Domain grounding is the practice of tying an AI system’s answers to authoritative, domain-specific references such as policies, manuals, logs, or verified databases. Instead of generating a response from general language patterns alone, the model is constrained to use evidence that belongs to the task at hand. This reduces the chance that a fluent but wrong answer is treated as reliable.
In cyber security, domain grounding matters because security decisions depend on exact details: access rules, alert meanings, configuration states, and incident procedures. It is used in defenses such as retrieval-augmented assistants, policy-aware chatbots, and analyst tools that cite approved sources before responding. Attackers also target weak grounding by prompting models to ignore source data, invent certainty, or follow untrusted text. Strong grounding, combined with human review, helps prevent hallucinations from becoming operational mistakes.