Legal, regulatory, and insurance considerations when leveraging AI in investigations, RCA, and CAPA: Framing and operational guardrails for safety leadership

AI-powered tools are transforming how organizations conduct incident investigations, root cause analyses, and corrective and preventive actions by improving rigor, accelerating pattern detection, and strengthening organizational learning. But the increased specificity of AI-generated outputs raises a critical question: Does a more detailed investigative record create greater exposure if recommended actions are not implemented?

AI does not introduce a fundamentally new liability theory, but it changes the scale and accessibility of internal records that may become discoverable in litigation or regulatory proceedings. Meanwhile, many insurance programs predate AI in operational safety workflows, creating potential coverage gaps – particularly as AI-specific exclusions begin to appear in liability policies.

This new publication provides a governance framework that distinguishes draft outputs from approved conclusions, ensures disciplined evaluation of recommended actions, maintains appropriate privilege and retention controls, and addresses insurance coverage implications so that organizations can realize AI's safety benefits while preserving a defensible and insurable record.