Human-AI Collaborative Security Operations: Optimizing SOC Analyst Cognitive Load Through Augmented Intelligence Frameworks

References

[1] Bureau of Labor Statistics. (2023). Occupational Outlook Handbook: Information Security Analysts. U.S. Department of Labor. https://www.bls.gov/ooh/computer-and-information-technology/information-security-analysts.htm

[2] Brynjolfsson, E., & McAfee, A. (2017). The business of artificial intelligence. Harvard Business Review, 95(4), 3-11.

[3] Card, S. K., Mackinlay, J. D., & Shneiderman, B. (2018). Readings in information visualization: Using vision to think. Morgan Kaufmann.

[4] Chen, L., Rodriguez, M., & Patel, S. (2023). Cognitive overload in security operations centers: A quantitative analysis of analyst performance degradation. Journal of Cybersecurity Research, 45(3), 234-251.

[5] CISA. (2023). Annual Threat Assessment: Cybersecurity and Infrastructure Security Agency. Department of Homeland Security.

[6] D'Alessandro, R., & Park, J. H. (2023). Factors contributing to cognitive load in cybersecurity decision-making: A comprehensive framework. International Journal of Information Security, 22(4), 445-462.

[7] Garcia, A., & Anderson, K. (2023). Human-AI collaboration in security operations: Optimal automation levels for threat detection. ACM Transactions on Privacy and Security, 27(2), 1-28.

[8] Johnson, T., & Williams, S. (2022). Alert fatigue and analyst burnout in U.S. security operations centers: A longitudinal study. Cybersecurity Management Quarterly, 18(2), 89-107.

[9] Kumar, V., Thompson, D., & Lee, M. (2023). Adaptive interfaces for cognitive load management in security operations. IEEE Transactions on Human-Machine Systems, 53(3), 167-179.

[10] Martinez, C., Zhang, W., & O'Brien, P. (2023). Limitations of fully automated cybersecurity systems: A multi-site evaluation. Computers & Security, 118, 102-115.

[11] Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63(2), 81-97.

[12] Nguyen, H., Kim, S., & Jackson, R. (2023). Artificial intelligence applications in security operations centers: A systematic review. Journal of Information Security and Applications, 67, 103-118.

[13] Parasuraman, R., Sheridan, T. B., & Wickens, C. D. (2000). A model for types and levels of human interaction with automation. IEEE Transactions on Systems, Man, and Cybernetics, 30(3), 286-297.

[14] Russell, S. (2019). Human compatible: Artificial intelligence and the problem of control. Viking Press.

[15] Sans Institute. (2023). SOC Survey Report: Security Operations Center Analysis. SANS Institute Press.

[16] Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257-285.

[17] Thompson, R., & Lee, C. (2023). Machine learning approaches for false positive reduction in security alert systems. IEEE Security & Privacy, 21(4), 45-53.

[18] Tufte, E. R. (2001). The visual display of quantitative information (2nd ed.). Graphics Press.

[19] Wickens, C. D., & Holland, J. G. (2000). Engineering psychology and human performance (3rd ed.). Prentice Hall.

[20] Wilson, M., & Taylor, B. (2023). Automation levels in cybersecurity: A taxonomy for human-AI collaboration. ACM Computing Surveys, 56(2), 1-35.

[21] Zhao, X., & Kim, Y. (2023). Explainable AI in cybersecurity: Reducing cognitive burden through transparent decision support. AI & Society, 39(2), 423-441.

@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},
  }