Educating New Military Leaders to be Robust against Influence Operations: A Case Study


  • Knut Østby
  • Kirsi Helkala Norwegian Defence University College
  • Ole Joachim Aasen



Influence operations, Military Education, Leadership Education, Robustness, Resilience


Influence operations and cognitive warfare are part of the new complex threat picture that Norway and other nations face. In general, military education and leadership education have traditions in place to build robustness against war demands, but how to build robustness against influence operations is still almost non-existing. In this case study, we show how an educational module on influence operations was conducted at the Norwegian Defence University College’s Cyber Academy department and how this module contributed to strengthening robustness against cognitive warfare. The impact of the educational module was evaluated by a questionnaire and a short group interview, and the results are shown in this paper. The findings indicate a positive development in the cadets' own perceived robustness. In addition, we also discuss and suggest some personal and organizational factors that can strengthen military leaders' robustness against influence operations. The findings and the discussions can be used as inspiration when educational modules are designed both in military and civilian education.

Author Biographies

Knut Østby

Knut Østby is an employee at the Norwegian Defence University College, Norway. His main research interest is cyber power, ethical AI, and innovation in military forces.

Kirsi Helkala, Norwegian Defence University College

Dr Kirsi Helkala is a professor of cyber security at the Norwegian Defence University College, Norway. She received her PhD in information security from University of Oslo in 2010. Her main research interest is human factors in cyber security.

Ole Joachim Aasen

Ole Joachim Aasen has a master's degree in computer science, with a focus on machine learning. He has a background in the Norwegian Armed Forces, and an interest in researching the intersection between human and machine learning.