  Preliminary security risk analysis (PSRA) provides a quick approach to
identify, evaluate and propose remeditation to potential risks in specific
scenarios. The extensive expertise required for an effective PSRA and the
substantial ammount of textual-related tasks hinder quick assessments in
mission-critical contexts, where timely and prompt actions are essential. The
speed and accuracy of human experts in PSRA significantly impact response time.
A large language model can quickly summarise information in less time than a
human. To our knowledge, no prior study has explored the capabilities of
fine-tuned models (FTM) in PSRA. Our case study investigates the proficiency of
FTM to assist practitioners in PSRA. We manually curated 141 representative
samples from over 50 mission-critical analyses archived by the industrial
context team in the last five years.We compared the proficiency of the FTM
versus seven human experts. Within the industrial context, our approach has
proven successful in reducing errors in PSRA, hastening security risk
detection, and minimizing false positives and negatives. This translates to
cost savings for the company by averting unnecessary expenses associated with
implementing unwarranted countermeasures. Therefore, experts can focus on more
comprehensive risk analysis, leveraging LLMs for an effective preliminary
assessment within a condensed timeframe.
