Secure Unique Default Hostnames Now GA for Functions and Logic Apps
We are pleased to announce that Secure Unique Default Hostnames are now generally available (GA) for Azure Functions and Logic Apps (Standard). This expands the security model previously available for Web Apps to the entire App Service ecosystem and provides customers with stronger, more secure, and standardized hostname behavior across all workloads. Why This Feature Matters Historically, App Service resources have used default hostname format such as: <SiteName>.azurewebsites.net While straightforward, this pattern introduced potential security risks, particularly in scenarios where DNS records were left behind after deleting a resource. In those situations, a different user could create a new resource with the same name and unintentionally receive traffic or bindings associated with the old DNS configuration, creating opportunities for issues such as subdomain takeover. Secure Unique Default Hostnames address this by assigning a unique, randomized, region‑scoped hostname to each resource, for example: <SiteName>-<Hash>.<Region>.azurewebsites.net This change ensures that: No other customer can recreate the same default hostname. Apps inherently avoid risks associated with dangling DNS entries. Customers gain a more secure, reliable baseline behavior across App Service. Adopting this model now helps organizations prepare for the long‑term direction of the platform while improving security posture today. What’s New: GA Support for Functions and Logic Apps With this release, both Azure Functions and Logic Apps (Standard) fully support the Secure Unique Default Hostname capability. This brings these services in line with Web Apps and ensures customers across all App Service workloads benefit from the same secure and consistent default hostname model. Azure CLI Support The Azure CLI for Web Apps and Function Apps now includes support for the “--domain-name-scope” parameter. This allows customers to explicitly specify the scope used when generating a unique default hostname during resource creation. Examples: az webapp create --domain-name-scope {NoReuse, ResourceGroupReuse, SubscriptionReuse, TenantReuse} az functionapp create --domain-name-scope {NoReuse, ResourceGroupReuse, SubscriptionReuse, TenantReuse} Including this parameter ensures that deployments consistently use the intended hostname scope and helps teams prepare their automation and provisioning workflows for the secure unique default hostname model. Why Customers Should Adopt This Now While existing resources will continue to function normally, customers are strongly encouraged to adopt Secure Unique Default Hostnames for all new deployments. Early adoption provides several important benefits: A significantly stronger security posture. Protection against dangling DNS and subdomain takeover scenarios. Consistent default hostname behavior as the platform evolves. Alignment with recommended deployment practices and modern DNS hygiene. This feature represents the current best practice for hostname management on App Service and adopting it now helps ensure that new deployments follow the most secure and consistent model available. Recommended Next Steps Enable Secure Unique Default Hostnames for all new Web Apps, Function Apps, and Logic Apps. Update automation and CLI scripts to include the --domain-name-scope parameter when creating new resources. Begin updating internal documentation and operational processes to reflect the new hostname pattern. Additional Resources For readers who want to explore the technical background and earlier announcements in more detail, the following articles offer deeper coverage of unique default hostnames: Public Preview: Creating Web App with a Unique Default Hostname This article introduces the foundational concepts behind unique default hostnames. It explains why the feature was created, how the hostname format works, and provides examples and guidance for enabling the feature when creating new resources. Secure Unique Default Hostnames: GA on App Service Web Apps and Public Preview on Functions This article provides the initial GA announcement for Web Apps and early preview details for Functions. It covers the security benefits, recommended usage patterns, and guidance on how to handle existing resources that were created without unique default hostnames. Conclusion Secure Unique Default Hostnames now provide a more secure and consistent default hostname model across Web Apps, Function Apps, and Logic Apps. This enhancement reduces DNS‑related risks and strengthens application security, and organizations are encouraged to adopt this feature as the standard for new deployments.Context Engineering Lessons from Building Azure SRE Agent
We started with 100+ tools and 50+ specialized agents. We ended with 5 core tools and a handful of generalists. The agent got more reliable, not less. Every context decision is a tradeoff: latency vs autonomy, evidence-building vs speed, oversight - and the cost of being wrong. This post is a practical map of those knobs and how we adjusted them for SRE Agent.
