Third-party cyber evaluations involving OpenAI models
openai.com·14h ago
Data Science
Towards AI·Jun 23·5 min read
Building Enterprise-Grade Security Boundaries for LLM Calls — OAuth 2.0 + APIM + Entra ID
TL;DR
Security and access control for large language model (LLM) applications are often neglected. This article details the integration of Azure API Management and Entra ID to implement OAuth 2.0 authorization for GenAI applications.
✦ Why It Matters
Engineers can implement OAuth 2.0 to secure LLM applications, enhancing data protection and compliance.
Key Takeaways
- Implement OAuth 2.0 for secure API access
- Use Azure API Management for endpoint protection
- Integrate Entra ID for user authentication
- Establish authorization boundaries for LLMs
Full Summary
Large language models (LLMs) are increasingly deployed in applications, but ensuring their security is crucial. This article explores how to use Azure API Management (APIM) and Entra ID to implement OAuth 2.0, a widely used authorization framework, to secure access to LLM endpoints.
The approach involves setting up an authorization boundary that requires valid tokens for access, thereby preventing unauthorized calls. The methodology includes configuring APIM to handle OAuth 2.0 flows and integrating it with Entra ID for user authentication.
Results indicate that this setup significantly enhances security, allowing only authenticated users to interact with the LLM. This integration not only protects sensitive data but also ensures compliance with security standards.
Engineers can leverage these tools to build more secure AI applications.