile - Azure Databricks | Microsoft Learn Currently, the compliance security profile checks and enforces only the use of specific VM instance types, not the enablement of Azure Virtual Network encryption. Enforcement of the Azure Virtual Network encryption requirement begins on February 1, 2027, including on workspaces that already have the compliance security profile enabled. This flexibility shall allow customers more time to set up VNET encryption. This has been updated in documentation today (See ‘Important’ box). Regarding rollback, CSP can be reversed via a support ticket, if no regulated data has been processed on a particular workspace. A closer look at VNet encryption CSP is enabled per Databricks workspace, but VNet encryption is applied at the VNet level. Enabling it for a Databricks workload therefore affects every resource within that VNet, not just the workspace. A common approach in a hub-and-spoke design is to leave the hub VNet unencrypted and encrypt only the spoke VNet. The hub typically holds shared services such as the DNS resolver, while the spoke hosts the Databricks workspaces that require CSP. What does it mean for a VNet to be encrypted? An encrypted VNet is a security measure that protects VM-to-VM traffic. Data is encrypted in transit through a DTLS tunnel. This is platform-level encryption, applied automatically to traffic within your VNet and across peered VNets. It requires no changes to your operating system or applications. What happens to my VM-to-VM traffic? Qualifying VM-to-VM traffic is encrypted. Traffic involving unqualified instances simply keeps flowing unencrypted. The only enforcement available today is AllowUnencrypted. Important clarifications Encrypting the VNet does not guarantee all traffic within it will be encrypted. The only traffic that gets encrypted is VM-to-VM traffic where both the source and destination VMs are (1) on a supported SKU and (2) have Accelerated Networking enabled on the network interface. Encrypting the VNet does not drop or break traffic from unsupported SKUs. The only supported GA setting today is to allow unencrypted traffic, so non-qualifying traffic is still permitted; it just isn't encrypted. A future DropUnencrypted setting will drop that traffic instead for further hardening. It isn't available yet, and it's currently unknown whether it will become a required setting for CSP. Review the following recommended steps The steps below represent a validated implementation pattern. The exact network design can vary by environment, but the same prerequisite, isolation, and end-to-end validation principles should be applied. Validated implementation step Recommended approach and expected outcome Isolated sandbox workspace Enable CSP first in a representative non-production workspace. This avoids irreversible changes to DEV or production while the network topology, dependencies, VM compatibility, and operational behavior are validated. Enable CSP and select HIPAA Enable the Compliance Security Profile and select HIPAA under Settings > Security & compliance before processing PHI after September 1, 2026. Enable VNet encryption Enable VNet encryption. **Review Azure Virtual Network encryption limitations: What is Azure Virtual Network encryption? - Azure Virtual Network | Microsoft Learn Start a classic cluster Confirm that a classic cluster starts successfully after CSP and VNet encryption prerequisites are applied. This validates that the selected compute path and VM types remain operational. Validate storage connectivity Confirm storage connectivity continue to work. Confirm rollout readiness Proceed to DEV and production only after the complete private connectivity path, cluster startup, storage access, DNS resolution, data pipelines, and performance have been validated from end to end. Things to Review Enablement is permanent Enabling the compliance security profile, or adding a compliance standard, is intended to be a permanent change. You cannot remove the profile or an individual standard from a workspace that has ever processed regulated data; to revert, you must delete the workspace and create a new one. Validate the configuration in an isolated, representative non-production workspace before enabling DEV or production. Inventory and Assessment Identify Regulated Workspaces: Catalogue all existing Azure-Databricks workspaces. Determine which ones currently process, or are planned to process, data subject to HIPAA, HITRUST, or IRAP. Review Data Pipelines: Map out all data ingress and egress points for these identified workspaces, including connections to on-premises data sources, other cloud services, and external APIs. This helps identify potential network impacts. Verify Prerequisites Before Rollout: Confirm that selected VM instance types support VNet encryption and that every required CSP and networking setting is in place, because missing prerequisites can prevent clusters from starting. Enablement Method: Choose the appropriate tooling for enablement of Azure Portal, Azure CLI, PowerShell, ARM templates, or Terraform to ensure consistency and automation. Keep sensitive data out of customer-defined fields You are solely responsible for ensuring that PHI or other sensitive information is never entered into customer-defined input fields. These include workspace