arXiv cs.AI· Pellegrino Casoria, Andrea Araldo, Rosario Patan\`e, Simon Pietro Romano·· 3 小时前AI 评分43
LLM 落地经济模型：从炒作到 ROI 的信息系统与安全运营采用分析
From Hype to ROI: An Economic Model for LLM Adoption in Information Systems and Security Operations
AI 导读
一篇 arXiv 论文提出通用经济模型，用 Monte Carlo 模拟评估 LLM 在信息系统和安全运营中心（SOC）采用中的 ROI 分布、Value at Risk 及敏感性分析。
正文 · 原文
Abstract:Large Language Models (LLMs) promise significant efficiency gains across information-intensive operations by automating repetitive language-based tasks. However, adoption decisions remain largely opportunistic, as organizations struggle to move from pilots to sustainable industrialization. Without systematic economic frameworks, practitioners cannot assess whether LLM investments create value in a given context.
This paper develops a general economic model to support informed LLM adoption decisions in operational information systems. Facing high uncertainty, we represent parameters as random variables centered on authoritative benchmarks, and evaluate the model by Monte Carlo simulation across pessimistic-to-optimistic assumptions on LLM efficiency. The ROI distributions show the probability of profitability and the Value at Risk for each configuration, together with a sensitivity analysis of its main drivers.
As a first instantiation, we apply the model to Security Operations Centers (SOCs), mapping workflows onto established standards to identify tasks suited to LLM augmentation. The model balances productivity gains, driven by alert volume, analyst effort, and LLM efficiency, against operational costs, including inference, infrastructure, and error remediation, covering both cloud API and self-hosted deployments.
Results show that adoption economics are context-dependent, and that automation is an exploitable lever, gated by accuracy, while the cost of errors and the model's price are secondary. Across the analyzed models and prices, most configurations are unprofitable, with profitability limited to high-accuracy, low-uncertainty deployments.
The model gives organizations a quantitative basis for evidence-based adoption decisions, by locating the boundary between profit and loss, and the loss risked beyond it.
| Subjects: | Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE) |
| Cite as: | arXiv:2610.33352 [cs.CR] |
| (or arXiv:2610.33352v1 [cs.CR] for this version) | |
| https://doi.org/10.48550/arXiv.2610.33352 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Pellegrino Casoria [view email]
[v1]
Fri, 23 Oct 2026 22:46:08 UTC (935 KB)
来源：arXiv cs.AI · arxiv.org