Modern distributed systems present unprecedented challenges for incident response, with telemetry volumes and architectural complexity overwhelming human cognitive capacity during critical outages. This article examines the integration of large language models as copilots for incident management, proposing a comprehensive framework that balances the speed advantages of artificial intelligence with rigorous safety controls. The article identifies three critical failure points in incident response—sense-making across disparate telemetry sources, hypothesis generation under stress, and safe mitigation execution—where AI assistance shows promise but also introduces significant risks, including hallucination, privilege boundary violations, and lack of production constraint awareness. Drawing on frameworks for AI risk management, software supply chain security, and human-AI collaboration, the article presents a three-phase architecture separating sensing, deciding, and acting with mandatory human validation gates between transitions. The proposed multi-layer safety framework encompasses data governance through automated redaction and schema validation, privilege architecture implementing separation of duties and risk budgets, verification mechanisms including counterfactual checking and shadow execution, and comprehensive auditability through immutable decision ledgers. Human-AI collaboration patterns emphasize augmentation rather than replacement of human judgment, with AI providing rapid data synthesis and pattern matching while humans contribute contextual reasoning, ethical judgment, and final decision authority. The framework demonstrates that bounded automation with explicit oversight can reduce detection and restoration times while preserving the reliability guarantees and accountability requirements that production systems demand, offering organizations a practical path to leveraging AI assistance without compromising operational safety.