Find the Alerts You Didn't Know You Were Missing with Azure SRE Agent
I had 6 alert rules. CPU. Memory. Pod restarts. Container errors. OOMKilled. Job failures. I thought I was covered. Then my app went down. I kept refreshing the Azure portal, waiting for an alert. Nothing. That's when it hit me: my alerts were working perfectly. They just weren't designed for this failure mode. Sound familiar? The Problem Every Developer Knows If you're a developer or DevOps engineer, you've been here: a customer reports an issue, you scramble to check your monitoring, and then you realize you don't have the right alerts set up. By the time you find out, it's already too late. You set up what seems like reasonable alerting and assume you're covered. But real-world failures are sneaky. They slip through the cracks of your carefully planned thresholds. My Setup: AKS with Redis I love to vibe code apps using GitHub Copilot Agent mode with Claude Opus 4.5. It's fast, it understands context, and it lets me focus on building rather than boilerplate. For this project, I built a simple journal entry app: AKS cluster hosting the web API Azure Cache for Redis storing journal data Azure Monitor alerts for CPU, memory, pod restarts, container errors, OOMKilled, and job failures Seemed solid. What could go wrong? The Scenario: Redis Password Rotation Here's something that happens constantly in enterprise environments: the security team rotates passwords. It's best practice. It's in the compliance checklist. And it breaks things when apps don't pick up the new credentials. I simulated exactly this. The pods came back up. But they couldn't connect to Redis (as expected). The readiness probes started failing. The LoadBalancer had no healthy backends. The endpoint timed out. And not a single alert fired. Using SRE Agent to Find the Alert Gaps Instead of manually auditing every alert rule and trying to figure out what I missed, I turned to Azure SRE Agent. I asked it a simple question: "My endpoint is timing out. What alerts do I have, and why didn't any of them fire?" Within minutes, it had diagnosed the problem. Here's what it found: My Existing Alerts Why They Didn't Fire High CPU/Memory No resource pressure,just auth failures Pod Restarts Pods weren't restarting, just unhealthy Container Errors App logs weren't being written OOMKilled No memory issues Job Failures No K8s jobs involved The gaps SRE Agent identified: ❌ No synthetic URL availability test ❌ No readiness/liveness probe failure alerts ❌ No "pods not ready" alerts scoped to my namespace ❌ No Redis connection error detection ❌ No ingress 5xx/timeout spike alerts ❌ No per-pod resource alerts (only node-level) SRE Agent didn't just tell me what was wrong, it created a GitHub issue with : KQL queries to detect each failure type Bicep code snippets for new alert rules Remediation suggestions for the app code Exact file paths in my repo to update Check it out: GitHub Issue How I Built It: Step by Step Let me walk you through exactly how I set this up inside SRE Agent. Step 1: Create an SRE Agent I created a new SRE Agent in the Azure portal. Since this workflow analyzes alerts across my subscription (not just one resource group), I didn't configure any specific resource groups. Instead, I gave the agent's managed identity Reader permissions on my entire subscription. This lets it discover resources, list alert rules, and query Log Analytics across all my resource groups. Step 2: Connect GitHub to SRE Agent via MCP I added a GitHub MCP server to give the agent access to my source code repository.MCP (Model Context Protocol) lets you bring any API into the agent. If your tool has an API, you can connect it. I use GitHub for both source code and tracking dev tickets, but you can connect to wherever your code lives (GitLab, Azure DevOps) or your ticketing system (Jira, ServiceNow, PagerDuty). Step 3: Create a Subagent inside SRE Agent for managing Azure Monitor Alerts I created a focused subagent with a specific job and only the tools it needs: Azure Monitor Alerts Expert Prompt: " You are expert in managing operations related to azure monitor alerts on azure resources including discovering alert rules configured on azure resources, creating new alert rules (with user approval and authorization only), processing the alerts fired on azure resources and identifying gaps in the alert rules. You can get the resource details from azure monitor alert if triggered via alert. If not, you need to ask user for the specific resource to perform analysis on. You can use az cli tool to diagnose logs, check the app health metrics. You must use the app code and infra code (bicep files) files you have access to in the