How to Add Guardrails to LiteLLM (and Secure It for Enterprise)

LiteLLM is presented as a multi-provider AI gateway focused on routing, load balancing, cost tracking, and rate limiting, with a configurable guardrail ecosystem of more than 40 third-party providers for prompt-injection detection, PII/PHI masking, secret detection, and content-policy enforcement. Guardrails can be configured in `config.yaml` to run before, during, or after model calls and can be enabled by request, API key, or team. The material argues that these native capabilities may not meet certain enterprise security and compliance needs, citing gaps in semantic intent-based enforcement, jurisdiction-aware routing, immutable inference-level audit trails, multi-agent visibility, adversarial testing, and compliance documentation. It describes NeuralTrust TrustGuard as a custom LiteLLM guardrail that evaluates inputs and outputs, returns allow, block, or transform decisions, requires no application-side changes, and offers choices such as fail-open versus fail-closed behavior and current-turn versus full-transcript inspection. It also notes a limitation for streaming responses, where post-call checks occur after response chunks have been sent, and distinguishes TrustGuard, which extends LiteLLM, from TrustGate, which is positioned as an alternative full AI gateway rather than a component intended to run alongside LiteLLM.

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