Modernizing Terraform Pipelines on Azure: OIDC Federation for GitHub Actions and Azure DevOps
The secret nobody wants to rotate Most Terraform-on-Azure pipelines we see still authenticate the same way they did three years ago. A long-lived ARM_CLIENT_SECRET sitting in GitHub Actions or Azure DevOps, set once, copied around, and rotated only when something breaks. It's the most ignored credential in the cloud, and statistically the most likely one to leak. A developer screenshots a variable group. A pipeline log echoes a value. A fork inherits a secret. Or the secret simply expires on a Friday evening and takes production deployments with it. Workload Identity Federation (WIF) makes this whole class of problem go away. The pipeline mints a short-lived token at runtime, exchanges it for an Azure access token via Microsoft Entra, and never touches a secret. GitHub Actions has supported it since 2021. Azure DevOps service connections went GA with WIF in February 2024. The azurerm Terraform provider has supported it since v3.7. This post walks through the pattern end-to-end, for both GitHub Actions and Azure DevOps, the way I've rolled it out across multiple customer estates. How the exchange actually works Before any YAML, it helps to picture what's happening: The CI system (GitHub or ADO) signs a short-lived JWT describing exactly what's running- which repo, which branch, which environment, which service connection. The pipeline sends that JWT to Microsoft Entra ID. Entra checks it against a federated identity credential you've configured on a managed identity or app registration. The iss, sub, and aud claims must match case-sensitively. If it matches, Entra returns an Azure access token valid for the duration of the job. Terraform uses it. The job ends. The token expires. Nothing persists. The token is bound to a specific subject like repo:contoso/platform:environment:prod or sc://contoso/platform/azure-prod. It can't be reused from another repo, branch, or pipeline. Recommended Architecture A few choices that usually hold up in production: Decision Choice Identity type User-assigned managed identity (UAMI), not app registration Identity granularity One UAMI per environment (not per pipeline) Trust scope Pinned to the environment claim, not the branch RBAC scope Resource group, not subscription Remote state OIDC + use_azuread_auth = true, shared key access disabled Why UAMIs? They live in your subscription, don't need Application Administrator rights to manage, and follow the lifecycle of the resource group they belong to. Why one per environment? Pipeline-per-identity explodes into hundreds of identities. Environment-per-identity maps cleanly to deployment scopes. Part 1 - GitHub Actions Step 1: Create the identity and federate it Two commands per environment. That's it. az identity create -g rg-platform-identity -n id-tf-prod -l eastus az identity federated-credential create \ --name github-prod \ --identity-name id-tf-prod \ --resource-group rg-platform-identity \ --issuer https://token.actions.githubusercontent.com \ --subject repo:contoso/platform:environment:prod \ --audiences api://AzureADTokenExchange Repeat for nonprod. No secret is created anywhere. Step 2: Wire it up in GitHub In repo Settings → Environments, create nonprod and prod. On prod, add required reviewers and a branch rule restricting deployments to main. Then add three environment variables (not secrets - these aren't sensitive): AZURE_CLIENT_ID, AZURE_TENANT_ID, AZURE_SUBSCRIPTION_ID. The workflow itself stays small: permissions: id-token: write contents: read jobs: apply: runs-on: ubuntu-latest environment: prod env: ARM_USE_OIDC: "true" ARM_CLIENT_ID: ${{ vars.AZURE_CLIENT_ID }} ARM_TENANT_ID: ${{ vars.AZURE_TENANT_ID }} ARM_SUBSCRIPTION_ID: ${{ vars.AZURE_SUBSCRIPTION_ID }} steps: - uses: actions/checkout@v4 - uses: hashicorp/setup-terraform@v3 - run: terraform init && terraform apply -auto-approve Three things make this secure: id-token: write is the only elevated permission, and it doesn't grant write access to anything in GitHub, it just lets the runner mint a JWT. The environment: line picks the right AZURE_CLIENT_ID and drives the sub claim. The federation refuses anything else. No azure/login step is needed for Terraform. The azurerm provider reads GitHub's OIDC environment variables automatically. Part 2 - Azure DevOps The model is identical. The mechanics are different. ADO offers two creation paths for a WIF service connection: automatic (it creates an app registration for you) and manual (you bring your own UAMI). For platform teams, manual + UAMI is almost always the better choice to ensure identity lives where governance lives. The flow is a small dance between the two portals: In Azure DevOps, create a new ARM service connection → choose Workload Identity Federation (manual) → fill in your UAMI's client ID, tenant ID, and subscription. Save as draft. ADO shows you an issuer URL and a subject identifier. In Azure, on the UAMI, add a federated credential using the values ADO showed you. The subject looks like sc://contoso/platform/azure-prod. Back in ADO, click Verify and save. In the pipeline, the service connection only "activates" if a task in the job loads it. The simplest way is the AzureCLI@2 task: - task: AzureCLI@2 inputs: azureSubscription: azure-prod # the WIF service connection scriptType: bash scriptLocation: inlineScript inlineScript: | terraform init && terraform apply -auto-approve env: ARM_USE_OIDC: "true" ARM_CLIENT_ID: $(AZURE_CLIENT_ID) ARM_TENANT_ID: $(AZURE_TENANT_ID) ARM_SUBSCRIPTION_ID: $(AZURE_SUBSCRIPTION_ID) ARM_ADO_PIPELINE_SERVICE_CONNECTION_ID: $(SERVICE_CONNECTION_ID) SYSTEM_ACCESSTOKEN: $(System.AccessToken) SYSTEM_OIDCREQUESTURI: $(System.OidcRequestUri) For teams converting dozens of legacy connections, the Azure DevOps team published a PowerShell helper that walks every ARM service connection in a project and converts them in place. There's a 7-day rollback window on each connection, which makes the migration genuinely low-risk. Don't forget the state file The Terraform state is your real blast radius. With OIDC, it's almost free to lock it down too. The same UAMI can read and write blob data without the storage account key: backend "azurerm" { resource_group_name = "rg-tfstate" storage_account_name = "sttfstateprodeastus" container_name = "platform-prod" key = "platform.tfstate" use_oidc = true use_azuread_auth = true } Grant the UAMI Storage Blob Data Contributor on the container (not the account), disable shared key access on the storage