github repo <insert your repo> to further understand the possible diagnoses and suggest remediations. Once analysis is done, you must create a github issue with details of analysis and suggested remediation to the source code files in the same repo." Tools enabled: az cli – List resources, alert rules, action groups Log Analytics workspace querying – Run KQL queries for diagnostics GitHub MCP – Search repositories, read file contents, create issues Step 4: Ask the Subagent About Alert Gaps I gave the agent context and asked a simple question: "@AzureAlertExpert: My API endpoint http://132.196.167.102/api/journals/john is timing out. What alerts do I have configured in rg-aks-journal, and why didn't any of them fire? The agent did the analysis autonomously and summarized findings with suggestions to add new alert rules in a GitHub issue. Here's the agentic workflow to perform azure monitor alert operations Why This Matters Faster response times. Issues get diagnosed in minutes, not hours of manual investigation. Consistent analysis. No more "I thought we had an alert for that" moments. The agent systematically checks what's covered and what's not. Proactive coverage. You don't have to wait for an incident to find gaps. Ask the agent to review your alerts before something breaks. The Bottom Line Your alerts have gaps. You just don't know it until something slips through. I had 6 alert rules and still missed a basic failure. My pods weren't restarting, they were just unhealthy. My CPU wasn't spiking, the app was just returning errors. None of my alerts were designed for this. You don't need to audit every alert rule manually. Give SRE Agent your environment, describe the failure, and let it tell you what's missing. Stop discovering alert gaps from customer complaints. Start finding them before they matter. A Few Tips Give the agent Reader access at subscription level so it can discover all resources Use a focused subagent prompt, don't try to do everything in one agent Test your MCP connections before running workflows What Alert Gaps Have Burned You? What's the alert you wish you had set up before an incident? Credential rotation? Certificate expiry? DNS failures? Let us know in the comments.From Vibe Coding to Working App: How SRE Agent Completes the Developer Loop
The Most Common Challenge in Modern Cloud Apps There's a category of bugs that drive engineers crazy: multi-layer infrastructure issues. Your app deploys successfully. Every Azure resource shows "Succeeded." But the app fails at runtime with a vague error like Login failed for user ''. Where do you even start? You're checking the Web App, the SQL Server, the VNet, the private endpoint, the DNS zone, the identity configuration... and each one looks fine in isolation. The problem is how they connect and that's invisible in the portal. Networking issues are especially brutal. The error says "Login failed" but the actual causes could be DNS, firewall, identity, or all three. The symptom and the root causes are in completely different resources. Without deep Azure networking knowledge, you're just clicking around hoping something jumps out. Now imagine you vibe coded the infrastructure. You used AI to generate the Bicep, deployed it, and moved on. When it breaks, you're debugging code you didn't write, configuring resources you don't fully understand. This is where I wanted AI to help not just to build, but to debug. Enter SRE Agent + Coding Agent Here's what I used: Layer Tool Purpose Build VS Code Copilot Agent Mode + Claude Opus Generate code, Bicep, deploy Debug Azure SRE Agent Diagnose infrastructure issues and create developer issue with suggested fixes in source code (app code and IaC) Fix GitHub Coding Agent Create PRs with code and IaC fix from Github issue created by SRE Agent Copilot builds. SRE Agent debugs. Coding Agent fixes. What I Built I used VS Code Copilot in Agent Mode with Claude Opus to create a .NET 8 Web App connected to Azure SQL via private endpoint: Private networking (no public exposure) Entra-only authentication Managed identity (no secrets) Deployed with azd up. All green. Then I tested the health endpoint: $ curl https://app-tsdvdfdwo77hc.azurewebsites.net/health/sql {"status":"unhealthy","error":"Login failed for user ''.","errorType":"SqlException"} Deployment succeeded. App failed. One error. How I Fixed It: Step by Step Step 1: Create SRE Agent with Azure Access I created an SRE Agent with read access to my Azure subscription. You can scope it to specific resource groups. The agent builds a knowledge graph of your resources and their dependencies visible in the Resource Mapping view below. Step 2: Connect GitHub to SRE Agent using GitHub