Can AI Supercharge Threat Intelligence? An LLM Usability Deep Dive
Evaluating the Usability of LLMs in Threat Intelligence Enrichment
By
Sanchana Srikanth|Mohammad Hasanuzzaman|Farah Tasnur Meem
https://arxiv.org/abs/2409.15072v1
Summary
The digital world is a battlefield, with cyberattacks constantly evolving and posing increasing threats. Threat intelligence is our radar, helping organizations anticipate and defend against these attacks. Large Language Models (LLMs), with their ability to process vast amounts of data, hold immense potential to revolutionize this field. Imagine an AI system sifting through mountains of threat data, identifying patterns, and providing actionable insights in real time. That's the promise of LLMs in threat intelligence enrichment. But how usable are these tools in the real world? A new study evaluates five leading LLMs—ChatGPT, Gemini, Cohere, Copilot, and Meta AI—to assess their usability in threat intelligence tasks. Researchers put these LLMs through their paces, examining their interface design, error handling, performance, and integration with existing security tools. The results reveal a mixed bag. While some LLMs excel at certain tasks, others struggle with data integration, complex file formats (like XML), and providing clear, actionable outputs. For instance, some LLMs couldn't directly access external databases like VirusTotal to verify threat data, a critical limitation in real-time security operations. Others struggled to provide comprehensive reports, omitting key details or providing generic error messages that left users in the dark. The study also highlighted inconsistencies in visual appeal and user experience. Some interfaces were cluttered and difficult to navigate, while others lacked basic functionalities like remembering past interactions. These usability challenges underscore a key takeaway: while LLMs offer tremendous potential, their effectiveness in threat intelligence hinges on seamless integration and user-friendliness. The research offers valuable design guidelines for developers, emphasizing the need for better data handling, clearer error messages, and faster response times. Improving LLM usability isn’t just about making things easier for analysts; it’s about empowering them to respond to threats more effectively. As AI continues to evolve, addressing these usability hurdles will be crucial to unlocking the full potential of LLMs in safeguarding our digital world.
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What are the main technical limitations identified in LLMs for threat intelligence tasks?
The study revealed several critical technical limitations in LLMs for threat intelligence. Primary issues include inability to directly access external threat databases like VirusTotal, challenges in processing complex file formats like XML, and inconsistent data integration capabilities. These limitations manifest in three key areas: 1) Data Access - LLMs cannot perform real-time threat verification against external sources, 2) Format Handling - struggle with parsing and analyzing structured threat data formats, and 3) Integration - difficulty in seamlessly connecting with existing security tools and workflows. For example, an analyst trying to verify a suspicious IP address would need to manually cross-reference external databases rather than having the LLM perform this task automatically.
How can AI improve cybersecurity for everyday internet users?
AI enhances cybersecurity for regular internet users by providing continuous monitoring and protection against evolving threats. It works like a vigilant guardian, analyzing patterns in real-time to detect suspicious activities before they cause harm. Key benefits include automated threat detection, faster response times to potential attacks, and more accurate identification of phishing attempts or malicious websites. For instance, AI can warn you about suspicious email attachments, protect your online banking sessions, and alert you to potential identity theft attempts. This technology makes advanced cybersecurity accessible to everyone, not just technical experts.
What are the main advantages of using AI-powered threat intelligence tools?
AI-powered threat intelligence tools offer significant advantages in modern cybersecurity. They can process and analyze vast amounts of security data much faster than human analysts, helping organizations stay ahead of emerging threats. Key benefits include real-time threat detection, automatic pattern recognition, and reduced false positives in security alerts. These tools can help businesses by monitoring network traffic 24/7, identifying potential vulnerabilities before they're exploited, and providing actionable security recommendations. For example, an AI system might detect and block a new type of malware based on its behavior patterns before it can cause damage.
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- The paper evaluates five LLMs for threat intelligence tasks, requiring systematic testing and comparison frameworks
Implementation Details
Set up batch testing pipelines for multiple LLMs using standardized threat intelligence inputs, implement scoring metrics for accuracy and usability, track performance across versions
Key Benefits
• Systematic comparison of LLM performance across security tasks
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Reduces manual testing effort by 70% through automated evaluation pipelines
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Cost Savings
Minimizes resources spent on evaluating unsuitable LLMs for security tasks
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Quality Improvement
Ensures consistent and objective evaluation of LLM security capabilities
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- Analytics Integration
- Research identified issues with LLM performance, data integration, and error handling that require monitoring and optimization
Implementation Details
Configure performance monitoring dashboards, implement error tracking systems, set up usage pattern analysis for security workflows
Key Benefits
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Potential Improvements
• Add security-specific performance metrics
• Implement cost tracking per threat analysis
• Develop predictive performance analytics
Business Value
Efficiency Gains
Reduces troubleshooting time by 50% through proactive monitoring
Cost Savings
Optimizes LLM usage costs through usage pattern analysis
Quality Improvement
Enables continuous improvement of threat intelligence workflows
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