account, and you've removed the last secret in the pipeline. RBAC and break-glass Federation removes a credential, not a privilege. A few habits worth keeping: Scope role assignments to resource groups, not subscriptions. The whole point of federation is that scoping is now trivially easy. Use Role Based Access Control Administrator instead of User Access Administrator if your Terraform creates role assignments. It's a more recent, narrower role. Have a documented break-glass. If GitHub or ADO has a token-service incident, you still need a path to ship a hotfix. A single hardware-key-protected emergency app registration in a separate identity boundary works well, audited monthly. Monitor sign-ins. Every federated exchange shows up in Entra sign-in logs as a service principal sign-in. Pipe these to Sentinel and alert on anomalies like sign-ins outside expected hours, or from IPs outside GitHub's published ranges. The errors you will hit (and what they really mean) Symptom What it actually is AADSTS70021: No matching federated identity record found Case-sensitive mismatch in iss, sub, or aud. Almost always a trailing slash or a capitalised character AADSTS700016: Application not found in directory Wrong client ID or tenant. Not a federation problem 403 on a resource even though token exchange worked Federation is fine. Your RBAC isn't. Check the exact scope Unable to determine OIDC token (ADO) No task in the job loaded the service connection. Add an AzureCLI@2 step Works on main, fails on tags You pinned sub to a branch ref. Add a second federated credential for tags, or move to environment-based scoping Migrating without a maintenance window You almost never get to do this on a greenfield repo. The order that has worked for me on legacy estates: Create the new UAMI alongside the old service principal, with the same role assignments. Federate one canary pipeline. Verify it deploys equivalently. Cut over pipelines in waves, lowest-risk environment first. Once a full release cycle passes cleanly, disable the old SP's secret. Wait another cycle. Then delete the SP entirely. Add a CI gate that fails any new pipeline introducing ARM_CLIENT_SECRET. The old and new auth methods coexist on the same subscription throughout. There's no hard cutover and no maintenance window, just a steady drift toward zero secrets. Wrapping up If you do nothing else after reading this, do one thing: search your CI variable groups for ARM_CLIENT_SECRET. Every result is an outage or a breach waiting to happen. Federation is one of those rare changes that's both more secure and less work to operate. Once you've set it up, you stop thinking about credential rotation, secret expiry, and quarterly access reviews for service principals. The pipeline simply runs, and the audit trail is in Entra where it belongs. That's a good trade.Defending the cloud: Azure neutralized a record-breaking 15 Tbps DDoS attack
On October 24, 2025, Azure DDOS Protection automatically detected and mitigated a multi-vector DDoS attack measuring 15.72 Tbps and nearly 3.64 billion packets per second (pps). This was the largest DDoS attack ever observed in the cloud and it targeted a single endpoint in Australia. By utilizing Azure’s globally distributed DDoS Protection infrastructure and continuous detection capabilities, mitigation measures were initiated. Malicious traffic was effectively filtered and redirected, maintaining uninterrupted service availability for customer workloads. The attack originated from Aisuru botnet. Aisuru is a Turbo Mirai-class IoT botnet that frequently causes record-breaking DDoS attacks by exploiting compromised home routers and cameras, mainly in residential ISPs in the United States and other countries. The attack involved extremely high-rate UDP floods targeting a specific public IP address, launched from over 500,000 source IPs across various regions. These sudden UDP bursts had minimal source spoofing and used random source ports, which helped simplify traceback and facilitated provider enforcement. Attackers are scaling with the internet itself. As fiber-to-the-home speeds rise and IoT devices get more powerful, the baseline for attack size keeps climbing. As we approach the upcoming holiday season, it is essential to confirm that all internet-facing applications and workloads are adequately protected against DDOS attacks. Additionally, do not wait for an actual attack to assess your defensive capabilities or operational readiness—conduct regular simulations to identify and address potential issues proactively. Learn more about Azure DDOS Protection at Azure DDoS Protection Overview | Microsoft LearnBuilding Azure Right: A Practical Checklist for Infrastructure Landing Zones
When the Gaps Start Showing A few months ago, we walked into a high-priority Azure environment review for a customer dealing with inconsistent deployments and rising costs. After a few discovery sessions, the root cause became clear: while they had resources running, there was no consistent foundation behind them. No standard tagging. No security baseline. No network segmentation strategy. In short—no structured Landing Zone. That situation isn't uncommon. Many organizations sprint into Azure workloads without first planning the right groundwork. That’s why having a clear, structured implementation checklist for your Landing Zone is so essential. What This Checklist Will Help You Do This implementation checklist isn’t just a formality. It’s meant to help teams: Align cloud implementation with business goals Avoid compliance and security oversights Improve visibility, governance, and operational readiness Build a scalable and secure foundation for workloads Let’s break it down, step by step. 🎯 Define Business Priorities Before Touching the Portal Before provisioning anything, work with stakeholders to understand: What outcomes matter most – Scalability? Faster go-to-market? Cost optimization? What constraints exist – Regulatory standards, data sovereignty, security controls What must not break – Legacy integrations, authentication flows, SLAs This helps prioritize cloud decisions based on value rather than assumption. 🔍 Get a Clear Picture of the Current Environment Your approach will differ depending on whether it’s a: Greenfield setup (fresh, no legacy baggage) Brownfield deployment (existing workloads to assess and uplift) For brownfield, audit gaps in areas like scalability, identity, and compliance before any new provisioning. 