  DevOps is a necessity in many industries, including the development of
Autonomous Vehicles. In those settings, there are iterative activities that
reduce the speed of SafetyOps cycles. One of these activities is "Hazard
Analysis & Risk Assessment" (HARA), which is an essential step to start the
safety requirements specification. As a potential approach to increase the
speed of this step in SafetyOps, we have delved into the capabilities of Large
Language Models (LLMs).
  Our objective is to systematically assess their potential for application in
the field of safety engineering. To that end, we propose a framework to support
a higher degree of automation of HARA with LLMs. Despite our endeavors to
automate as much of the process as possible, expert review remains crucial to
ensure the validity and correctness of the analysis results, with necessary
modifications made accordingly.