MCP server I connected the GitHub MCP server so the agent could read my repository and create issues. Step 3: Create Sub Agent to analyze source code I created a sub-agent for analyzing source code using GitHub mcp tools. this lets SRE Agent understand not just Azure resources, but also the Bicep and source code files that created them. "you are expert in analyzing source code (bicep and app code) from github repos" Step 4: Invoke Sub-Agent to Analyze the Error In the SRE Agent chat, I invoked the sub-agent to diagnose the error I received from my app end point. It correlated the runtime error with the infrastructure configuration Step 5: Watch the SRE Agent Think and Reason SRE Agent analyzed the error by tracing code in Program.cs, Bicep configurations, and Azure resource relationships Web App, SQL Server, VNet, private endpoint, DNS zone, and managed identity. Its reasoning process worked through each layer, eliminating possibilities one by one until it identified the root causes. Step 6: Agent Creates GitHub Issue Based on its analysis, SRE Agent summarized the root causes and suggested fixes in a GitHub issue: Root Causes: Private DNS Zone missing VNet link Managed identity not created as SQL user Suggested Fixes: Add virtualNetworkLinks resource to Bicep Add SQL setup script to create user with db_datareader and db_datawriter roles Step 7: Merge the PR from Coding Agent Assign the Github issue to Coding Agent which then creates a PR with the fixes. I just reviewed the fix. It made sense and I merged it. Redeployed with azd up, ran the SQL script: curl -s https://app-tsdvdfdwo77hc.azurewebsites.net/health/sql | jq . { "status": "healthy", "database": "tododb", "server": "tcp:sql-tsdvdfdwo77hc.database.windows.net,1433", "message": "Successfully connected to SQL Server" } 🎉 From error to fix in minutes without manually debugging a single Azure resource. Why This Matters If you're a developer building and deploying apps to Azure, SRE Agent changes how you work: You don't need to be a networking expert. SRE Agent understands the relationships between Azure resources private endpoints, DNS zones, VNet links, managed identities. It connects dots you didn't know existed. You don't need to guess. Instead of clicking through the portal hoping something looks wrong, the agent systematically eliminates possibilities like a senior engineer would. You don't break your workflow. SRE Agent suggests fixes in your Bicep and source code not portal changes. Everything stays version controlled. Deployed through pipelines. No hot fixes at 2 AM. You close the loop. AI helps you build fast. Now AI helps you debug fast too. Try It Yourself Do you vibe code your app, your infrastructure, or both? How do you debug when things break? Here's a challenge: Vibe code a todo app with a Web App, VNet, private endpoint, and SQL database. "Forget" to link the DNS zone to the VNet. Deploy it. Watch it fail. Then point SRE Agent at it and see how it identifies the root cause, creates a GitHub issue with the fix, and hands it off to Coding Agent for a PR. Share your experience. I'd love to hear how it goes. Learn More Azure SRE Agent documentation Azure SRE Agent blogs Azure SRE Agent community Azure SRE Agent home page Azure SRE Agent pricingExtend SRE Agent with MCP: Build an Agentic Workflow to Triage Customer Issues
Your inbox is full. GitHub issues piling up. "App not working." "How do I configure alerts?" "Please add dark mode." You open each one, figure out what it is, ask for more info, add labels, route to the right team. An hour later, you're still sorting issues. Sound familiar? The Triage Tax Every L1 support engineer, PM, and on-call developer who's handled customer issues knows this pain. When tickets come in, you're not solving problems, you're sorting them. Read the issue. Is it a bug or a question? Check the docs. Does this feature exist? Ask for more info. Wait two days. Re-triage. Add labels. Route to engineering. It's tedious. It requires judgment, you need to understand the product, know what info is needed, check documentation. And honestly? It's work that nobody volunteers for but someone has to do. In large organizations, it gets even more complex. The issue doesn't just need to be triaged, it needs to be routed to the right engineering team. Is this an auth issue? Frontend? Backend? Infrastructure? A wrong routing decision means delays, re-assignments, and frustrated customers. What if an AI agent could do this for you? Enter Azure SRE Agent + MCP Here's what I built: I gave SRE Agent access to my GitHub and PagerDuty accounts via MCP, uploaded my triage rubric as a markdown file, and set it to run twice a day. No more reading