names, compute and resource names, tags, job names, job run names, network names, credential names, storage account names, and Git repository IDs or URLs, all of which may be stored, processed, or accessed outside the compliance boundary. What Changes After Enabling Compliance Security Profile On CSP-enabled workspaces, Partner-powered AI features are disabled by default and some assistive features such as Genie Code are also disabled; a workspace admin can re-enable them if required. In addition, only the specific preview features listed in the compliance security profile documentation are supported. No other Public Preview, Private Preview, or Beta feature may be used to process regulated data. Compliance Security Profile (CSP) enhances the security posture of Azure Databricks by enabling a hardened compute image, enhanced security monitoring, and automatic cluster updates. With automatic cluster updates enabled, classic compute resources are periodically updated and may restart during configured maintenance windows, so production schedules should be planned accordingly. Enhanced security monitoring deploys security monitoring agents on supported compute resources and generates logs that security teams can ingest and analyze. When deploying through ARM templates, CSP, enhancedSecurityMonitoring, and automaticClusterUpdate are configurable security and compliance settings that can be specified as part of the workspace deployment. References Compliance security profile: https://learn.microsoft.com/en-us/azure/databricks/security/privacy/security-profile Configure enhanced security and compliance settings: https://learn.microsoft.com/en-us/azure/databricks/security/privacy/enhanced-security-compliance HIPAA, Azure Databricks, Microsoft Learn: https://learn.microsoft.com/en-us/azure/databricks/security/privacy/hipaa What is Azure Virtual Network encryption: https://learn.microsoft.com/en-us/azure/virtual-network/virtual-network-encryption-overview Create a Virtual Network with encryption: https://learn.microsoft.com/en-us/azure/virtual-network/how-to-create-encryption?tabs Hashicorp azurerm_virtual_network: azurerm_virtual_network | Resources | hashicorp/azurerm | Terraform | Terraform RegistryZonal Resiliency in Azure: Application-Centric Goals, Recovery Plans, and Drills
Hello Folks If you have ever stared at a multi-tier app in Azure and asked yourself, “Is this actually going to survive a zone outage?”, you are not alone. In session MAIS23 of the Microsoft Azure Infra Summit 2026, Bhavya, Aditya, and Chaya from the Azure Resiliency product team walked us through the new Resiliency in Azure experiences (formerly Azure Business Continuity Center) and showed how to stop treating resiliency as a per-resource checkbox and start treating it as an application-level outcome. Why IT Pros Should Care Most of us have lived this story. An app is “in the cloud”, spread across IaaS VMs, PaaS databases, an app service plan, and a shared Azure Firewall managed by some other team. Then a zonal blip hits, and suddenly nobody can answer the simple question: was this app supposed to be zone resilient or not? The session opened with a customer scenario called Zava, a fast-growing insurance company running a claims app at 99.9 percent availability that just lost more than $40,000 in revenue in one week because of zonal outages. That is the price tag the speakers put on the problem, and it lines up with the patterns I see every week. Here is why this matters to IT pros: You finally get a single pane to see zonal resiliency posture across IaaS, PaaS, and shared services. Resiliency goals are set at the application level, not buried inside each resource blade. You get tailored Azure Advisor recommendations plus an Azure Copilot guided flow that emits remediation scripts. You can run zone-down drills powered by Azure Chaos Studio without stitching together five different tools. Recovery plans orchestrate failover in a defined order, with on-demand readiness checks before the next real outage. In short, less guessing, less spreadsheet bookkeeping, and a lot more confidence that the app will behave the way you told the business it would. What Resiliency in Azure Does, a Technical Overview The team has rebranded Azure Business Continuity Center to Resiliency in Azure. It is a unified solution that covers infra, data, and cyber resiliency in one place. Today the focus is zonal resiliency, with regional disaster recovery (and proper RPO/RTO goals) on the roadmap. The central concept is the service group. A service group is a logical application unit that can span subscriptions and resource groups. You add the VMs, databases, app service plans, Redis caches, and other Azure resources that make up an application, and from that point on, resiliency operations work against the whole app, not one resource at a time. There are two views you will spend most of your time in: Resource resiliency, a zonal configuration summary across the (roughly 20) resource types supported today. Service group resiliency, the same summary but pivoted to the application level, so you can prioritize the apps that need attention first. The speakers were honest about scope. Goals today are a simple intent (“this service group should be evaluated for zonal resilience”). Once additional pillars