Simbian announced the "
AI SOC LLM Leaderboard" - the industry's most
comprehensive benchmark to measure LLM performance in Security Operations
Centers (SOCs). The new benchmark compares LLMs across a diverse range of
attacks and SOC tools in a realistic IT environment over all phases of alert
investigation, from alert ingestion to disposition and reporting. It includes a
public leaderboard to help professionals decide the best LLM for their SOC
needs.
"SOC analysts and vendors building tools for the SOC are
rapidly embracing LLMs to scale their operations, increase accuracy, and reduce
costs," said Ambuj Kumar,
Simbian CEO and Co-Founder. "Our industry-first benchmark enables SOC
teams and vendors to pick the best LLM for this purpose. This benchmark is made
possible by Simbian's AI SOC
Agent, a proven solution leading the industry in end-to-end alert
investigation leveraging LLMs."
Existing benchmarks compare LLMs over broad criteria such as
language understanding, math, and reasoning. Some benchmarks exist for broad
security tasks or very basic SOC tasks like alert summarization. But prior to
today's announcement, no benchmark existed to comprehensively measure LLMs on
the primary role of SOCs, which is to investigate alerts end-to-end. This task
involves diverse skills, including the ability to:
- Understand alerts from a broad range of detection
sources;
- Determine how to
investigate any given alert;
- Generate code to support that investigation;
- Understand data, extract evidence, and map it to attack
stages;
- Reason over evidence to arrive at a clear disposition
and severity;
- Produce
clear reports and response actions; and
- Customize investigations for each organization's context.
Simbian's AI
SOC LLM Leaderboard is the industry's first and only benchmark that
measures LLMs on autonomous end-to-end investigation of alerts, utilizing the
above skills. To make the benchmark applicable across a range of SOC
environments, it leverages 100 diverse full-kill chain scenarios that test all
layers of defense. It is also the industry's first benchmark to measure
investigation performance in a lab environment mimicking an enterprise, with
investigations autonomously retrieving data from live tools across the
environment.
This first LLM benchmark tested today's top-tier LLM models from Anthropic,
OpenAI, Google, and DeepSeek. All tested models were able to complete
over half (61%-67%) of the tasks involved in alert investigation, as long as
there was a solid framework to break down an investigation into clearly defined
tasks for LLMs. For this benchmark, that framework was provided by Simbian's AI SOC Agent.
The AI SOC LLM Leaderboard reveals that LLMs are more
capable than commonly believed for autonomous alert investigation. Marginal
difference was observed between standard LLMs and thinking LLMs for alert
investigation. The results showed that the best LLM for cybersecurity is a
generalist (like Sonnet 3.5) that knows how to code as well as how to perform
logical reasoning, rather than a specialist that excels at code (Sonnet 4.0) or
at logical reasoning (Opus 4). Finally, the benchmark highlighted that
specialization such as SOC-specific training or a mix of LLMs yields higher
performance than any single LLM.
Alert fatigue is common across SOCs and it is only getting
worse with AI-powered attacks, requiring SOC teams to scale their capacity
rapidly. AI offers a solution, and this benchmark guides the industry on the
best LLM for the SOC. Simbian will update the measurement results periodically. Follow the AI
SOC LLM Leaderboard page at https://simbian.ai/best-ai-for-cybersecurity.