every ticket manually. No more asking the same "please provide more info" questions. No more morning triage sessions. What My Setup Looks Like My app's customer issues come in through GitHub. My team uses PagerDuty to track bugs and incidents. So I connected both via MCP to the SRE Agent. I also uploaded my triage logic as a .md file on how to classify issues, what info is required for each category, which labels to use, which team handles what. And since I didn't want to run this workflow manually, I set up a scheduled task to trigger it twice a day. Now it just runs. I verify its work if I want to. What the Agent Does Fetches all open, unlabeled GitHub issues Reads each issue and classifies it (bug, doc question, feature request) Checks if required info is present Posts a comment asking for details if needed, or acknowledges the issue Adds appropriate labels Creates a PagerDuty incident for bugs ready for engineering Moves to the next issue How I Built It: Step by Step Let me walk you through exactly how I set this up inside SRE Agent. Step 1: Create an SRE Agent I created a new SRE Agent in the Azure portal. Since this workflow triages GitHub issues and not Azure resources, I didn't need to configure any Azure resource groups or subscriptions. Just an agent. Step 2: Connect MCP Servers I added two MCP servers to give the agent access to my tools: GitHub MCP– Fetch issues, post comments, add labels PagerDuty MCP – Create incidents for bugs that need dev team's attention MCP (Model Context Protocol) lets you bring any API into the agent. If your tool has an API, you can connect it. Step 3: Create Subagents I created two focused subagents, each with a specific job and only the tools it needs: GitHub Issue Triager "You are expert in triaging GitHub issues, classifying them into categories such as user needs to supply additional information, bug, documentation question, or feature request. Use the knowledge base to search for the right document that helps you with performing this triaging. Perform all actions autonomously without waiting for user input. Hand off to Incident Creator for the issues you classified as bugs." Tools: GitHub MCP (issues, labels, comments) Incident Creator Here "You are expert in managing incidents in PagerDuty, listing services, incidents, creating incidents with all details. Once done, hand off back to GitHub Issue Triager." Tools: PagerDuty MCP (services, incidents) The handoff between them creates a workflow. They collaborate without human involvement. Step 4: Add Your Knowledge I uploaded my triage logic as a .md file to the agent's knowledge base. This is my rubric - my mental model for how to triage issues: How do I classify bugs vs. doc questions vs. feature requests? What info is required for each category? What labels do I use? When should an incident be created? Which team handles which type of issue? I wrote it down the way I'd explain it to a new teammate. The agent searches and follows it. Step 5: Add a Scheduled Task I didn't want to trigger this workflow manually every time. SRE Agent supports scheduled tasks, workflows that run automatically on a cadence. I set up a trigger to run twice a day: morning and evening. Now the workflow is fully automated. Here is the end to end automated agentic workflow to triage customer tickets. Why MCP Matters Every team uses different tools. Maybe your customer issues live in Zendesk, incidents go to ServiceNow and you use Jira or Azure DevOps. SRE Agent doesn't lock you in. With MCP, you connect to whatever tools you already use. The agent orchestrates across them. That's the extensibility model: your tools, your workflow, orchestrated by the agent. The Result Before: 2 hours every morning sorting tickets. After: By the time anyone logs in, issues are labeled, missing-info requests are posted, urgent bugs have incidents, and feature requests are acknowledged. Your team can finally focus on the complex stuff not sorting tickets. Why This Matters Faster response times. Issues get acknowledged in minutes, not days. Consistent classification. No "this should have been a P1" moments. No tickets bouncing between teams. Happier customers. They get a response immediately even if it's just "we're looking into it." Focus on what matters. Your team should be solving problems, not sorting them. The Bottom Line Triage isn't the job, it's the tax on the job. It quietly eats the hours your team could spend building, debugging, and shipping. You don't need to build a custom triage bot. You don't need to wire up webhooks and write glue code. You give the SRE agent your tools, your logic, and a schedule and it handles the sorting. Use GitHub? Connect GitHub. Use