Economic model finds most SOC LLM setups unprofitable
Original title: From Hype to ROI: An Economic Model for LLM Adoption in Information Systems and Security Operations
Monte Carlo results limit profitability to high-accuracy, low-uncertainty deployments across analyzed prices.
Researchers propose an economic model for LLM adoption in operational information systems, treating uncertain inputs as random variables around published benchmarks. A first case maps SOC workflows to standards and weighs alert volume, analyst effort, and model efficiency against inference, infrastructure, and error-remediation costs. Cloud API and self-hosted options are both covered. Across the analyzed models and prices, most configurations lose money; accuracy matters more than error cost or model price.
Unconfirmed: Profitability figures come from the authors' simulated assumptions, not measured SOC deployments.
- ROI outputs include profitability probability and Value at Risk.
- A sensitivity analysis identifies the main economic drivers.
- The model covers cloud API and self-hosted deployments.
- Automation value is described as gated by accuracy.
From the original article
1 min read at arxiv.orgLarge 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.
By Pellegrino Casoria · arxiv.org
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