About the SecAI Framework

Background, methodology, and team behind the SecAI Framework.

Table of contents

  1. Framework Overview
    1. Mission Statement
    2. Framework Goals
  2. Framework Author
  3. Research Methodology
    1. Phase 1: Architecture Analysis (Weeks 1-4)
    2. Phase 2: Implementation & Testing (Weeks 5-12)
    3. Phase 3: Policy Development (Weeks 13-16)
    4. Phase 4: Real-World Validation (Weeks 17-24)
    5. Phase 5: Publication & Community (Ongoing)
  4. Research Standards
    1. Security & Privacy
    2. Quality Standards
  5. How This Research is Funded
  6. Contributing to This Research
    1. Ways to Contribute
    2. Contribution Guidelines
  7. Acknowledgments
    1. Technology Partners
    2. Community Contributors
    3. Inspiration & References
  8. Contact & Support
    1. Get in Touch
    2. Follow the Framework
  9. License & Usage
    1. Content License
    2. Code Examples License
    3. Trademark Notice
  10. Disclaimer
  11. Version History
  12. Frequently Asked Questions

Framework Overview

Mission Statement

To provide a comprehensive, production-ready Azure security assessment framework that enables systematic evaluation of Azure environments across configuration, process, and best practices dimensions.

Framework Goals

  1. Systematic Assessment - Provide automated scripts for comprehensive Azure security assessment
  2. Multi-Framework Validation - Validate against MCSB, CIS v8, NIST 800-53, PCI-DSS, CSA CCM
  3. Process Maturity - Evaluate operational effectiveness through structured interviews
  4. Share Knowledge - Publish methodology and tools for public benefit
  5. Build Community - Foster collaboration among security professionals

Framework Author

Derek Brent Moore
Security Architect

  • Background: Enterprise security architecture, cloud security, Azure assessments
  • Specialization: Multi-framework compliance validation, security architecture
  • Focus: Azure security assessments, compliance frameworks, automated security validation
  • Email: derek@zimax.net
  • LinkedIn: linkedin.com/in/derekbmoore
  • Twitter/X: @zimaxnet

Research Methodology

Phase 1: Architecture Analysis (Weeks 1-4)

Objectives:

  • Analyze Cursor IDE security model
  • Review Azure AI Foundry capabilities
  • Identify integration patterns
  • Map threat landscape

Activities:

  • Security architecture review
  • Data flow analysis
  • Privacy controls validation
  • Network security assessment

Phase 2: Implementation & Testing (Weeks 5-12)

Objectives:

  • Deploy reference architectures
  • Test security controls
  • Validate compliance alignment
  • Measure performance

Activities:

  • Azure environment deployment
  • Cursor Enterprise configuration
  • Private endpoint testing
  • Security validation

Phase 3: Policy Development (Weeks 13-16)

Objectives:

  • Create security policy templates
  • Develop SOPs
  • Document best practices
  • Build compliance frameworks

Activities:

  • Policy template creation
  • SOP documentation
  • Compliance mapping
  • Review and refinement

Phase 4: Real-World Validation (Weeks 17-24)

Objectives:

  • Test with confidential customer programs
  • Gather metrics and KPIs
  • Refine based on feedback
  • Document case studies

Activities:

  • Pilot deployments
  • Security monitoring
  • Cost analysis
  • Case study documentation

Phase 5: Publication & Community (Ongoing)

Objectives:

  • Publish findings to GitHub Pages
  • Share on social media
  • Accept community contributions
  • Maintain and update content

Activities:

  • Wiki publication
  • Social media engagement
  • Community contributions review
  • Continuous improvement

Research Standards

Security & Privacy

Confidentiality:

  • All customer data sanitized before publication
  • Identifying information removed or anonymized
  • Case studies reviewed and approved by customers
  • No actual secrets, credentials, or sensitive code published

Ethics:

  • No vulnerability research or exploitation
  • Responsible disclosure of any security issues found
  • Respect for intellectual property
  • Transparent about limitations and assumptions

Quality Standards

Documentation:

  • Peer-reviewed content
  • Tested configurations
  • Version-controlled documentation
  • Regular updates and corrections

Technical Accuracy:

  • All code examples tested
  • Configurations validated in lab environment
  • Screenshots and diagrams kept current
  • References to official documentation

How This Research is Funded

This research is funded by:

  1. Consulting Services: Revenue from enterprise consulting engagements
  2. Training Programs: Security training and workshops
  3. Personal Investment: Time and resources contributed by researchers

This research is independent and not sponsored by Microsoft, Cursor, Anthropic, or any AI vendor. All opinions and recommendations are our own.

