# On Large Language Models in Mission-Critical IT Governance: Are We Ready Yet?

arXiv: https://arxiv.org/abs/2412.11698 · HTML: https://mlsanity.com/paper/2412.11698/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 46 references that are in the map.
- First posted on arXiv 16 Dec 2024.

## Builds on (most cited first, 10 of 10)

- [Large Language Models Can Be Easily Distracted by Irrelevant Context](https://mlsanity.com/paper/2302.00093/) (2023)
- [GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models](https://mlsanity.com/paper/2410.05229/) (2024)
- [Large Language Models for Software Engineering: Survey and Open Problems](https://mlsanity.com/paper/2310.03533/) (2023)
- [Marco-o1: Towards Open Reasoning Models for Open-Ended Solutions](https://mlsanity.com/paper/2411.14405/) (2024)
- [Leveraging Large Language Models for Preliminary Security Risk Analysis: A Mission-Critical Case Study](https://mlsanity.com/paper/2403.15756/) (2024)
- [Beyond Words: On Large Language Models Actionability in Mission-Critical Risk Analysis](https://mlsanity.com/paper/2406.10273/) (2024)
- [6GSoft: Software for Edge-to-Cloud Continuum](https://mlsanity.com/paper/2407.05963/) (2024)
- [Developers' Perception of GitHub Actions: A Survey Analysis](https://mlsanity.com/paper/2303.04084/) (2023)
- [Practical relevance of software engineering research: Synthesizing the community's voice](https://mlsanity.com/paper/1812.01395/) (2018)
- [Early Career Developers' Perceptions of Code Understandability. A Study of Complexity Metrics](https://mlsanity.com/paper/2303.07722/) (2023)

## Built on by (most cited first, 9 of 9)

- [Generative AI for Software Architecture. Applications, Challenges, and Future Directions](https://mlsanity.com/paper/2503.13310/) (2025)
- [What Were You Thinking? An LLM-Driven Large-Scale Study of Refactoring Motivations in Open-Source Projects](https://mlsanity.com/paper/2509.07763/) (2025)
- [A Call for Critically Rethinking and Reforming Data Analysis in Empirical Software Engineering](https://mlsanity.com/paper/2501.12728/) (2025)
- [Can AI Agents Generate Microservices? How Far are We?](https://mlsanity.com/paper/2603.09004/) (2026)
- [Autonomic Microservice Management via Agentic AI and MAPE-K Integration](https://mlsanity.com/paper/2506.22185/) (2025)
- [Perspective Dial: Measuring Perspective of Text and Guiding LLM Outputs](https://mlsanity.com/paper/2506.23377/) (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)
- [Induced Numerical Instability: Hidden Costs in Multimodal Large Language Models](https://mlsanity.com/paper/2603.04453/) (2026)

## Abstract (arXiv)

Context. The security of critical infrastructure has been a pressing concern since the advent of computers and has become even more critical in today's era of cyber warfare. Protecting mission-critical systems (MCSs), essential for national security, requires swift and robust governance, yet recent events reveal the increasing difficulty of meeting these challenges. Aim. Building on prior research showcasing the potential of Generative AI (GAI), such as Large Language Models, in enhancing risk analysis, we aim to explore practitioners' views on integrating GAI into the governance of IT MCSs. Our goal is to provide actionable insights and recommendations for stakeholders, including researchers, practitioners, and policymakers. Method. We designed a survey to collect practical experiences, concerns, and expectations of practitioners who develop and implement security solutions in the context of MCSs. Conclusions and Future Works. Our findings highlight that the safe use of LLMs in MCS governance requires interdisciplinary collaboration. Researchers should focus on designing regulation-oriented models and focus on accountability; practitioners emphasize data protection and transparency, while policymakers must establish a unified AI framework with global benchmarks to ensure ethical and secure LLMs-based MCS governance.