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Documentation Index

Fetch the complete documentation index at: https://docs.vijil.ai/llms.txt

Use this file to discover all available pages before exploring further.

Instead of testing behavior in advance (like Diamond), Dome works in real time. It sits between the user and the agent, intercepting both inputs and outputs as they happen. This allows Dome to filter harmful content, detect suspicious patterns, and strengthen policies without slowing things down (latency stays under ~300ms). What makes Dome effective is its multi-layer approach. That means that Dome doesn’t rely on just one method, but combines these things:
  • simple pattern matching
  • machine learning classifiers
  • embedding-based similarity checks
  • and LLM-based evaluation
All of these components are working together just to catch different risks, whether they are some obvious types of policy violation or something that’s less obvious.

Defense Components

Under the hood, Dome follows a clear structure for how protection is applied: Guardrail → Guard → Detector You don’t need to configure everything from scratch, but if you try to understand this hierarchy it makes it easier to see how decisions are made and where controls are applied.

Defense Components

  • Guardrail
    A guardrail defines what kind of behavior you want to control. For example, blocking sensitive data, preventing prompt injection, or enforcing content policies.
  • Guard
    Guards are the building material inside a guardrail. Each guard is focused on a specific check or rule, such as scanning for secrets, toxic language, or unusual patterns.
  • Detector
    Detectors are the ones who actually evaluate the data. They analyze inputs and outputs and decide whether something should be marked, blocked, or modified.
When you put together these components, they allow Dome to apply protection.
Last modified on May 7, 2026