# Leveraging Large Language Models for Preliminary Security Risk Analysis: A Mission-Critical Case Study

arXiv: https://arxiv.org/abs/2403.15756 · HTML: https://mlsanity.com/paper/2403.15756/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 17 references that are in the map.
- First posted on arXiv 23 Mar 2024.

## Builds on (most cited first, 2 of 2)

- [Unit Test Case Generation with Transformers and Focal Context](https://mlsanity.com/paper/2009.05617/) (2020)
- [InvestLM: A Large Language Model for Investment using Financial Domain Instruction Tuning](https://mlsanity.com/paper/2309.13064/) (2023)

## Built on by (most cited first, 10 of 10)

- [Beyond Words: On Large Language Models Actionability in Mission-Critical Risk Analysis](https://mlsanity.com/paper/2406.10273/) (2024)
- [On Large Language Models in Mission-Critical IT Governance: Are We Ready Yet?](https://mlsanity.com/paper/2412.11698/) (2024)
- [Opportunities and Challenges of Generative-AI in Finance](https://mlsanity.com/paper/2410.15653/) (2024)
- [6GSoft: Software for Edge-to-Cloud Continuum](https://mlsanity.com/paper/2407.05963/) (2024)
- [Artificial Intelligence as the New Hacker: Developing Agents for Offensive Security](https://mlsanity.com/paper/2406.07561/) (2024)
- [A Call for Critically Rethinking and Reforming Data Analysis in Empirical Software Engineering](https://mlsanity.com/paper/2501.12728/) (2025)
- [Descriptor: C++ Self-Admitted Technical Debt Dataset (CppSATD)](https://mlsanity.com/paper/2505.01136/) (2025)
- [Autonomic Microservice Management via Agentic AI and MAPE-K Integration](https://mlsanity.com/paper/2506.22185/) (2025)
- [The Invisible Hand of AI Libraries Shaping Open Source Projects and Communities](https://mlsanity.com/paper/2601.01944/) (2026)
- [Evaluating Large Language Models for Detecting Architectural Decision Violations](https://mlsanity.com/paper/2602.07609/) (2026)

## Abstract (arXiv)

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.