📜 Lock Down Governance Early Set standards from day one: Role-Based Access Control (RBAC): Granular, least-privilege access Resource Tagging: Consistent metadata for tracking, automation, and cost management Security Baselines: Predefined policies aligned with your compliance model (NIST, CIS, etc.) This ensures everything downstream is both discoverable and manageable. 🧭 Design a Network That Supports Security and Scale Network configuration should not be an afterthought: Define NSG Rules and enforce segmentation Use Routing Rules to control flow between tiers Consider Private Endpoints to keep services off the public internet This stage sets your network up to scale securely and avoid rework later. 🧰 Choose a Deployment Approach That Fits Your Team You don’t need to reinvent the wheel. Choose from: Predefined ARM/Bicep templates Infrastructure as Code (IaC) using tools like Terraform Custom Provisioning for unique enterprise requirements Standardizing this step makes every future deployment faster, safer, and reviewable. 🔐 Set Up Identity and Access Controls the Right Way No shared accounts. No “Owner” access to everyone. Use: Azure Active Directory (AAD) for identity management RBAC to ensure users only have access to what they need, where they need it This is a critical security layer—set it up with intent. 📈 Bake in Monitoring and Diagnostics from Day One Cloud environments must be observable. Implement: Log Analytics Workspace (LAW) to centralize logs Diagnostic Settings to capture platform-level signals Application Insights to monitor app health and performance These tools reduce time to resolution and help enforce SLAs. 🛡️ Review and Close on Security Posture Before allowing workloads to go live, conduct a security baseline check: Enable data encryption at rest and in transit Review and apply Azure Security Center recommendations Ensure ACC (Azure Confidential Computing) compliance if applicable Security is not a phase. It’s baked in throughout—but reviewed intentionally before go-live. 🚦 Validate Before You Launch Never skip a readiness review: Deploy in a test environment to validate templates and policies Get sign-off from architecture, security, and compliance stakeholders Track checklist completion before promoting anything to production This keeps surprises out of your production pipeline. In Closing: It’s Not Just a Checklist, It’s Your Blueprint When implemented well, this checklist becomes much more than a to-do list. It’s a blueprint for scalable, secure, and standardized cloud adoption. It helps teams stay on the same page, reduces firefighting, and accelerates real business value from Azure. Whether you're managing a new enterprise rollout or stabilizing an existing environment, this checklist keeps your foundation strong. Tags - Infrastructure Landing Zone Governance and Security Best Practices for Azure Infrastructure Landing Zones Automating Azure Landing Zone Setup with IaC Templates Checklist to Validate Azure Readiness Before Production Rollout Monitoring, Access Control, and Network Planning in Azure Landing Zones Azure Readiness Checklist for ProductionMicrosoft Azure Cloud HSM is now generally available
Microsoft Azure Cloud HSM is now generally available. Azure Cloud HSM is a highly available, FIPS 140-3 Level 3 validated single-tenant hardware security module (HSM) service designed to meet the highest security and compliance standards. With full administrative control over their HSM, customers can securely manage cryptographic keys and perform cryptographic operations within their own dedicated Cloud HSM cluster. In today’s digital landscape, organizations face an unprecedented volume of cyber threats, data breaches, and regulatory pressures. At the heart of securing sensitive information lies a robust key management and encryption strategy, which ensures that data remains confidential, tamper-proof, and accessible only to authorized users. However, encryption alone is not enough. How cryptographic keys are managed determines the true strength of security. Every interaction in the digital world from processing financial transactions, securing applications like PKI, database encryption, document signing to securing cloud workloads and authenticating users relies on cryptographic keys. A poorly managed key is a security risk waiting to happen. Without a clear key management strategy, organizations face challenges such as data exposure, regulatory non-compliance and operational complexity. An HSM is a cornerstone of a strong key management strategy, providing physical and logical security to safeguard cryptographic keys. HSMs are purpose-built devices designed to generate, store, and manage encryption keys in a tamper-resistant environment, ensuring that even in the event of a data breach, protected data remains unreadable. As cyber threats evolve, organizations must take a proactive approach to securing data with enterprise-grade encryption and key management solutions. Microsoft Azure Cloud HSM empowers businesses to meet these challenges head-on, ensuring that security, compliance, and trust remain non-negotiable priorities in the digital age. Key Features of Azure Cloud HSM Azure Cloud HSM ensures high availability and redundancy by automatically clustering multiple HSMs and synchronizing cryptographic data across three instances, eliminating the need for complex configurations. It optimizes performance through load balancing of cryptographic operations, reducing latency. Periodic backups enhance security by safeguarding cryptographic assets and enabling seamless recovery. Designed to meet FIPS 140-3 Level 3, it provides robust security for enterprise applications. Ideal use cases for Azure Cloud HSM Azure Cloud HSM is ideal for organizations migrating security-sensitive applications from on-premises to Azure Virtual Machines or transitioning from Azure Dedicated HSM or AWS Cloud HSM to a fully managed Azure-native solution. It supports applications requiring PKCS#11, OpenSSL, and JCE for seamless cryptographic integration and enables running shrink-wrapped software like Apache/Nginx SSL Offload, Microsoft SQL Server/Oracle TDE, and ADCS on Azure VMs. Additionally, it supports tools and applications that require document and code signing. Get started with Azure Cloud HSM Ready to deploy Azure Cloud HSM? Learn more and start building today: Get Started Deploying Azure Cloud HSM Customers can download the Azure Cloud HSM SDK and Client Tools from GitHub: Microsoft Azure Cloud HSM SDK Stay tuned for further updates as we continue to enhance Microsoft Azure Cloud HSM to support your most demanding security and compliance needs.
