Block unsafe AI actions before they execute.
DriftGard can run in Evaluation Mode for existing AI apps or Inline Enforcement mode for provider traffic. In Inline Enforcement, your app calls DriftGard's OpenAI-compatible gateway, DriftGard calls the configured provider, evaluates input and output, and returns the final response with audit evidence.
App -> DriftGard -> LLM
Every request receives a decision: observe, allow, block, redact, or escalate.
Runtime decisions for real production risk.
Runtime enforcement is not a dashboard. It is the control point that decides what an AI system is allowed to do before and after model execution.
AI responses
Block unsafe or non-compliant outputs before users see them. Return safe fallback messages when policy is violated.
Model governance
Allow all models by default, or enforce approved model lists per project with allow-and-flag or block behavior for unknown models.
Agent actions
Evaluate action type, user context, agent role, session history, and jurisdiction before execution.
Tool calls
Validate tool name, parameters, limits, identity, and business rules before external systems are touched.
Routing intelligence
Select eligible providers and models by capability, cost, or latency objective. Observe mode records routing evidence; enforce mode applies the selected route while Control Pack policies remain authoritative unless override is enabled.
One control point for every action.
Use the gateway directly or through the SDKs.
Applications can call POST /gateway/v1/chat/completions, Node services can use gatewayChatCompletions(), and Python services can use gateway_chat_completions(). The gateway accepts OpenAI-style requests while provider settings can target OpenAI-compatible APIs, Azure OpenAI deployments, or Anthropic Claude. Streaming variants are available for buffered or pass-through Server-Sent Events.
See an unsafe action blocked live.
Run a pilot against one high-risk AI workflow and see DriftGard enforce policy before execution.
Book demo