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Human-AI Collaborative Security Operations: Optimizing SOC Analyst Cognitive Load Through Augmented Intelligence Frameworks
Subject area: Science,Engineering and Technology · Area of research: ICT
Abstract
The escalating complexity and volume of cybersecurity threats have overwhelmed traditional Security Operations Center (SOC) analyst capabilities, creating a critical need for innovative approaches to threat detection and response. This study examines the implementation of augmented intelligence frameworks in U.S.-based SOCs to optimize analyst cognitive load while maintaining operational effectiveness. Through a comprehensive analysis of 47 enterprise SOCs across the United States, we demonstrate that human-AI collaborative security operations can reduce analyst cognitive load by 43% while improving threat detection accuracy by 67%. Our proposed framework integrates machine learning algorithms with human expertise to create a symbiotic relationship that enhances both efficiency and effectiveness. The research reveals that strategic AI augmentation, rather than replacement, of human analysts leads to superior outcomes in threat hunting, incident response, and strategic security planning. Key findings indicate that organizations implementing our augmented intelligence framework experience a 52% reduction in false positive alerts, a 38% improvement in mean time to detection (MTTD), and a 41% decrease in analyst burnout rates. This study provides actionable insights for SOC managers, cybersecurity professionals, and organizational leaders seeking to optimize their security operations through human-AI collaboration.
Keywords
Security Operations Center, Augmented Intelligence, Cognitive Load, Human-AI Collaboration, Cybersecurity, Machine Learning
How to cite this paper
@article{1709110,
author = {Saheed Femi Osholake, Chinemelum Umealajekwu, Anthony Edohen, Abiola Olusola Majekodunmi, Uchenna Evans-Anoruo},
title = {Human-AI Collaborative Security Operations: Optimizing SOC Analyst Cognitive Load Through Augmented Intelligence Frameworks},
journal = {Iconic Research And Engineering Journals},
year = {2024},
volume = {8},
number = {6},
pages = {1102-1115},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1709110.pdf},
abstract = {The escalating complexity and volume of cybersecurity threats have overwhelmed traditional Security Operations Center (SOC) analyst capabilities, creating a critical need for innovative approaches to threat detection and response. This study examines the implementation of augmented intelligence frameworks in U.S.-based SOCs to optimize analyst cognitive load while maintaining operational effectiveness. Through a comprehensive analysis of 47 enterprise SOCs across the United States, we demonstrate that human-AI collaborative security operations can reduce analyst cognitive load by 43% while improving threat detection accuracy by 67%. Our proposed framework integrates machine learning algorithms with human expertise to create a symbiotic relationship that enhances both efficiency and effectiveness. The research reveals that strategic AI augmentation, rather than replacement, of human analysts leads to superior outcomes in threat hunting, incident response, and strategic security planning. Key findings indicate that organizations implementing our augmented intelligence framework experience a 52% reduction in false positive alerts, a 38% improvement in mean time to detection (MTTD), and a 41% decrease in analyst burnout rates. This study provides actionable insights for SOC managers, cybersecurity professionals, and organizational leaders seeking to optimize their security operations through human-AI collaboration.},
keywords = {Security Operations Center, Augmented Intelligence, Cognitive Load, Human-AI Collaboration, Cybersecurity, Machine Learning},
month = {December},
}