Module 13: SPIFFE for AI Infrastructure Slides

Slide Outline

1. SPIFFE for AI Infrastructure - Bonus: securing AI agents, LLM pipelines, and vector databases
2. Learning Objectives - 4 outcomes for this module
3. Why This Module Matters - AI infrastructure is the next frontier for workload identity. As AI agents become autonomous, they need verified identit
4. Before vs After - The operational shift this module teaches
5. The AI Identity Problem - Lesson section from the full module
6. SPIFFE for AI Agents - Lesson section from the full module
7. Securing MCP Servers - Lesson section from the full module
8. Vector Database Protection - Lesson section from the full module
9. Future of Workload Identity - Lesson section from the full module
10. Real-World Use Cases - AI agent authentication — autonomous agents proving identity to APIs, LLM endpoint protection — only authorized services can invoke expensive model calls
11. Common Mistakes to Avoid - 3 mistakes covered
12. Hands-On Labs - 1 hands-on lab
13. Key Takeaways - 5 points to remember

Learning Objectives

• Understand identity challenges in AI infrastructure
• Secure AI agent-to-service communication with SPIFFE
• Implement workload identity for ML pipelines
• Protect vector databases and model endpoints with mTLS

Why This Module Matters

AI infrastructure is the next frontier for workload identity. As AI agents become autonomous, they need verified identities to access tools, databases, and other services. The same SPIFFE infrastructure you build for microservices today secures your AI agents tomorrow. This is not theoretical — it is already happening in production systems.

Common Mistakes

• Using shared API keys for AI agents (one compromised key exposes all models)
• Not scoping AI agent permissions — an agent that can query should not be able to train
• Treating AI workloads differently from microservices — they need the same identity primitives

Key Takeaways

• AI agents are workloads — they need cryptographic identity, not API keys
• SPIFFE secures agent-to-LLM, agent-to-tool, and agent-to-database communication
• MCP servers should verify agent identity before granting tool access
• The zero trust skills you learn for microservices apply directly to AI infrastructure
• Early adoption of workload identity for AI is a career differentiator

Hands-On Labs

1. Securing AI Agent Communication

   Give AI agents SPIFFE identities and enforce access policies.

   • Deploy a simulated AI agent with SPIFFE identity
   • Deploy an LLM mock service with SPIFFE identity
   • Configure mTLS between agent and LLM service
   • Add OPA policy: only authorized agents can call the LLM endpoint

   View lab files on GitHub