Real-World Case Studies

Sanitized examples from production Cursor deployments with Azure AI Foundry.

All case studies are anonymized and sanitized to protect confidential customer information. Identifying details have been removed or replaced with generic placeholders.

Table of contents

  1. Overview
  2. Case Study Template
  3. Case Study 1: Enterprise Cloud Migration
    1. Organization Profile
    2. Challenge
    3. Solution Architecture
    4. Implementation Details
    5. Security Controls Implemented
    6. Results
    7. Lessons Learned
    8. Recommendations
  4. Case Study 2: Healthcare Development (HIPAA)
    1. Organization Profile
    2. Challenge
    3. Solution Highlights
    4. Unique Security Controls
    5. Results
    6. Key Takeaway
  5. Case Study 3: Financial Services (PCI-DSS)
    1. Organization Profile
  6. Case Study 4: Government Agency (FedRAMP)
    1. Organization Profile
  7. Contribute Your Case Study

Overview

This section documents real-world implementations of Cursor IDE with Azure AI Foundry integration. Each case study provides:

  • Organization Profile: Industry, size, compliance requirements
  • Implementation Approach: Architecture decisions and configurations
  • Security Controls: Specific security measures deployed
  • Lessons Learned: Key takeaways and recommendations
  • Metrics: Performance, cost, and security KPIs

Case Study Template

Use this template for documenting your own implementations:

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# Case Study: [Project Name]

## Organization Profile
- **Industry**: [Healthcare/Finance/Government/etc.]
- **Team Size**: [number] developers
- **Compliance**: [SOC 2/HIPAA/FedRAMP/etc.]
- **Cloud Platform**: Azure

## Challenge
[What problem were you solving?]

## Solution
[How did you implement Cursor + Azure AI Foundry?]

### Architecture
[Diagram or description of your setup]

### Configuration
[Key configuration decisions]

## Security Controls
[What security measures did you implement?]

## Results
- **Metric 1**: [Baseline → Result]
- **Metric 2**: [Baseline → Result]
- **Cost**: [Budget and actual cost]

## Lessons Learned
### What Worked Well
- [Success 1]
- [Success 2]

### What We'd Do Differently
- [Challenge 1]
- [Challenge 2]

## Recommendations
[Advice for others implementing similar solutions]

Case Study 1: Enterprise Cloud Migration

This case study represents an anonymized composite of multiple similar implementations.

Organization Profile

  • Industry: Insurance
  • Team Size: 75 developers
  • Compliance Requirements: SOC 2 Type II, state insurance regulations
  • Existing Tools: VS Code, GitHub, Azure DevOps
  • Cloud Maturity: Intermediate (2 years on Azure)

Challenge

Organization was migrating legacy on-premises applications to Azure. Development team needed:

  1. Faster code generation for cloud-native patterns
  2. Consistent security practices across distributed teams
  3. Compliance with strict data residency requirements
  4. Cost-effective AI assistance without data sovereignty concerns

Key Constraint: No customer data or proprietary algorithms could leave Azure tenant.

Solution Architecture 

graph TB
    subgraph "Developer Workstations (75 devs)"
        A[Cursor IDE<br/>Privacy Mode]
    end
    
    subgraph "Azure Tenant (Central US)"
        B[Azure OpenAI<br/>Private Endpoint]
        C[Key Vault]
        D[Log Analytics]
    end
    
    subgraph "Monitoring"
        E[Azure Sentinel]
        F[Cost Management]
    end
    
    A -->|HTTPS/Private Link| B
    A -->|Secret Retrieval| C
    B --> D
    C --> D
    D --> E
    B --> F
    
    style A fill:#7c3aed,stroke:#5b21b6,color:#fff
    style B fill:#0066cc,stroke:#003d7a,color:#fff
    style E fill:#10b981,stroke:#059669,color:#fff

Implementation Details

Phase 1: Pilot (Weeks 1-4)

  • Selected 10-developer pilot team
  • Deployed Azure OpenAI with GPT-4 Turbo
  • Configured Cursor Enterprise with Privacy Mode
  • Established baseline metrics

Phase 2: Rollout (Weeks 5-8)

  • Expanded to 75 developers
  • Implemented automated onboarding
  • Configured Azure Monitor alerts
  • Established security monitoring

Phase 3: Optimization (Weeks 9-12)

  • Fine-tuned model selection by use case
  • Implemented cost controls and quotas
  • Enhanced security policies based on usage patterns

Security Controls Implemented

Control Implementation Validation
Data Sovereignty Azure OpenAI private endpoints only Network traffic analysis
Secret Management All API keys in Key Vault, monthly rotation Automated scanning
Access Control Entra ID + MFA + Conditional Access Quarterly access reviews
Audit Logging 730-day retention in Log Analytics Compliance audit passed
Privacy Mode MDM-enforced on all installations Weekly automated checks

Results

Security Metrics:

  • ✅ Zero secret exposure incidents
  • ✅ 100% compliance audit pass rate
  • ✅ Zero unauthorized access attempts
  • ✅ 100% of code stayed within Azure tenant

Productivity Metrics:

  • ⚡ 47% improvement in code writing velocity
  • ⏱️ 65% reduction in environment setup time (3 days → 8 hours)
  • 📈 2.3x faster onboarding for new developers
  • ✅ 89% developer satisfaction score

