Azure Well Architected Review

Azure Well-Architected Review

This workflow performs a structured Azure Well-Architected Framework (WAF) review against your workload's IaC files and deployed infrastructure. It identifies risks across all 5 WAF pillars and creates GitHub issues to track remediation.

When to invoke

• "Run an Azure well-architected review."
• "Assess this Azure workload against WAF pillars."
• "Review our Bicep or Terraform for Azure architecture risks."
• "Create GitHub issues for Azure Well-Architected findings."

Prerequisites and context

• Azure CLI (az) configured and authenticated
• IaC files present in the repository (Bicep, Terraform, or ARM templates)
• GitHub MCP server configured and authenticated

Procedure

Step 1: Load Well-Architected Framework Reference

Fetch current Azure WAF best practices:

• https://learn.microsoft.com/en-us/azure/well-architected/
• Service guides for the Azure services in use (https://learn.microsoft.com/en-us/azure/well-architected/service-guides/)
• Workload-specific guidance relevant to the workload type (SaaS, mission-critical, AI, etc.)

If the microsoft.docs.mcp MCP server is available, use it to query the latest pillar checklists and service-specific recommendations.

Step 2: Discover IaC & Architecture

Establish the review scope, then inventory both the code and the live environment:

1. Confirm the Azure scope: Ask the user which subscription(s)/resource group(s) are in scope, or infer them from IaC parameters and confirm.
2. Scan the repository for IaC files:
   • Bicep: **/*.bicep, bicepconfig.json
   • Terraform: **/*.tf (azurerm/azapi providers)
   • ARM templates: **/azuredeploy*.json, **/*.template.json, files with $schema containing deploymentTemplate
3. Inventory live resources (always, even when IaC exists): az resource list --resource-group <rg> --output json (or subscription-wide), plus targeted az <service> show calls for configuration details the pillar checks need.
4. Compare IaC with live inventory: Flag drift — resources present in Azure but absent from IaC (portal-created), resources defined in IaC but not deployed, and configuration mismatches. Record drift findings for Step 3 (they typically map to the Operational Excellence pillar).

Identify key Azure services in use (compute, data, networking, security, observability) and generate a Mermaid architecture diagram.

Step 3: Pillar-by-Pillar Review

Pillar 1: Reliability

• Availability zones enabled for zonal services (VMs, VMSS, AKS node pools, App Service, SQL, Storage ZRS)
• Production SKUs support the required SLA (no Basic/Free tiers on critical paths)
• Azure SQL / Cosmos DB backup and point-in-time restore configured with appropriate retention
• Geo-redundancy configured where RPO requires it (GRS/RA-GRS storage, SQL failover groups, Cosmos DB multi-region)
• Autoscale rules configured for App Service plans, VMSS, AKS (no fixed single instance for production)
• Health probes configured on Load Balancer / Application Gateway / Front Door backends
• Dead-lettering enabled for Service Bus queues/subscriptions and Event Grid subscriptions
• Retry policies with exponential backoff implemented for transient fault handling
• Disaster recovery plan defined (documented RTO/RPO, tested failover)

Pillar 2: Security

• Managed identities used instead of service principals with secrets or connection strings
• No hardcoded credentials, keys, or connection strings in IaC or code
• Secrets stored in Azure Key Vault with RBAC authorization (not access policies)
• Storage accounts deny public blob access and disallow shared key access where possible
• Private endpoints (or at minimum service endpoints + firewall rules) for PaaS data services
• NSGs restrict inbound traffic to minimum required ports/CIDRs (no * → * allow rules)
• TLS 1.2+ enforced on all endpoints (minimumTlsVersion, httpsOnly)
• Azure RBAC follows least privilege (no Owner/Contributor at subscription scope for workload identities)
• Microsoft Defender for Cloud enabled on relevant resource types (az security pricing list)
• Azure WAF (Application Gateway or Front Door) configured for public-facing web endpoints
• Diagnostic settings send security logs to Log Analytics / Microsoft Sentinel

Pillar 3: Cost Optimization

• Reservations or savings plans evaluated for steady-state compute (VMs, App Service, SQL)
• Storage lifecycle management policies move blobs to cool/archive tiers
• Right-sized SKUs based on actual utilization (no oversized VMs/App Service plans)
• Dev/test environments use auto-shutdown schedules and Dev/Test pricing where eligible
• Azure Budgets and cost alerts configured (az consumption budget list)
• Unattached managed disks and orphaned public IPs identified and removed
• Consumption/serverless tiers used for spiky or low-volume workloads (Functions, Container Apps, SQL serverless)
• Log Analytics retention and data-cap settings tuned to avoid ingestion overruns