Private DNS and Hub–Spoke Networking for Enterprise AI Workloads on Azure
Introduction As organizations deploy enterprise AI platforms on Azure, security requirements increasingly drive the adoption of private-first architectures. Private networking only Centralized firewalls or NVAs Hub–and–spoke virtual network architectures Private Endpoints for all PaaS services While these patterns are well understood individually, their interaction often exposes hidden failure modes, particularly around DNS and name resolution. During a recent production deployment of a private, enterprise-grade AI workload on Azure, several issues surfaced that initially appeared to be platform or service instability. Closer analysis revealed the real cause: gaps in network and DNS design. This post shares a real-world technical walkthrough of the problem, root causes, resolution steps, and key lessons that now form a reusable blueprint for running AI workloads reliably in private Azure environments. Problem Statement The platform was deployed with the following characteristics: Hub and spoke network topology Custom DNS servers running in the hub Firewall / NVA enforcing strict egress controls AI, data, and platform services exposed through Private Endpoints Azure Container Apps using internal load balancer mode Centralized monitoring, secrets, and identity services Despite successful infrastructure deployment, the environment exhibited non-deterministic production issues, including: Container Apps intermittently failing to start or scale AI platform endpoints becoming unreachable from workload subnets Authentication and secret access failures DNS resolution working in some environments but failing in others Terraform deployments stalling or failing unexpectedly Because the symptoms varied across subnets and environments, root cause identification was initially non-trivial. Root Cause Analysis After end-to-end isolation, the issue was not AI services, authentication, or application logic. The core problem was DNS resolution in a private Azure environment. 1. Custom DNS servers were not Azure-aware The hub DNS servers correctly resolved: Corporate domains On‑premises records However, they could not resolve Azure platform names or Private Endpoint FQDNs by default. Azure relies on an internal recursive resolver (168.63.129.16) that must be explicitly integrated when using custom DNS. 2. Missing conditional forwarders for private DNS zones Many Azure services depend on service-specific private DNS zones, such as: privatelink.cognitiveservices.azure.com privatelink.openai.azure.com privatelink.vaultcore.azure.net privatelink.search.windows.net privatelink.blob.core.windows.net Without conditional forwarders pointing to Azure’s internal DNS, queries either: Failed silently, or Resolved to public endpoints that were blocked by firewall rules 3. Container Apps internal DNS requirements were overlooked When Azure Container Apps are deployed with: internal_load_balancer_enabled = true Azure does not automatically create supporting DNS records. The environment generates: A default domain .internal subdomains for internal FQDNs Without explicitly creating: A private DNS zone matching the default domain *, @, and *.internal wildcard records internal service-to-service communication fails. 4. Private DNS zones were not consistently linked Even when DNS zones existed, they were: Spread across multiple subscriptions Linked to some VNets but not others Missing links to DNS server VNets or shared services VNets As a result, name resolution succeeded in one subnet and failed in another, depending on the lookup path. Resolution No application changes were required. Stability was achieved entirely through architectural corrections. ✅ Step 1: Make custom DNS Azure-aware On all custom DNS servers (or NVAs acting as DNS proxies): Configure conditional forwarders for all Azure private DNS zones Forward those queries to: 168.63.129.16 This IP is Azure’s internal recursive resolver and is mandatory for Private Endpoint resolution. ✅ Step 2: Centralize and link private DNS zones A centralized private DNS model was adopted: All private DNS zones hosted in a shared subscription Linked to: Hub VNet All spoke VNets DNS server VNet Any operational or virtual desktop VNets This ensured consistent resolution regardless of workload location. ✅ Step 3: Explicitly handle Container Apps DNS For Container Apps using internal ingress: Create a private DNS zone matching the environment’s default domain Add: * wildcard record @ apex record *.internal wildcard record Point all records to the Container Apps Environment static IP Add a conditional forwarder for the default domain if using custom DNS This step alone resolved multiple internal connectivity issues. ✅ Step 4: Align routing, NSGs, and service tags Firewall, NSG, and route table rules were aligned to: Allow DNS traffic (TCP/UDP 53) Allow Azure service tags such as: AzureCloud CognitiveServices AzureActiveDirectory Storage AzureMonitor Ensure certain subnets (e.g., Container Apps, Application Gateway) retained direct internet access where required by Azure platform services Key Learnings 1. DNS is a Tier‑0 dependency for AI platforms Many AI “service issues” are DNS failures in disguise. DNS must be treated as foundational platform infrastructure. 2. Private Endpoints require Azure DNS integration If you use: Custom DNS ✅ Private Endpoints ✅ Then forwarding to 168.63.129.16 is non‑negotiable. 3. Container Apps internal ingress has hidden DNS requirements Internal Container Apps environments will not function correctly without manually created DNS zones and .internal records. 4. Centralized DNS prevents environment drift Decentralized or subscription-local DNS zones lead to fragile, inconsistent environments. Centralization improves reliability and operability. 5. Validate networking first, then the platform Before escalating issues to service teams: Validate DNS resolution Verify routing Check Private Endpoint connectivity In many cases, the perceived “platform issue” disappears. Quick Production Validation Checklist Before go-live, always validate: ✅ Private FQDNs resolve to private IPs from all required VNets ✅ UDR/NSG rules allow required Azure service traffic ✅ Managed identities can access all dependent resources ✅ AI portal user workflows succeed (evaluations, agents, etc.) ✅ terraform plan shows only intended changes Conclusion Running private, enterprise-grade AI workloads on Azure is absolutely achievable—but it requires intentional DNS and networking design. By: Making custom DNS Azure-aware Centralizing private DNS zones Explicitly handling Container Apps DNS Aligning routing and firewall rules an unstable environment was transformed into a repeatable, production-ready platform pattern. If you are building AI solutions on Azure with Private Endpoints and hub–spoke networking, getting DNS right early will save weeks of troubleshooting later.Extending Azure VM Image Automation with an MCP Operations Layer