Cost Metrics:

  • 💰 $11.50/developer/month actual AI cost
  • 📊 ROI: 450% in first year
  • 💵 Total Cost of Ownership: $863/month for 75 developers

Lessons Learned

What Worked Well:

  1. Private Endpoints Were Worth the Complexity
    • Extra setup time paid off in security audits
    • Auditors appreciated network isolation
    • No public internet exposure simplified compliance
  2. Pilot Phase Was Critical
    • Identified configuration issues early
    • Built internal champions
    • Refined policies before wide deployment
  3. MDM Enforcement Prevented Issues
    • Automated Privacy Mode enforcement
    • Eliminated configuration drift
    • Reduced help desk tickets

What We’d Do Differently:

  1. Start with Cost Alerts Earlier
    • First month cost was 2x estimate
    • Developers used expensive models for simple tasks
    • Now have strict model selection guidelines
  2. Document .cursorignore Patterns Sooner
    • Initial implementations indexed too many files
    • Performance suffered until exclusions added
    • Now part of project templates
  3. Implement Role-Based Deployments
    • Junior developers don’t need GPT-4
    • Security reviewers need o1-preview
    • Cost optimization through targeted deployment

Recommendations

For organizations with similar requirements:

  1. Don’t Skip the Pilot: Test with representative team first
  2. Budget 30% Over Estimate: Usage patterns are hard to predict
  3. Invest in Monitoring: Good observability pays for itself
  4. Document Everything: Build runbooks as you go
  5. Train Your Team: Security awareness is critical

Case Study 2: Healthcare Development (HIPAA)

Organization Profile

  • Industry: Healthcare
  • Team Size: 25 developers
  • Compliance: HIPAA, HITRUST
  • Use Case: EHR system modernization
  • Critical Requirement: PHI must never leave private network

Challenge

Healthcare provider modernizing Electronic Health Record (EHR) system. Critical constraints:

  • HIPAA Compliance: PHI cannot be exposed to AI
  • Network Isolation: No public internet access for development
  • Audit Requirements: Complete audit trail of all AI usage
  • Data Classification: Separate handling for PHI vs non-PHI code

Solution Highlights

Architecture Decision: Hybrid approach

  • PHI-related code: Manual development only, NO AI assistance
  • Non-PHI code: AI assistance via Azure OpenAI in isolated VNet
  • Clear boundaries: Data classification labels in code

Key Configuration:

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{
  "cursor.ai.enabled": true,
  "cursor.ai.provider": "azure-openai",
  "cursor.ai.blockedPaths": [
    "**/phi/**",
    "**/patient-data/**",
    "**/ehr-core/**"
  ],
  "cursor.ai.requireApproval": [
    "**/integrations/**",
    "**/api/**"
  ]
}

Unique Security Controls

  1. Data Classification Integration
    • Code folders labeled (PHI, Internal, Public)
    • AI automatically disabled for PHI folders
    • Manual approval required for sensitive folders
  2. Split Environment
    • Separate Azure OpenAI for PHI vs non-PHI projects
    • Network segmentation enforced via NSGs
    • Separate audit logs per environment
  3. Enhanced Monitoring
    • Real-time alerts for AI usage in blocked paths
    • Daily security review of AI interactions
    • Automated compliance reporting

Results

Compliance:

  • ✅ Passed HIPAA audit with zero findings
  • ✅ HITRUST certification achieved
  • ✅ Zero PHI exposure incidents

Productivity:

  • Infrastructure Code: 60% faster (non-PHI)
  • API Development: 40% faster (with manual review)
  • PHI Code: Manual only (no AI), but other time saved

Cost:

  • $6.80/developer/month (lower usage due to restrictions)
  • Cost justified by audit success and time savings on non-PHI code

Key Takeaway

Hybrid Approach Works: Not all code needs AI assistance. Selectively enabling AI for low-risk code provides value while maintaining compliance.

Case Study 3: Financial Services (PCI-DSS)

Organization Profile

  • Industry: Financial Services
  • Team Size: 150 developers (multiple teams)
  • Compliance: PCI-DSS Level 1, SOC 2 Type II
  • Use Case: Payment processing platform modernization
  • Critical Requirement: Cardholder data protection

Content coming soon. This case study is currently in review.

Case Study 4: Government Agency (FedRAMP)

Organization Profile

  • Industry: Government
  • Team Size: 45 developers
  • Compliance: FedRAMP Moderate
  • Use Case: Citizen services digital transformation
  • Critical Requirement: Data residency and access controls

Content coming soon. This case study is currently in review.

Contribute Your Case Study

Have you implemented Cursor with Azure AI Foundry? We’d love to hear your story!

How to Contribute:

  1. Use the case study template above
  2. Sanitize all confidential information
  3. Submit via GitHub pull request
  4. Our team will review and publish

What to Share:

  • ✅ Architecture decisions and reasoning
  • ✅ Security controls and validation methods
  • ✅ Quantitative results (costs, metrics)
  • ✅ Lessons learned and recommendations

What to Exclude:

  • ❌ Company names and identifying details
  • ❌ Actual customer data or code
  • ❌ Specific security vulnerabilities
  • ❌ Proprietary configurations

Last Updated: October 10, 2025
Status: Accepting Contributions

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