Pillar 4: Operational Excellence

• All infrastructure defined as IaC (no manual portal changes; deny assignments or policy where feasible)
• Consistent tagging strategy applied across all resources (owner, environment, cost center)
• Azure Monitor alerts defined for key metrics and service health
• Automated deployment pipeline present (GitHub Actions / Azure Pipelines, no manual deployments)
• Azure Activity Log and resource diagnostic settings routed to Log Analytics
• Application Insights (or OpenTelemetry equivalent) instrumented for application workloads
• Azure Policy assignments enforce organizational standards (allowed locations, SKUs, tags)
• Runbooks or operational documentation present

Pillar 5: Performance Efficiency

• Right-sized compute SKUs validated against load requirements
• Caching implemented where beneficial (Azure Cache for Redis, CDN/Front Door caching)
• Azure Front Door or CDN used for global static content delivery
• Autoscale based on load metrics rather than fixed instance counts
• Database performance tier appropriate (DTU vs vCore, elastic pools, Cosmos DB RU autoscale)
• Premium/zone-redundant storage used for latency-sensitive disk workloads
• Connection pooling and async patterns used for database and HTTP clients

Step 4: Risk Classification

For each finding, classify:

• High Risk: Security vulnerability, single point of failure, no backup/recovery
• Medium Risk: Suboptimal reliability, cost inefficiency, performance concern
• Low Risk: Best practice deviation, minor optimization opportunity

Step 5: User Confirmation

 Azure Well-Architected Review Summary

 Review Results:
• IaC Files Analyzed: X
• Azure Services Identified: Y
• Total Findings: Z
  • High Risk: A (immediate action required)
  • Medium Risk: B (should address soon)
  • Low Risk: C (nice to have)

 Top High Risk Findings:
1. [Pillar]: [Finding] — [Why it matters]
2. [Pillar]: [Finding] — [Why it matters]

 This will create Z individual GitHub issues + 1 EPIC issue.

 Proceed with creating GitHub issues? (y/n)

Gate: Only proceed to Steps 6–7 if the user gives an explicit affirmative response (e.g. "y", "yes"). On a negative, ambiguous, or missing response, do not create any GitHub issues — output the full findings as formatted markdown to the console and stop.

Step 6: Create Individual Finding Issues

Label with "well-architected" and the pillar name (e.g., "security", "reliability").

Title: [WAF-<PILLAR>] [Brief Finding] — [Risk Level]

Body:

## Well-Architected Finding: [Brief Title]

**Pillar**: [Name] | **Risk Level**: [High/Medium/Low] | **Effort**: [Low/Medium/High]

### Description
[Clear explanation of the finding and why it matters]

### Remediation

**IaC Fix** (preferred):
```bicep
// Bicep example
resource storageAccount 'Microsoft.Storage/storageAccounts@2023-05-01' = {
  name: storageAccountName
  location: location
  sku: { name: 'Standard_ZRS' }
  kind: 'StorageV2'
  properties: {
    minimumTlsVersion: 'TLS1_2'
    allowBlobPublicAccess: false
    supportsHttpsTrafficOnly: true
  }
}
```

**Azure CLI fallback**:
```bash
az storage account update --name <name> --resource-group <rg> \
  --min-tls-version TLS1_2 --allow-blob-public-access false --https-only true
```

### Azure Reference
- [WAF Best Practice Link]
- [Microsoft Learn Documentation Link]

### Validation
- [ ] Change implemented in IaC and deployed
- [ ] Azure Policy compliance passes (if applicable)
- [ ] Microsoft Defender for Cloud recommendation resolved (if applicable)

**Well-Architected Recommendation**: [WAF checklist item this maps to]

Step 7: Create EPIC Tracking Issue

Label with "well-architected" and "epic".

Title: [EPIC] Azure Well-Architected Review — X findings across 5 pillars

Body: Executive summary with pillar breakdown table (finding counts by pillar and risk level), Mermaid architecture diagram, prioritized checklist linking all individual issues (High → Medium → Low), and success criteria:

• All High-risk findings resolved
• Medium findings have accepted mitigation plans
• No regression in existing Azure Monitor alerts or Azure Policy compliance

Error Handling

• No IaC Files Found: Limit review to live resource discovery via Azure CLI (az resource list) and note the gap
• Insufficient Azure Permissions: List required read-only roles for the review (Reader, Security Reader)
• GitHub Creation Failure: Output all findings as formatted markdown to console

Output template

## Azure Well-Architected review result

**Status:** issues created | findings only | blocked
**Scope:** `<subscription/resource group>`

| Pillar | Risk | Finding | Evidence | Remediation | Issue |
| --- | --- | --- | --- | --- | --- |
| Security | High Risk | `<finding>` | `<IaC/live evidence>` | `<IaC or az command>` | `<url or not created>` |

### Architecture
```mermaid
graph TD
  A[User] --> B[Azure workload]

## Quality gate
- [ ] All 5 WAF pillars reviewed against IaC and live infrastructure
- [ ] All findings classified by risk level and pillar
- [ ] Actionable remediation steps with IaC examples for each finding
- [ ] GitHub issues created for team tracking
- [ ] Architecture diagram generated for EPIC context
- [ ] Microsoft Learn documentation references included