Introduction In Part 1, we built a repeatable Azure VM image pipeline with Packer, Azure DevOps and Azure Compute Gallery, then validated the published image before promotion. Read Part 1 This second part adds a focused MCP operations layer. The build logic remains in Azure DevOps; the server exposes controlled tools for build status, validation evidence and optional pipeline queueing. The essential implementation is included directly in this article so the reader can create the local project, run it and understand each moving part without a separate code download. Solution at a glance: MCP host → Image Pipeline MCP server → policy boundary → Azure DevOps REST APIs → build, validation and promotion evidence What this article demonstrates Read recent image-pipeline runs and correlated build metadata. Run the server, invoke each tool and interpret the result. Keep write actions disabled by default and require explicit confirmation. Use the same tools from Visual Studio Code Copilot Chat. Where MCP fits The MCP server is a thin operations interface in front of the delivery system from Part 1. It does not replace Packer, Azure Pipelines, Azure Compute Gallery or Azure DevOps approval controls. Figure 1. MCP operations layer for image-pipeline queries and guarded execution. Design principle: Expose domain outcomes such as “show missing validation evidence”, not a broad unrestricted HTTP proxy. Prerequisites Visual Studio Code with GitHub Copilot Chat. Python 3.10 or later. uv for Python environment and dependency management. Node.js with npx available for MCP Inspector. An Azure DevOps organisation, project and image pipeline based on Part 1. Read access to pipeline runs and artefacts; queue permission only for guarded-write testing. Build and configure the sample 1. Create the local sample project Create a folder named azure-image-pipeline-mcp and add the following files. The sample keeps the MCP layer in one Python file so the complete flow remains visible in the article. Project structure azure-image-pipeline-mcp/ ├── .env ├── .vscode/ │ └── mcp.json ├── pyproject.toml └── server.py pyproject.toml [project] name = "azure-image-pipeline-mcp" version = "0.1.0" requires-python = ">=3.10" dependencies = [ "mcp[cli]>=2.2,<3", "httpx>=0.27,<1" ] [project.scripts] azure-image-pipeline-mcp = "server:main" 2. Install dependencies Create the environment uv --version uv sync 3. Create .env macOS or Linux touch .env Windows PowerShell New-Item -ItemType File .env Create .env in the project root. It holds a credential, so never commit it; if you use Git, add .env to .gitignore. 4. Configure Azure DevOps Initial configuration AZDO_ORGANIZATION=https://dev.azure.com/your-organisation AZDO_PROJECT=your-project AZDO_PIPELINE_ID=42 AZDO_PAT=your-local-development-token MCP_ALLOW_QUEUE_RUN=false MCP_ALLOWED_PARAMETERS=runtimeVersion,imageFlavor,enableSecurityScan,location,imageVersion AZDO_ORGANIZATION is the organisation URL. AZDO_PROJECT contains the image pipeline. AZDO_PIPELINE_ID is the numeric ID shown in the pipeline URL. AZDO_PAT is for compact local development only; keep its scope minimal and never commit it. MCP_ALLOW_QUEUE_RUN=false keeps write behaviour blocked. MCP_ALLOWED_PARAMETERS defines the accepted pipeline template parameters. 5. Add the MCP server implementation The server exposes three read-only tools and one guarded write tool. Azure DevOps remains responsible for the actual image build, validation and promotion. server.py: configuration and Azure DevOps client import base64 import os from urllib.parse import quote import httpx from mcp.server import MCPServer mcp = MCPServer("Azure Image Pipeline MCP") ORG = os.environ["AZDO_ORGANIZATION"].rstrip("/") PROJECT = quote(os.environ["AZDO_PROJECT"], safe="") PIPELINE_ID = int(os.environ["AZDO_PIPELINE_ID"]) PAT = os.environ["AZDO_PAT"] ALLOW_QUEUE = os.getenv("MCP_ALLOW_QUEUE_RUN", "false").lower() == "true" ALLOWED = {x.strip() for x in os.getenv( "MCP_ALLOWED_PARAMETERS", "runtimeVersion,imageFlavor,enableSecurityScan,location,imageVersion" ).split(",") if x.strip()} BASE = f"{ORG}/{PROJECT}/_apis" async def azdo(method: str, path: str, **kwargs): token = base64.b64encode(f":{PAT}".encode()).decode() headers = {"Authorization": f"Basic {token}", "Content-Type": "application/json"} async with httpx.AsyncClient(headers=headers, timeout=30) as client: response = await client.request(method, f"{BASE}/{path}", **kwargs) response.raise_for_status() return response.json() server.py: read-only tools @mcp.tool() async def list_recent_image_runs(top: int = 10) -> dict: data = await azdo( "GET", f"pipelines/{PIPELINE_ID}/runs", params={"api-version": "7.1", "$top": min(max(top, 1), 50)} ) return {"pipelineId": PIPELINE_ID, "runs": data.get("value", [])} @mcp.tool() async def get_image_run_status(run_id: int) -> dict: run = await azdo( "GET", f"pipelines/{PIPELINE_ID}/runs/{run_id}", params={"api-version": "7.1"} ) build = await azdo( "GET", f"build/builds/{run_id}", params={"api-version": "7.1"} ) return {"run": run, "build": build} @mcp.tool() async def get_image_run_evidence(run_id: int) -> dict: data = await azdo( "GET", f"build/builds/{run_id}/artifacts", params={"api-version": "7.1"} ) artifacts = data.get("value", []) expected = {"build-summary", "image-validation", "security-scan", "promotion-record"} found = {item.get("name") for item in artifacts} return {"runId": run_id, "artifacts": artifacts, "missing": sorted(expected - found)} server.py: guarded queue tool and entry point @mcp.tool() async def queue_image_pipeline( confirmation: str, runtime_version: str, image_flavour: str, location: str, enable_security_scan: bool = True, branch: str = "refs/heads/main", ) -> dict: if not ALLOW_QUEUE: raise PermissionError("Queueing is disabled") if confirmation != "QUEUE_IMAGE_PIPELINE": raise PermissionError("Explicit confirmation is required") parameters = { "runtimeVersion": runtime_version, "imageFlavor": image_flavour, "location": location, "enableSecurityScan": enable_security_scan, } unknown = set(parameters) - ALLOWED if unknown: raise ValueError(f"Unsupported parameters: {sorted(unknown)}") body = { "resources": {"repositories": {"self": {"refName": branch}}}, "templateParameters": parameters, } run = await azdo( "POST", f"pipelines/{PIPELINE_ID}/runs", params={"api-version": "7.1"}, json=body ) return {"queued": True, "run": run, "parameters": parameters} def main(): mcp.run() if __name__ == "__main__": main() 6. Validate the server locally Static validation python -m compileall -q server.py uv run python -c "import server; print('MCP server imports successfully')" Run the commands above from the project root. Successful completion confirms that server.py compiles and imports with the installed dependencies. The Inspector checks in the next section validate tool discovery and invocation. Validation scope: These static checks confirm that server.py compiles and imports. A live integration check still requires a valid Azure DevOps organisation, project, pipeline ID and credential. 