CRApr 23

Can SOC Operators Explain their Decisions while Triaging Alarms? A Real-World Study

arXiv:2604.2200130.6h-index: 9
Predicted impact top 59% in CR · last 90 daysOriginality Incremental advance
AI Analysis

For SOC operators and security tool designers, this work reveals a critical need for decision-support systems that improve not only accuracy but also the ability to articulate reasoning.

This study investigates whether SOC analysts can explain their alarm triage decisions. In a field study with 12 analysts, correct decisions were made 83% of the time, but only 39% of explanations reflected the actual root cause, highlighting a gap between decision accuracy and explainability.

Security Operations Centers (SOCs) are pivotal in modern enterprises. Tasked to monitor complex network environments constantly under attack, SOCs can be active 24/7 and can include hundreds of operators supported by state-of-the-art technologies. Abundant research has studied the internal processes of SOCs, highlighting their pros and cons, as well as the challenges faced by SOC analysts -- such as dealing with the overwhelming number of false alarms triggered by automated security mechanisms. In this context, we wonder: given that "someone" must triage the alarms, and that such triaging must be grounded on established knowledge or evidence-based reasoning, can SOC employees justify why a certain decision was taken while triaging alarms? Answering such a research question (RQ) can better guide future efforts. We hence tackle this RQs. First, via a systematic literature review across 257 research documents, we provide evidence that such RQ received limited attention so far. Then, we partner-up with a real-world SOC and carry out a field study (n=12) with SOC employees. We show them real alarms raised in their SOC, and inquire whether such alarms are indicative of true security problems or not. Then, we ask to explain their decision. We found that while most analysts were able to separate "true from false" alarms (the decision was correct in 83% of the cases), a correct justification was hardly provided (only 39% of the provided explanations reflected the actual root cause). Ultimately, our results highlight the need for decision-support systems that help SOC analysts not only make the right call -- but also understand and articulate why it is right.

Foundations

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