DriftGard Request Demo
Runtime enforcement

Block unsafe AI actions before they execute.

DriftGard sits in the request path between an AI decision and business impact. It evaluates responses, model usage, tool calls, risk context, and policy rules before the action reaches a user or production system.

Request Demo Explore Tool Governance

AI Agent -> DriftGard -> Action

Every action receives a decision: allow, block, redact, or escalate.

AllowSafe action continues to the user, tool, or workflow.
BlockUnsafe action is stopped before execution.
EscalateHigh-risk action routes to human approval.
RecordDecision, reason, and policy evidence are preserved.
Pre-action checks Fallback handling Model governance Circuit breaker Session chain tracking Audit evidence
What it controls

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.

01

AI responses

Block unsafe or non-compliant outputs before users see them. Return safe fallback messages when policy is violated.

02

Model governance

Allow all models by default, or enforce approved model lists per project with allow-and-flag or block behavior for unknown models.

03

Agent actions

Evaluate action type, user context, agent role, session history, and jurisdiction before execution.

04

Tool calls

Validate tool name, parameters, limits, identity, and business rules before external systems are touched.

Architecture

One control point for every action.

AI decision-> DriftGard policy check-> Allow / Block / Escalate-> Tool / User / System

See an unsafe action blocked live.

Run a pilot against one high-risk AI workflow and see DriftGard enforce policy before execution.

Run a Pilot Audit