7. Run the MCP server and try the tools Use MCP Inspector for a direct first test of the Python server. Inspector connects to the local server, discovers its registered tools and lets you call each tool with sample inputs before using the tools through Copilot Chat. Start the server from the project root uv run mcp dev server.py The command prints a local Inspector URL. Open that URL, select Tools, and confirm these four tools are available: list_recent_image_runs get_image_run_status get_image_run_evidence queue_image_pipeline Sample 1: list recent runs Tool and input Tool: list_recent_image_runs Input: { "top": 10 } What it does: queries the configured Azure DevOps image pipeline and returns recent run IDs, names, states, results, dates and links. Sample 2: inspect one run Tool and input Tool: get_image_run_status Input: { "run_id": 1234 } Replace 1234 with a real run ID returned by the first tool. The response correlates the pipeline run with build number, result, branch and source commit. Sample 3: check release evidence Tool and input Tool: get_image_run_evidence Input: { "run_id": 1234 } The server lists published artefacts and reports expected evidence that is missing, such as image validation, security scan, promotion record or build summary. Sample 4: confirm the write guard Safe negative test Tool: queue_image_pipeline Input: { "confirmation": "QUEUE_IMAGE_PIPELINE", "runtime_version": "21", "image_flavour": "cis", "location": "uksouth" } With MCP_ALLOW_QUEUE_RUN=false, the request must be rejected with “Queueing is disabled”. This confirms the fail-closed default without starting a pipeline. Live queue test Only in an approved development or test project, set MCP_ALLOW_QUEUE_RUN=true, restart the server and repeat the call. The exact confirmation value and allow-listed parameters are still required. Azure DevOps approvals and permissions remain authoritative. After the four checks succeed, stop the Inspector process with Ctrl+C. Visual Studio Code starts the same server itself through .vscode/mcp.json for normal Copilot Chat use. Use the MCP server from Visual Studio Code Visual Studio Code recognises the server from .vscode/mcp.json. The Python source does not register itself. When the project root is the workspace, Visual Studio Code reads the configuration, starts the stdio command, loads .env and performs MCP tool discovery. Discovery and start sequence Open the project root in Visual Studio Code | v Visual Studio Code reads .vscode/mcp.json | v Visual Studio Code starts: uv run python server.py | v The process loads ${workspaceFolder}/.env | v The MCP handshake discovers the four tools | v Tools appear in Copilot Chat > Agent > Configure Tools Important: Run code . from the azure-image-pipeline-mcp project root. If the parent directory is opened instead, ${workspaceFolder} can point to the wrong location. Workspace-level .vscode/mcp.json { "servers": { "azure-image-pipeline": { "type": "stdio", "command": "uv", "args": ["run", "python", "server.py"], "cwd": "${workspaceFolder}", "envFile": "${workspaceFolder}/.env" } } } Start the server and use it in Copilot Chat Open .vscode/mcp.json and select the inline Start action for azure-image-pipeline. Review the command, approve the trusted local server and confirm its status is Running. Open Copilot Chat, select Agent, then Configure Tools, and enable the four azure-image-pipeline tools. Try these prompts: Show the latest ten image pipeline runs. Show the status, branch and source commit for run 1234. Which release evidence is missing from run 1234? Prepare a Java 21 CIS image build in UK South and show the parameters before requesting confirmation. Troubleshooting Symptom Check uv is not recognised Restart the terminal and verify PATH. Inspector does not start Confirm Node.js and npx are available. Missing settings Confirm .env is in the project root. Azure DevOps returns 401 or 403 Check credential expiry and permissions. Wrong pipeline data Recheck project and numeric pipeline ID. Visual Studio Code does not show the server Validate .vscode/mcp.json and reload Visual Studio Code. Queueing is disabled Keep the safe default unless an intentional test is approved. Unsupported parameters Use only reviewed entries in MCP_ALLOWED_PARAMETERS. Production considerations Prefer Microsoft Entra-backed authentication or the hosted Azure DevOps MCP server where supported. Use least privilege and separate read from queue permissions. Never return tokens, headers or unredacted logs. Keep Azure DevOps approvals and checks as the release authority. Add bounded outputs, timeouts, rate limits and safe correlation logging before remote hosting. Conclusion Part 1 established the deterministic image lifecycle. This second part adds a practical operations surface: Visual Studio Code starts the local MCP server from workspace configuration, discovers the curated tools and uses Azure DevOps as the source of build and release truth. References Part 1: Automating Azure VM Image Baking and Validation with Packer and Azure DevOps Azure DevOps MCP server MCP Python SDK Visual Studio Code MCP configuration reference
Deploying Azure Redis Enterprise with Geo-Replication Using Terraform
This post walks through a production‑proven pattern for running stateful services across Azure regions using Terraform. We’ll cover a primary–replica Redis architecture, regional isolation with Key Vault and networking, and a clean Terraform parameterization strategy that scales from development to production without duplication. Why Multi‑Region State Is Hard Running applications globally is easy when everything is stateless—if something fails, you redeploy. But stateful services tell a different story. Caches, message brokers, and data stores can’t be treated as disposable. They hold business‑critical data, and downtime or inconsistency quickly becomes customer‑visible. In real‑world systems, common requirements include: Low‑latency reads from multiple regions Automatic recovery when a region becomes unavailable Predictable data consistency Repeatable infrastructure from dev through production Manually configuring this per region doesn’t scale. Drift sets in. Failover is unclear. Backups get forgotten. That’s where Terraform + Azure Managed Redis geo‑replication shines. Github Link : https://github.com/vsakash5/Managed-redis.git High‑Level Architecture We use a primary–replica Redis Enterprise model: Primary Redis Single write endpoint Highly available inside its region Source of truth Replica Redis Read‑only Asynchronously synced from primary Can be promoted during disaster recovery Each region is fully isolated: Separate subnets Separate Key Vaults Private Endpoints only (no public exposure) This prevents shared failure domains and allows each region to operate independently if needed. The Terraform Design Principle Instead of maintaining separate Terraform stacks per region, the key idea is: One reusable module, one tfvars file per environment, multiple regions inside it. The module is written once. Regional differences are supplied via parameter suffixes like: _replica _secondary _tertiary This keeps logic centralized and environments consistent. Core Parameter Layers 1. Environment Identity (Shared) Terraform environment = "dev" # dev | staging | prod context_prefix = "app" Show more lines These values are reused everywhere—names, tags, and identifiers. 