This repository shares practical security considerations and architecture patterns for building and reviewing GenAI workloads in cloud environments.
The goal is to document real security thinking around LLM systems, RAG architectures, AI APIs, and data protection — in a vendor-neutral way.
Generative AI systems introduce new security challenges beyond traditional applications:
• Prompt injection attacks
• Sensitive data exposure
• Weak identity boundaries
• Over-permissioned data sources
• Insecure model endpoints
• Lack of logging and governance
This repository documents practical patterns that architects and security engineers can use when designing AI-enabled platforms.
- LLM threat modeling
- Secure RAG architectures
- Identity and access for AI services
- Data protection strategies
- Secure AI API exposure
- Logging and monitoring for AI workloads
- Governance and security reviews
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docs/ – architecture notes and design considerations
patterns/ – reusable security patterns
checklists/ – security review checklists
diagrams/ – architecture diagrams
examples/ – sample implementation ideas
Cloud Architects
Security Engineers
DevSecOps Engineers
Platform Teams
AI Platform Builders
This repository focuses on practical guidance and architecture thinking rather than product-specific implementation.