arXiv cs.AI· Pellegrino Casoria, Andrea Araldo, Rosario Patan\`e, Simon Pietro Romano·· 12 小时前AI 评分31
从炒作到 ROI：面向信息系统与安全运营的 LLM 采用经济模型
From Hype to ROI: An Economic Model for LLM Adoption in Information Systems and Security Operations
AI 导读
一篇 arXiv 论文提出通用的 LLM 采用经济模型，用蒙特卡洛模拟在悲观到乐观的 LLM 效率假设下给出 ROI 分布、盈利概率与 Value at Risk，并以安全运营中心（SOC）为首个实例。结果显示采用经济学取决于具体场景，自动化是可利用的杠杆但受准确率制约，错误成本与模型价格是次要因素；在所分析的模型与价格下多数配置不盈利，盈利仅限高准确率、低不确定性部署。
正文
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