Leveraging Large Language Models for Preliminary Security Risk Analysis: A Mission-Critical Case Study
This addresses the need for faster and more accurate preliminary security assessments in mission-critical industries, though it is incremental as it applies existing fine-tuning methods to a new domain-specific task.
The study tackled the challenge of slow and error-prone preliminary security risk analysis (PSRA) in mission-critical contexts by using a fine-tuned large language model (FTM), which reduced errors and hastened detection compared to seven human experts, leading to cost savings.
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.