Building a Secure Internal AI Assistant for Enterprise
This article discusses how enterprises can build an internal AI assistant that is more useful to employees than public tools like ChatGPT, while also meeting security and compliance requirements.
Why it matters
Enterprises need to provide employees with productive AI tools, but cannot risk exposing sensitive data to public services. This architecture addresses both needs.
Key Points
- 1Banning public AI tools doesn't work - employees will find ways to use them anyway
- 2An internal AI assistant can provide company-specific answers and data, making it more useful than public tools
- 3The architecture includes authentication, PII detection, internal search, LLM inference, and audit logging
Details
The article outlines a technical architecture for building an internal AI assistant that can be more useful to employees than public tools like ChatGPT. The key components include: 1) Authentication through the company's existing identity provider, 2) PII detection and redaction before queries leave the internal network, 3) An internal search pipeline that combines vector search, keyword search, and structured data lookup, 4) LLM inference hosted internally or through a private API, and 5) Comprehensive audit logging. This approach allows the AI assistant to provide company-specific answers and access relevant internal data, while maintaining security and compliance controls.
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