Inferagate
AI Security

Inspect prompts and responses before they become incidents.

Apply prompt security, DLP, output scanning, and policy enforcement across apps, tools, routes, providers, and workspaces.

Prompt protection

Detect risky instructions before model execution.

  • Prompt injection detection.
  • Jailbreak and malicious instruction detection.
  • Suspicious payload and unsafe content controls.
DLP and scanning

Control sensitive data in both directions.

  • Detect API keys, tokens, credentials, source code, and internal identifiers.
  • Scan user prompts, model responses, tool requests, and tool responses.
  • Block, redact, warn, monitor, or require approval based on policy.
Console areaGuardrails, Logs, Policies
Primary usersSecurity, platform, compliance
OutcomeApproved AI traffic with evidence
Step by step

Build guardrails and prove policy behavior before rollout.

Open guardrails
Inferagate guardrails policy control screen
  1. 01
    Create a policy bundle

    Start from Guardrails and select the protections that belong together for a route or workspace.

  2. 02
    Choose enforcement actions

    Set deny, warn, redact, or approval behavior per category so operators know the expected outcome.

  3. 03
    Run a simulation

    Use the test surface to confirm how prompts, responses, tool calls, and sensitive data are handled.

  4. 04
    Review evidence in logs

    Inspect sanitized, blocked, and passed events without exposing prompt content unnecessarily.