This paper presents VIGIL, a novel part-centric structured forensic framework for deepfake detection using multimodal large language models. The approach employs a plan-then-examine pipeline where the model first plans which facial parts warrant inspection based on global visual cues, then examines each part with independently sourced forensic evidence. A stage-gated injection mechanism delivers part-level forensic evidence only during examination to ensure unbiased part selection. The framework is inspired by expert forensic practice and aims to improve the reliability of deepfake detection by separating evidence generation from manipulation localization, addressing the issue of hallucinated explanations in current MLLM-based methods.