Faithfulness is the degree to which an explanation matches the model’s actual internal process or decision path. In AI systems, especially those that generate chain-of-thought or natural-language justifications, a response can sound logical without accurately reflecting why the model chose it. A faithful explanation is not just plausible; it is causally aligned with the factors that shaped the output.
This matters in cyber security because unfaithful explanations can hide prompt sensitivity, bias, or manipulation. An attacker may influence a model with authority cues, misleading context, or crafted prompts while the visible rationale still looks clean and neutral. Defenders should test whether explanations track behavior under counterfactual prompts, input perturbations, and demographic or role-based variations. For high-impact uses, faithfulness supports auditability, incident review, and safer human oversight; without it, a convincing explanation may be little more than a polished story.