arXiv AI
From Hype to ROI: An Economic Model for LLM Adoption in Information Systems and Security Operations
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arXiv AI
Oct 15
From Chasing Ghosts to Missed Attacks: Perspectives and Perceptions of SOC Practitioners on LLM Integration, Risks, and Readiness
The paper investigates how Security Operations Center (SOC) analysts perceive the integration of Large Language Models (LLMs) into their workflows. Through 25 interviews and interactive scenarios, the authors identify 15 LLM use cases across six functional categories, noting that while LLMs are useful for automating repetitive tasks, they are not yet suitable for high‑impact incident analysis due to limitations in technical depth, context awareness, and organization‑specific knowledge. Despite these concerns, practitioners show a strong willingness to adopt LLMs, driven by competitive pressure and a desire for operational efficiency.
arXiv AI
Jun 17
Like a Hammer, It Can Build, It Can Break: Large Language Model Uses, Perceptions, and Adoption in Cybersecurity Operations on Reddit
arXiv:2604. 09998v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have recently emerged as promising tools for augmenting Security Operations Center (SOC) workflows, with vendors increasingly marketing autonomous AI solutions for SOCs.
arXiv AI
Aug 25
Agentic Security: A Systematization of Tools, Failure Modes, and Design Laws for LLM-Driven Penetration Testing
arXiv:2608.21423v1 Announce Type: cross Abstract: Agentic security uses large-language-model (LLM) agents to plan, dispatch, and interpret security tools. As these systems move from demonstrations to...
arXiv AI
1d ago
Create Your Own Work Stack: Deploying Self-hosted AI and Tooling in a Tech Startup
The paper reports on Grayhat Developers’ experience deploying a hybrid AI stack that combines local open‑source models with commercial agents. After finding local models too slow for complex coding tasks, the team kept commercial agents for those tasks while using local models for lower‑complexity work, documentation sync, and tool coordination, reducing commercial AI usage by 27–36%. The initial self‑hosted deployment suffered from UX friction, but a centrally configured deployment improved adoption.
arXiv AI
Sep 23
Compared to What? A Human-Anchored Security Benchmark for LLM-Generated Infrastructure-as-Code
arXiv:2608.28021v2 Announce Type: replace-cross Abstract: Large language models increasingly author Infrastructure-as-Code (IaC), where one insecure default is provisioned straight into production. P...
Hugging Face Trending Papers
Jul 8
Predicting LLM Safety Before Release by Simulating Deployment
Pre-deployment safety evaluations aim to inform the downstream risks of releasing a new AI model. Yet most evaluations provide limited evidence about how often undesired model behavior will occur in deployment: they generally have insufficient coverage, are unrepresentative, and are generally recognizable as tests.