Reading
An enterprise-grade AI proxy framework that provides comprehensive security protection for large language model (LLM) inference endpoints through prompt injection firewall, PII desensitization, RBAC permission control, and trusted execution environment.
Section 01
An enterprise-grade AI proxy framework that provides comprehensive security protection for large language model (LLM) inference endpoints through prompt injection firewall, PII desensitization, RBAC permission control, and trusted execution environment.
Section 02
Section 03
With the widespread application of large language models (LLMs) in enterprise scenarios, security issues have become increasingly prominent. Enterprises face multiple risks when using LLM APIs:
Section 04
Attackers may embed malicious instructions in user inputs, attempting to make the model perform unintended operations, such as leaking system prompts, bypassing content security policies, or executing unauthorized data access.
Section 05
Users may inadvertently disclose personally identifiable information (PII), trade secrets, or regulated data in conversations. If this information is sent to third-party LLM service providers, it will pose serious compliance risks.
Section 06
Traditional API calls often lack fine-grained access control, making it difficult to implement differentiated model access strategies based on user roles, data sensitivity, or business scenarios.
Section 07
For industries with extremely high data security requirements such as finance, healthcare, and government, sending sensitive data to external cloud services may violate data localization or data sovereignty regulations.
The Zero Trust AI API project is designed to address these security challenges. It is not just a simple API wrapper, but a complete enterprise-grade security framework that introduces the zero-trust security concept into LLM inference endpoints.
Section 08
The project adopts a layered defense security architecture, implementing strict security controls at each processing stage:
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