Zendesk? Connect Zendesk. PagerDuty, ServiceNow, Jira - whatever your team runs on, the agent meets you there. Stop sorting tickets. Start shipping. A Few Tips Test MCP endpoints before configuring them in the SRE agent Give each subagent only the tools it needs, don't enable everything Start read-only until you trust the classification, then enable comments Do You Still Want to Triage Issues Manually? What tools does your team use to track customer-reported issues and incidents? Let us know in the comments, we'd love to hear how you'd use this workflow with your stack. Is triage your most toilsome workflow or is there something even worse eating your team's time? Let us know in the comments.Stop Running Runbooks at 3 am: Let Azure SRE Agent Do Your On-Call Grunt Work
Your pager goes off. It's 2:47am. Production is throwing 500 errors. You know the drill - SSH into this, query that, check these metrics, correlate those logs. Twenty minutes later, you're still piecing together what went wrong. Sound familiar? The On-Call Reality Nobody Talks About Every SRE, DevOps engineer, and developer who's carried a pager knows this pain. When incidents hit, you're not solving problems - you're executing runbooks. Copy-paste this query. Check that dashboard. Run these az commands. Connect the dots between five different tools. It's tedious. It's error-prone at 3am. And honestly? It's work that doesn't require human creativity but requires human time. What if an AI agent could do this for you? Enter Azure SRE Agent + Runbook Automation Here's what I built: I gave SRE Agent a simple markdown runbook containing the same diagnostic steps I'd run manually during an incident. The agent executes those steps, collects evidence, and sends me an email with everything I need to take action. No more bouncing between terminals. No more forgetting a step because it's 3am and your brain is foggy. What My Runbook Contains Just the basics any on-call would run: az monitor metrics – CPU, memory, request rates Log Analytics queries – Error patterns, exception details, dependency failures App Insights data – Failed requests, stack traces, correlation IDs az containerapp logs – Revision logs, app configuration That's it. Plain markdown with KQL queries and CLI commands. Nothing fancy. What the Agent Does Reads the runbook from its knowledge base Executes each diagnostic step Collects results and evidence Sends me an email with analysis and findings I wake up to an email that says: "CPU spiked to 92% at 2:45am, triggering connection pool exhaustion. Top exception: SqlException (1,832 occurrences). Errors correlate with traffic spike. Recommend scaling to 5 replicas." All the evidence. All the queries used. All the timestamps. Ready for me to act. How to Set This Up (6 Steps) Here's how you can build this yourself: Step 1: Create SRE Agent Create a new SRE Agent in the Azure portal. No Azure resource groups to configure. If your apps run on Azure, the agent pulls context from the incident itself. If your apps run elsewhere, you don't need Azure resource configuration at all. Step 2: Grant Reader Permission (Optional) If your runbooks execute against Azure resources, assign Reader role to the SRE Agent's managed identity on your subscription. This allows the agent to run az commands and query metrics. Skip this if your runbooks target non-Azure apps. Step 3: Add Your Runbook to SRE Agent's Knowledge base You already have runbooks, they're in your wiki, Confluence, or team docs. Just add them as .md files to the agent's knowledge base. To learn about other ways to link your runbooks to the agent, read this Step 4: Connect Outlook Connect the agent to your Outlook so it can send you the analysis email with findings. Step 5: Create a Subagent Create a subagent with simple instructions like: "You are an expert in triaging and diagnosing incidents. When triggered, search the knowledge base for the relevant runbook, execute the diagnostic steps, collect evidence, and send an email summary with your findings." Assign the tools the agent needs: RunAzCliReadCommands – for az monitor, az containerapp commands QueryLogAnalyticsByWorkspaceId – for KQL queries against Log Analytics QueryAppInsightsByResourceId – for App Insights data SearchMemory – to find the right runbook SendOutlookEmail – to deliver the analysis Step 6: Set Up Incident Trigger Connect your incident management tool - PagerDuty, ServiceNow, or Azure Monitor alerts and setup the incident trigger to the subagent. When an incident fires, the agent kicks off automatically. That's it. Your agentic workflow now looks like this: This Works for Any App, Not Just Azure Here's the thing: SRE Agent is platform agnostic. It's