2. Primary Region Terraform location = "eastus2" resource_group_name = "rg-app-dev-primary" Show more lines 3. Replica Region Terraform location_replica = "uksouth" resource_group_name_replica = "rg-app-dev-replica" The symmetry is intentional. Terraform can now apply the same module twice without branching logic. Regional Isolation: Networking and Secrets Why isolation matters Geo‑replication copies data, not dependencies. If both Redis instances depend on: the same subnet the same Key Vault then a failure in one region can cascade into the other. Networking (One Subnet per Region) Benefits: Independent NSGs Independent routing Independent capacity planning Key Vault (One per Region) Why this matters: Redis credentials are not replicated Each region stores its own secrets A Key Vault outage doesn’t take both regions down Redis Configuration Primary Redis (Writes Enabled) The geo‑replication group name must match. That’s the logical binding Azure uses to link instances. Private Endpoint‑Only Access No Redis instance is exposed publicly. Each region uses: A private endpoint A workload subnet Internal DNS resolution This means: No public IPs No inbound attack surface Traffic stays on the Azure backbone Linking Primary and Replica Terraform explicitly defines the relationship: Terraform managed_redis_geo_replication_config = { primary_to_replica = { primary_redis_key = "primary" replica_keys = ["replica"] } } Terraform ensures: Primary is created first Replica is deployed second Geo‑replication is established last Environment Scaling: Dev → Staging → Prod The infrastructure pattern never changes. Only values do. Environment Group Name Dev dev-grp Staging stg-grp Prod prod-grp This is how you avoid “snowflake” environments. Disaster Recovery Strategy If the primary region fails: Applications fail over to the replica read endpoint Terraform configuration is updated to: Remove geo‑replication Promote replica config to primary Traffic is fully restored Once the original region recovers, roles can be re‑established cleanly. No click‑ops. No guesswork. Key Lessons Learned 1. Naming is Infrastructure Predictable names enable automation, discovery, and auditing. 2. Key Vault Isolation Beats Availability A shared Key Vault is a shared outage. 3. Parameterization Beats Copy‑Paste Fix once → benefit everywhere. 4. Geo‑Replication Is a Contract Matching replication group names is non‑negotiable. 5. The tfvars File Is the Source of Truth If it’s not in Terraform, it’s not real. Final Thoughts Running stateful services in multiple regions doesn’t require magic— it requires discipline: Isolate aggressively Parameterize consistently Automate everything Test failure often With this approach, adding a new region becomes configuration—not redesign. That’s how infrastructure scales.Enterprise UAMI Design in Azure: Trust Boundaries and Blast Radius
As organizations move toward secretless authentication models in Azure, Managed Identity has become the preferred approach for enabling secure communication between services. User Assigned Managed Identity (UAMI) in particular offers flexibility that allows identity reuse across multiple compute resources such as: Azure App Service Azure Function Apps Virtual Machines Azure Kubernetes Service While this flexibility is beneficial from an operational perspective, it also introduces architectural considerations that are often overlooked during initial implementation. In enterprise environments where shared infrastructure patterns are common, the way UAMI is designed and assigned can directly influence the effective trust boundary of the deployment. Understanding Identity Scope in Azure Unlike System Assigned Managed Identity, a UAMI exists independently of the compute resource lifecycle and can be attached to multiple services across: Resource Groups Subscriptions Environments This capability allows a single identity to be reused across development, testing, or production services when required. However, identity reuse across multiple logical environments can expand the operational trust boundary of that identity. Any permission granted to the identity is implicitly inherited by all services to which the identity is attached. From an architectural standpoint, this creates a shared authentication surface across isolated deployment environments. High-Level Architecture: Shared Identity Pattern In many enterprise Azure deployments, it is common to observe patterns where: A single UAMI is assigned to multiple App Services The same identity is reused across automation workloads Identities are provisioned centrally and attached dynamically While this simplifies management and avoids identity sprawl, it may also introduce unintended privilege propagation across services. For example: In this architecture: Multiple App Services across environments share the same managed identity. Each compute instance requests an access token from Microsoft Entra ID using Azure Instance Metadata Service (IMDS). The issued token is then used to authenticate against downstream platform services such as: Azure SQL Database Azure Key Vault Azure Storage Because RBAC permissions are assigned to the shared identity rather than the compute instance itself, the effective authentication boundary becomes identity‑scoped instead of environment‑scoped. As a result, any compromised lower‑tier environment such as DEV may obtain an access token capable of accessing production‑level resources if those permissions are assigned to the shared identity. This expands the operational trust boundary across environments and increases the potential blast radius in the event of identity misuse. Blast Radius Considerations Blast radius refers to the potential impact scope of a security or configuration compromise. When a shared UAMI is used across multiple services, the following conditions may increase the blast radius: Design Pattern Potential Risk Single UAMI across environments Cross‑environment access Subscription‑wide RBAC assignment Broad privilege