Enterprise AI Deep Dive: Boosting Threat Intelligence with Custom LLMs

An expert analysis of the research paper "Evaluating the Usability of LLMs in Threat Intelligence Enrichment" by Sanchana Srikanth, Mohammad Hasanuzzaman, and Farah Tasnur Meem. We dissect the findings to reveal how enterprises can bypass the pitfalls of off-the-shelf models and achieve superior ROI with tailored AI solutions.

Executive Summary: From Academic Insights to Enterprise Action

The 2024 study by Srikanth et al. provides a critical usability evaluation of five prominent Large Language Models (LLMs)ChatGPT, Gemini, Cohere, Copilot, and Meta AIin the high-stakes domain of Cyber Threat Intelligence (CTI). The research meticulously exposes significant gaps in integration, output quality, user experience, and real-time performance that render generic LLMs unreliable for serious enterprise security operations. Key findings reveal that while these models show promise, they consistently fail in practical CTI enrichment tasks due to issues like an inability to process standard data formats (e.g., XML), a lack of direct integration with essential security tools like VirusTotal, and inconsistent, often incomplete, analytical outputs.

For enterprises, this research is a crucial warning: adopting generic LLMs for CTI without customization is not just inefficientit's a security risk. At OwnYourAI.com, we see these challenges as opportunities. The paper's findings validate our core philosophy: true enterprise value is unlocked through custom-built AI solutions. This analysis translates the study's academic benchmarks into a strategic enterprise playbook, demonstrating how tailored LLM implementations can solve these documented usability failures, streamline Security Operations Center (SOC) workflows, enhance threat detection accuracy, and deliver a quantifiable return on investment. We will explore how custom data pipelines, fine-tuned models, and purpose-built user interfaces are essential for transforming LLMs from fascinating technologies into mission-critical security assets.

The Core Usability Crisis: Why Generic LLMs Falter in Enterprise CTI

The research paper identifies five critical areas where general-purpose LLMs fail to meet the demands of professional threat intelligence. Below, we break down these findings, analyze their enterprise impact, and outline how OwnYourAI's custom solutions directly address each challenge.

Data-Driven Insights: Visualizing LLM Performance Gaps

The study's quantitative results paint a clear picture of the performance disparity between models. We have recreated their key findings in interactive charts below, adding our enterprise-level analysis to highlight the business implications of these metrics.

Strategic Roadmap: A Phased Approach to Enterprise-Grade CTI with LLMs

Moving from a generic LLM to a powerful, custom-built CTI asset requires a strategic approach. Based on the paper's design guidelines, here is OwnYourAI's 5-phase roadmap for successful implementation.

Calculate Your ROI: The Business Case for Custom CTI Solutions

Generic LLMs may seem cost-effective, but their hidden costs in lost productivity, inefficiency, and increased risk are substantial. Use our interactive calculator to estimate the potential annual savings and ROI your organization could achieve by implementing a custom AI solution that solves the usability problems identified in the research.

Test Your Knowledge: Are You Ready for Enterprise AI in CTI?

Based on the findings of Srikanth et al., see how well you understand the current landscape of LLM usability in cybersecurity. This short quiz will test your key takeaways from the analysis.