executing your runbooks, whatever they contain. On-prem databases? Add your diagnostic SQL. Custom monitoring stack? Add those API calls. The agent doesn't care where your app runs. It cares about following your runbook and getting you answers. Why This Matters Lower MTTR. By the time you're awake and coherent, the analysis is done. Consistent execution. No missed steps. No "I forgot to check the dependencies" at 4am. Evidence for postmortems. Every query, every result, timestamped and documented. Focus on what matters. Your brain should be deciding what to do not gathering data. The Bottom Line On-call runbook execution is the most common, most tedious, and most automatable part of incident response. It's grunt work that pulls engineers away from the creative problem-solving they were hired for. SRE Agent offloads that work from your plate. You write the runbook once, and the agent executes it every time, faster and more consistently than any human at 3am. Stop running runbooks. Start reviewing results. Try it yourself: Create a markdown runbook with your diagnostic queries and commands, add it to your SRE Agent's knowledge base, and let the agent handle your next incident. Your 3am self will thank you.Call Function App from Azure Data Factory with Managed Identity Authentication
Integrating Azure Function Apps into your Azure Data Factory (ADF) workflows is a common practice. To enhance security beyond the use of function API keys, leveraging managed identity authentication is strongly recommended. Given the fact that many existing guides were outdated with recent updates to Azure services, this article provides a comprehensive, up-to-date walkthrough on configuring managed identity in ADF to securely call Function Apps. The provided methods can also be adapted to other Azure services that need to call Function Apps with managed identity authentication. The high level process is: Enable Managed Identity on Data Factory Configure Microsoft Entra Sign-in on Azure Function App Configure Linked Service in Data Factory Assign Permissions to the Data Factory in Azure Function Step 1: Enable Managed Identity on Data Factory On the Data Factory’s portal, go to Managed Identities, and enable a system assigned managed identity. Step 2: Configure Microsoft Entra Sign-in on Azure Function App 1. Go to Function App portal and enable Authentication. Choose "Microsoft" as the identity provider. 2. Add an app registration to the app, it could be an existing one or you can choose to let the platform create a new app registration. 3. Next, allow the ADF as a client application to authenticate to the function app. This step is a new requirement from previous guides. If these settings are not correctly set, the 403 response will be returned: Add the Application ID of the ADF managed identity in Allowed client application and Object ID of the ADF managed identity in the Allowed identities. If the requests are only allowed from specific tenants, add the Tenant ID of the managed identity in the last box. 4. This part sets the response from function app for the unauthenticated requests. We should set the response as "HTTP 401 Unauthorized: recommended for APIs" as sign-in page is not feasible for API calls from ADF. 5. Then, click next and use the default permission option. 6. After everything is set, click "Add" to complete the configuration. Copy the generated App (client) id, as this is used in data factory to handle authorization. Step 3: Configure Linked Service in Data Factory 1. To use an Azure Function activity in a pipeline, follow the steps here: Create an Azure Function activity with UI 2. Then Edit or New a Azure Function Linked Service. 3. Change authentication method to System Assigned Managed Identity, and paste the copied client ID of function app identity provider from Step 2 into Resource ID. This step is necessary as authorization does not work without this. Step 4: Assign Permissions to the Data Factory in Azure Function 1. On the function app portal, go to Access control (IAM), and Add a new role assignment. 2. Assign reader role. 3. Assign the Data Factory’s Managed Identity to that role. After everything is set, test that the function app can be called from Azure Data Factory successfully. Reference: https://prodata.ie/2022/06/16/enabling-managed-identity-authentication-on-azure-functions-in-data-factory/ https://learn.microsoft.com/en-us/azure/data-factory/control-flow-azure-function-activity https://docs.azure.cn/en-us/app-service/overview-authentication-authorizationIndustry-Wide Certificate Changes Impacting Azure App Service Certificates