Contributing to This Research

We welcome contributions from the security community!

Ways to Contribute

1. Submit Case Studies

  • Share your implementation experiences
  • Document lessons learned
  • Contribute anonymized metrics

2. Improve Documentation

  • Fix typos or errors
  • Add clarifications
  • Suggest improvements
  • Translate content

3. Share Security Insights

  • Report security considerations we missed
  • Suggest additional threat models
  • Provide feedback on recommendations

4. Test Configurations

  • Validate configurations in your environment
  • Report compatibility issues
  • Share optimization tips

Contribution Guidelines

Before Contributing:

  1. Read existing documentation thoroughly
  2. Check if issue/suggestion already exists
  3. Sanitize any confidential information
  4. Follow our code of conduct

Submission Process:

  1. Fork the GitHub repository
  2. Create a feature branch
  3. Make your changes
  4. Submit a pull request
  5. Respond to review feedback

What We Accept:

  • ✅ Security improvements
  • ✅ Documentation enhancements
  • ✅ Tested configurations
  • ✅ Case studies (anonymized)
  • ✅ Bug fixes

What We Don’t Accept:

  • ❌ Marketing or promotional content
  • ❌ Untested configurations
  • ❌ Security vulnerabilities (report privately)
  • ❌ Confidential customer information

Acknowledgments

Technology Partners

This research leverages:

  • Microsoft Azure - Cloud platform and AI services
  • Azure AI Foundry - Enterprise AI infrastructure
  • Cursor IDE - AI-powered code editor
  • GitHub Pages - Documentation hosting
  • Jekyll - Static site generator

Community Contributors

Thank you to everyone who has contributed to this research:

  • [Contributor Name] - [Contribution]
  • [Contributor Name] - [Contribution]
  • Your name here? - [Submit a contribution!]

Inspiration & References

This research builds upon work by:

  • Microsoft Azure Security team
  • NIST Cybersecurity Framework
  • CIS Benchmarks
  • OWASP AI Security Project
  • Cloud Security Alliance

Contact & Support

Get in Touch

For Framework Questions:

For Security Issues:

Follow the Framework

Stay updated on new developments:

License & Usage

Content License

This research is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0).

You are free to:

  • Share - Copy and redistribute the material in any medium or format
  • Adapt - Remix, transform, and build upon the material for any purpose, even commercially

Under the following terms:

  • Attribution - You must give appropriate credit, provide a link to the license, and indicate if changes were made

Code Examples License

All code examples in this repository are additionally available under the MIT License, allowing maximum flexibility for implementation.

Trademark Notice

  • “Cursor” is a trademark of Anysphere, Inc.
  • “Azure” and “Azure AI” are trademarks of Microsoft Corporation
  • “GitHub” is a trademark of GitHub, Inc.
  • This research is not officially endorsed by any of these companies

Disclaimer

Important Disclaimers:

  1. No Warranty: This research is provided “as-is” without warranty of any kind
  2. Professional Advice: Always consult your organization’s security team
  3. Compliance: Verify configurations meet your specific compliance requirements
  4. Testing: Test all configurations in non-production environments first
  5. Liability: Authors not responsible for any security incidents or data loss

Version History

Version Date Changes
1.0 October 2025 Initial publication
1.1 TBD Community feedback incorporated
2.0 TBD Major update with additional case studies

Frequently Asked Questions

Q: Is this research specific to certain industries? A: No, the security principles apply across industries. Case studies cover healthcare, finance, government, and general enterprise.

Q: Do I need Cursor Enterprise to follow this research? A: Most security principles apply to all Cursor versions, but some features (SSO, MDM) require Enterprise.

Q: Can I use this for other AI IDEs (GitHub Copilot, etc.)? A: Many principles are transferable, but specific configurations are Cursor-focused.

Q: How often is this research updated? A: Major updates quarterly, minor updates as needed based on feedback and new features.

Q: Can my company sponsor this research? A: We maintain independence, but appreciate contributions via community engagement and case study sharing.

Last Updated: October 10, 2025
Research Status: Active
Next Review: January 2026

Table of contents