The safest approach involves implementing a robust human-in-the-loop mechanism where the AI's decision to deny a runbook step is always subject to human review and explicit approval. The AI should be mandated to provide a clear, concise, and auditable justification for its denial, referencing predefined safety protocols, potential risks, or conflicting system states. This justification is crucial for operators to understand the AI's reasoning and either validate the denial or override it if the AI's assessment is deemed incorrect or incomplete. Furthermore, all denial events, including the AI's rationale and the subsequent human action, must be comprehensively logged and audited to ensure accountability and enable continuous improvement of the AI's decision-making model. Establishing dynamic thresholds and escalation paths for critical denials can further enhance safety, ensuring that high-impact rejections are immediately flagged to senior personnel for immediate attention and resolution.