Executive Summary In early 2026, industry-wide changes mandated by browser applications and the CA/B Forum will affect both how TLS certificates are issued as well as their validity period. The CA/B Forum is a vendor body that establishes standards for securing websites and online communications through SSL/TLS certificates. Azure App Service is aligning with these standards for both App Service Managed Certificates (ASMC, free, DigiCert-issued) and App Service Certificates (ASC, paid, GoDaddy-issued). Most customers will experience no disruption. Action is required only if you pin certificates or use them for client authentication (mTLS). Who Should Read This? App Service administrators Security and compliance teams Anyone responsible for certificate management or application security Quick Reference: What’s Changing & What To Do Topic ASMC (Managed, free) ASC (GoDaddy, paid) Required Action New Cert Chain New chain (no action unless pinned) New chain (no action unless pinned) Remove certificate pinning Client Auth EKU Not supported (no action unless cert is used for mTLS) Not supported (no action unless cert is used for mTLS) Transition from mTLS Validity No change (already compliant) Two overlapping certs issued for the full year None (automated) If you do not pin certificates or use them for mTLS, no action is required. Timeline of Key Dates Date Change Action Required Mid-Jan 2026 and after ASMC migrates to new chain ASMC stops supporting client auth EKU Remove certificate pinning if used Transition to alternative authentication if the certificate is used for mTLS Mar 2026 and after ASC validity shortened ASC migrates to new chain ASC stops supporting client auth EKU Remove certificate pinning if used Transition to alternative authentication if the certificate is used for mTLS Actions Checklist For All Users Review your use of App Service certificates. If you do not pin these certificates and do not use them for mTLS, no action is required. If You Pin Certificates (ASMC or ASC) Remove all certificate or chain pinning before their respective key change dates to avoid service disruption. See Best Practices: Certificate Pinning. If You Use Certificates for Client Authentication (mTLS) Switch to an alternative authentication method before their respective key change dates to avoid service disruption, as client authentication EKU will no longer be supported for these certificates. See Sunsetting the client authentication EKU from DigiCert public TLS certificates. See Set Up TLS Mutual Authentication - Azure App Service Details & Rationale Why Are These Changes Happening? These updates are required by major browser programs (e.g., Chrome) and apply to all public CAs. They are designed to enhance security and compliance across the industry. Azure App Service is automating updates to minimize customer impact. What’s Changing? New Certificate Chain Certificates will be issued from a new chain to maintain browser trust. Impact: Remove any certificate pinning to avoid disruption. Removal of Client Authentication EKU Newly issued certificates will not support client authentication EKU. This change aligns with Google Chrome’s root program requirements to enhance security. Impact: If you use these certificates for mTLS, transition to an alternate authentication method. Shortening of Certificate Validity Certificate validity is now limited to a maximum of 200 days. Impact: ASMC is already compliant; ASC will automatically issue two overlapping certificates to cover one year. No billing impact. Frequently Asked Questions (FAQs) Will I lose coverage due to shorter validity? No. For App Service Certificate, App Service will issue two certificates to span the full year you purchased. Is this unique to DigiCert and GoDaddy? No. This is an industry-wide change. Do these changes impact certificates from other CAs? Yes. These changes are an industry-wide change. We recommend you reach out to your certificates’ CA for more information. Do I need to act today? If you do not pin or use these certs for mTLS, no action is required. Glossary ASMC: App Service Managed Certificate (free, DigiCert-issued) ASC: App Service Certificate (paid, GoDaddy-issued) EKU: Extended Key Usage mTLS: Mutual TLS (client certificate authentication) CA/B Forum: Certification Authority/Browser Forum Additional Resources Changes to the Managed TLS Feature Set Up TLS Mutual Authentication Azure App Service Best Practices – Certificate pinning DigiCert Root and Intermediate CA Certificate Updates 2023 Sunsetting the client authentication EKU from DigiCert public TLS certificates Feedback & Support If you have questions or need help, please visit our official support channels or the Microsoft Q&A, where our team and the community can assist you.