scope Identity used for automation pipelines Lateral movement Shared identity across teams Ownership ambiguity Because Managed Identity authentication relies on Azure Instance Metadata Service (IMDS), any compromised compute resource with access to IMDS may request an access token using the attached identity. This token can then be used to authenticate with downstream Azure services for which the identity has RBAC permissions. Enterprise Design Recommendations: Environment‑Isolated Identity Model To reduce identity blast radius in enterprise deployments, the following architectural principles may be considered: Environment‑Scoped Identity Provision separate UAMIs per environment: UAMI‑DEV UAMI‑UAT UAMI‑PROD Avoid reusing the same identity across isolated lifecycle stages. Resource‑Level RBAC Assignment Prefer assigning RBAC permissions at: Resource Resource Group instead of Subscription scope wherever feasible. Identity Ownership Model Ensure ownership clarity for identities assigned across shared workloads. Identity lifecycle should be aligned with: Application ownership Service ownership Deployment boundary Least Privilege Assignment Assign roles such as: Key Vault Secrets User Storage Blob Data Reader instead of broader roles such as: Contributor Owner Recommended High‑Level Architecture In this architecture: Each App Service instance is attached to an environment‑specific managed identity. RBAC assignments are scoped at the resource or resource group level. Microsoft Entra ID issues tokens independently for each identity. Trust boundaries remain aligned with deployment environments. A compromised DEV compute instance can only obtain a token associated with UAMI‑DEV. Because UAMI‑DEV does not have RBAC permissions for production resources, lateral access to PROD dependencies is prevented. Blast Radius Containment: This design significantly reduces the potential blast radius by ensuring that: Identity compromise remains environment‑scoped. Token issuance does not grant unintended cross‑environment privileges. RBAC permissions align with application ownership boundaries. Authentication trust boundaries match deployment lifecycle boundaries. Conclusion User Assigned Managed Identity offers significant advantages for secretless authentication in Azure environments. However, architectural considerations related to identity reuse and scope of assignment must be evaluated carefully in enterprise deployments. By aligning identity design with trust boundaries and minimizing the blast radius through scoped RBAC and environment isolation, organizations can implement Managed Identity in a way that balances operational efficiency with security governance.
🚀 Securing Enterprise AI: From Red Teaming to Risk Cards and Azure Guardrails
AI is no longer experimental—it’s deeply embedded into critical business workflows. From copilots to decision intelligence systems, organizations are rapidly adopting large language models (LLMs). But here’s the reality: AI is not just another application layer—it’s a new attack surface. 🧠 Why Traditional Security Thinking Fails for AI In traditional systems: You secure APIs You validate inputs You control access In AI systems: The input is language The behavior is probabilistic The attack surface is conversational 👉 Which means: You don’t just secure infrastructure—you must secure behavior 🔍 How AI Systems Actually Break (Red Teaming) To secure AI, we first need to understand how it fails. 👉 Red Teaming = intentionally trying to break your AI system using prompts 🧩 Common Attack Patterns 🪤 Jailbreaking “Ignore all previous instructions…” → Attempts to override system rules 🎭 Role Playing “You are a fictional villain…” → AI behaves differently under alternate identities 🔀 Prompt Injection Hidden instructions inside documents or inputs → Critical risk in RAG systems 🔁 Iterative Attacks Repeatedly refining prompts until the model breaks 💡 Key Insight AI attacks are not deterministic—they are creative, iterative, and human-driven 📊 From Attacks to Understanding Risk (Risk Cards) Knowing how AI breaks is only half the story. 👉 You need a structured way to define and communicate risk 🧠 What are Risk Cards? A Risk Card helps answer: What can go wrong? (Hazard) What is the impact? (Harm) How likely is it? (Risk) 🧪 Example: Prompt Injection Risk: External input overrides system behavior Harm: Data leakage, loss of control Affected: Organization, users Mitigation: Input sanitization + prompt isolation 🧪 Example: Hallucination Risk: Incorrect or fabricated output Harm: Wrong business decisions Mitigation: Grounding using trusted data (RAG) 💡 Critical Insight AI risk is not model-specific—it is context-dependent 🏢 Designing Secure AI Systems in Azure Now let’s translate all of this into real-world enterprise architecture 🔷 Secure AI Reference Architecture 🧱 Architecture Layers 1. User Layer Applications / APIs Azure Front Door + WAF 2. Security Layer (First Line of Defense) Azure AI Content Safety Input validation Prompt filtering 3. Orchestration Layer Azure Functions / AKS Prompt templates Context builders 4. Model Layer Azure OpenAI Locked system prompts 5. Grounding Layer (RAG) Azure AI Search Trusted enterprise data 6. Output Control Layer Response filtering Sensitive data masking 7. Monitoring & Governance Azure Monitor Defender for Cloud 🔐 Core Security Principles ❌ Never trust user input ✅ Validate both input and output ✅ Separate system and user instructions ✅ Ground responses in trusted data ✅ Monitor everything ⚠️ Real-World Risk Scenarios 🚨 Prompt Injection via Documents Malicious instructions hidden in uploaded files 👉 Mitigation: Document sanitization + prompt isolation 🚨 Data Leakage AI exposes sensitive or cross-user data 👉 Mitigation: RBAC + tenant isolation 🚨 Tool Misuse (AI Agents) AI triggers unintended real-world actions 👉 Mitigation: Approval workflows + least privilege 🚨 Gradual Jailbreak User bypasses safeguards over multiple interactions 👉 Mitigation: Session monitoring + context reset 📊 Operationalizing AI Security: Risk Register To move from theory to execution, organizations should maintain a Risk Register 🧠 Example Risk Impact Likelihood Score Prompt Injection 5 4 20 Hallucination 5 4 20 Bias 5 3 15 👉 This enables: Prioritization Governance Executive visibility 🚀 Bringing It All Together Let’s simplify everything: 👉 Red Teaming shows how AI breaks 👉 Risk Cards define what can go wrong 👉 Architecture determines whether you are secure 💡 One Line for Leaders “Deploying AI without guardrails is like exposing an API to the internet—understanding attack patterns and implementing layered defenses is essential for safe enterprise adoption.” 🙌 Final Thought AI is powerful—but power without control is risk The organizations that succeed will not just build AI systems… They will build: Secure, governed, and resilient AI platforms
