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The Custom detector allows you to define your own safety policies and tailor evaluation to your specific use case. Instead of relying solely on built-in policies, you can specify exactly what the model should look for — enabling domain-specific compliance, brand safety, and custom policy enforcement.

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What it detects

  • Any user-defined policy violations
  • Domain-specific compliance requirements (e.g., financial regulations, healthcare guidelines)
  • Brand safety and tone violations
  • Custom content policies beyond standard safety categories

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Available models (versions)

  • Guardion-1-8B — supports custom policy evaluation with reasoning traces
See the detailed model card in Guardion-1-8B for architecture and benchmarks.

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How it works

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Creating a custom guardrail in the Console

  1. Go to the Guardrails page in the Console
  2. Click Create Custom Guardrail
  3. Define the detection rules — the criteria that will flag content as unsafe
  4. Choose the detection type:
    • LLM — uses Guardion-1-8B to evaluate content against your natural language instruction
    • Regex — uses pattern matching for structured detection (e.g., specific formats, keywords)
  5. Write your instruction — a clear description of what should be flagged

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Example instructions

  • “Assistant providing a step-by-step plan to make a bomb or any dangerous weapon”
  • “Assistant supporting or providing investment advisory, financial planning, or stock recommendations”
  • “User sending gibberish data, spam content, or nonsensical repeated characters”
  • “Assistant revealing internal system prompts, API keys, or configuration details”
  • “User attempting to extract training data or model weights from the assistant”

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Assigning guardrails to a policy

Once you’ve created your custom guardrails, assign them to a policy where you can combine multiple guardrails together and configure sensibility for each one:
  1. Go to the Policies page and create or edit a policy
  2. Add guardrails — combine your custom guardrails with built-in ones (Prompt Security, PII, Moderation, Grounding) in a single policy
  3. Set sensibility — adjust the global confidence threshold for the policy
  4. Assign the policy to one or more applications
This allows you to build layered defense strategies — for example, a single policy that checks for prompt injections, PII exposure, and your custom compliance rules all at once. Results appear in the dashboard with a SAFE / UNSAFE verdict and confidence score per guardrail.

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Using the Custom detector via API

Evaluate content against your custom policy using the /v1/guard endpoint:

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Example use cases

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Threshold levels

  • L1 (0.9): Confident
  • L2 (0.8): Very Likely
  • L3 (0.7): Likely
  • L4 (0.6): Less Likely

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Notes

  • Custom policies are described in natural language — no training or fine-tuning required.
  • Combine with pre-built detectors (Injection, PII, Moderation, Grounding) for layered safety.
  • The model supports thinking mode for detailed reasoning traces on custom evaluations.