Burnout and Turnover Intentions Among SOC Analysts: A Longitudinal Study
APA Citation
Williams, K. et al. (2023). Burnout and Turnover Intentions Among SOC Analysts: A Longitudinal Study. *Human Factors*. https://doi.org/10.1177/00187208231198765
View original paper →What Did This Cybersecurity Research Find?
This cybersecurity workplace study tracked 312 SOC analysts across 18 months to measure burnout trajectories and their link to turnover. Cybersecurity operations staff working rotating shifts reported burnout rates 2.3 times higher than those on fixed schedules. Alert fatigue from false positives was the single strongest predictor of intent to leave.
Key Findings
- 1Rotating shift SOC analysts had 2.3x higher burnout scores than fixed-shift peers
- 2Alert fatigue from false positives was the top predictor of turnover intention
- 3Analysts with access to automation tools reported 27% lower emotional exhaustion
- 4Mean tenure before burnout-related departure was 2.1 years
- 5Organizations with structured career ladders retained SOC staff 40% longer
How Does This Apply to Cybersecurity Careers?
SOC analysts can advocate for schedule structures and alert tuning that reduce burnout. Managers hiring for SOC roles should understand what drives attrition in their teams.
Who Should Read This?
Frequently Asked Questions
What did this cybersecurity research find?
This cybersecurity workplace study tracked 312 SOC analysts across 18 months to measure burnout trajectories and their link to turnover. Cybersecurity operations staff working rotating shifts reported burnout rates 2.3 times higher than those on fixed schedules. Alert fatigue from false positives was the single strongest predictor of intent to leave.
How is this research relevant to cybersecurity careers?
SOC analysts can advocate for schedule structures and alert tuning that reduce burnout. Managers hiring for SOC roles should understand what drives attrition in their teams.
Where was this cybersecurity research published?
This study was published in Human Factors in 2023. The DOI is 10.1177/00187208231198765. Access the original paper through the publisher link above.
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