Enterprise AI Analysis
Leveraging LLMs for Non-Security Experts in Threat Hunting: Detecting Living off the Land Techniques
This analysis explores the potential of Large Language Models (LLMs) like ChatGPT and Gemini in proactive threat hunting, specifically focusing on Living off the Land (LotL) techniques. We evaluate their ability to generate effective security queries for non-security experts, highlighting current limitations and future opportunities for integration into cybersecurity workflows.
Executive Impact
LLMs present a dual challenge and opportunity for cybersecurity. While they can aid in threat hunting for resource-constrained organizations, their current limitations in generating accurate, reliable queries mean they are not standalone solutions. Strategic integration requires significant human oversight and expertise, yet the potential for augmenting IT teams remains.
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Avg. Undetected Threat Lifespan
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LLM-Generated Queries Evaluated
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LLM Query Success Rate for Splunk
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Understanding Large Language Models
LLMs, built on the Transformer architecture, excel at understanding context and generating human-like text by analyzing vast amounts of data. This allows them to perform tasks like text generation, translation, and summarization effectively. However, they face significant limitations, including producing factually incorrect information (hallucinations), inheriting biases from training data, and struggling with true reasoning or abstract thinking. These issues highlight why LLMs cannot yet be relied upon for critical tasks without expert oversight.
Navigating Modern Threat Landscapes
Threat hunting is a proactive security measure essential for detecting subtle signs of compromise that automated systems miss. It requires highly skilled professionals with deep understanding of system behavior and threat actor methodologies. Organizations, especially smaller ones, often lack the resources or expertise for dedicated threat hunting, making them vulnerable. The challenge is compounded by Living off the Land (LotL) techniques, where attackers misuse legitimate system tools, blending into normal network activity and evading traditional signature-based detection.
AI's Role in Modern Security Operations
While commercial AI-driven cybersecurity tools claim advanced threat detection using LLMs, they often come with high costs and require significant security maturity to utilize effectively. The current research indicates LLMs do not consistently produce accurate or reliable queries for detecting LotL techniques, particularly for non-security experts. This suggests that while LLMs hold promise as supportive resources, they are not yet suitable as standalone threat hunting tools and require substantial refinement and human intervention for practical use in cybersecurity workflows.
194 Days
Average lifespan of an undetected threat in a system, emphasizing the urgency of proactive defense.
Enterprise Process Flow: Threat Hunting Lifecycle
CREATE Hypotheses
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INVESTIGATE Via Tools & Techniques
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UNCOVER New Patterns & TTPs
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INFORM & ENRICH Analytics
| Feature | ChatGPT 3.5 | Google Gemini 1.0 |
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| Suitability for Non-Experts |
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Case Study: Query Generation for LotL Detection
The experiment evaluated 180 LLM-generated queries across Microsoft Defender, Splunk, and Elastic/Kibana for detecting ten Living off the Land (LotL) techniques. Contrary to expectations, LLMs largely failed to produce accurate and relevant results. Specifically, Splunk queries yielded no successful results, primarily due to mismatches in indexing structures and data environments. Microsoft Defender performed best, likely due to better alignment with LLM training data.
A critical finding was that even with increasingly prescriptive prompts, LLMs struggled, often producing syntax errors or targeting incorrect data sources. This highlights that LLMs are not yet capable of autonomously generating effective threat hunting queries, especially for non-security experts, underscoring the ongoing need for human expertise in understanding specific security tool environments and prompt engineering.
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