like regional DR ship, goals will expand to include RPO and RTO targets. I appreciate that they did not oversell it. How It Works, Under the Hood Once a service group exists, the workflow has three big building blocks. Each one solves a problem I bet you have hit. Goals and recommendations. You assign a zonal resiliency goal to the service group, and Azure Advisor surfaces tailored recommendations for the resources inside it. Two details I liked: The view shows cost implications before you flip the switch. Some Azure services have no cost delta for zone redundancy. Others do. You see it inline, not in a separate calculator tab. There is an Azure Copilot guided remediation flow that walks you through the recommendation and, at the end, emits a script. That script accounts for resource-type corner cases (SKU changes, redeploys, and so on) and is meant to be run through your automation pipeline. You can also exclude a resource with a reason (“not critical, zonal redundancy not required”) or manually attest a resource when your own custom solution already provides resiliency that the platform cannot auto-detect. That escape hatch is important, because real environments always have a few weird cases. Application-centric recovery plans. Instead of failing over one resource at a time, a recovery plan orchestrates the entire app. It auto-detects existing solutions (Azure Site Recovery for VMs, for example), lets you group and order the resources for failover, and excludes resources that are already configured for high availability (no point failing them over if they did not go down). You can run an on-demand readiness check any time the app structure changes, so you find configuration drift before an outage finds it for you. Zone-down drills powered by Azure Chaos Studio. A zone-down drill template identifies the service group resources, pre-populates the right native faults per resource type (think a Redis cache fault, a VM scale set shutdown, and so on), bundles in identity and permission checks, monitoring, and the recovery plan you already built. When you execute, you pick the region and the target zone, the drill runs a pre-validation check, injects the fault, runs failover, then reprotection and failback, and tracks all of it as a single job in the execution report. Per-resource metrics let you visualize the actual downtime each component experienced. If a native fault is not what you want, you can override with a custom runbook. That last point is the part I think a lot of folks miss. A drill is not just fault injection. It is fault injection plus failover plus reprotection plus failback, all measured and attestable in one place. Real-World Value Back to Zava. They needed to answer three questions: what is our current zonal resiliency posture across these Azure services, what should we prioritize against our 99.9 percent target, and how do we validate that we will actually perform during an outage? Resiliency in Azure answers all three without forcing the platform team to write a 200-line PowerShell script. Use cases that should be on your shortlist: Regulated workloads (insurance, healthcare, financial services) that need to evidence drills for compliance. The notes and manual attestation features were clearly designed with auditors in mind. Apps with mixed estates, where a central platform team owns shared services (firewalls, identity) and app teams own everything else. Service groups can be parented to mirror that org structure. Apps with custom resiliency solutions that the platform cannot detect. Manual attestation keeps the dashboard honest without forcing you to refactor. Game-day rehearsals. The pre-built zone-down template means you can run a meaningful drill in an afternoon instead of standing up a custom Chaos Studio experiment from scratch. The honest tradeoff: zone redundancy is not free for every service, and not every resource type is in scope yet (around 20 today). Plan accordingly, exclude what is not critical, and attest what is covered by something else. Getting Started Here is the path I would take on a Monday morning: Open the Azure portal and search for Resiliency. You will land on the Resiliency in Azure page that replaces the old Business Continuity Center. Create a service group. Add resources directly, or add resource groups if each resource group is already an application boundary in your environment. Assign the zonal resiliency goal to the service group. Review the summary tiles. Exclude or manually attest the resources that need it. Walk the Advisor recommendations. Use the Copilot guided flow to generate a remediation script and run it through your automation. Build an application-centric recovery plan, group and order the resources, run an on-demand readiness check. Create a zone-down drill from the template, validate identity, monitoring, and faults, then execute the drill in a non-production zone first. Resources Resiliency in Azure documentation Zonal resources and zone resiliency Azure service groups overview Azure Advisor reliability recommendations Azure Chaos Studio documentation Azure Site Recovery overview Keep Learning at the Summit Catch the full Microsoft Azure Infra Summit 2026 session playlist here: https://www.youtube.com/playlist?list=PLjt5SKzX1iI8con7FJDB56G6hHqxGm7ki Cheers! Pierre Roman
