COGNITIVE SECURITY IN THE AI AGE: HR’S STRATEGIC ROLE IN BUILDING THE INTELLIGENT HUMAN FIREWALL FOR SUSTAINABLE DECISION-MAKING

Authors

  • Dr. (Col) Virendra Mishra (Retd) Professor of Practice at IPS Academy, Institute of Engineering & Science, Indore
  • Dr. (Col) Dinesh Kumar (Retd) Professor of Practice at IPS Academy, Institute of Engineering & Science, Indore

DOI:

https://doi.org/10.69980/y9vz8f07

Keywords:

Cognitive Security, Artificial Intelligence, Organizational Behaviour, Decision-Making, Sustainable Leadership, Human-in-Command, Algorithmic Bias, Cognitive firewall, OWNER

Abstract

The accelerated integration of Artificial Intelligence (AI) into organizational processes has fundamentally transformed how individuals think, teams interact, and decisions are made. While AI enhances efficiency, analytical depth, and innovation capability, it simultaneously reshapes cognitive processes, interpersonal communication, and collective judgment. Over-dependence on algorithmic recommendations, diminishing reflective thinking, AI-mediated communication, and diffusion of responsibility increasingly threaten decision quality, team bonding, ethical conduct, and long-term organizational sustainability. In this context, many organizational failures no longer originate from technological breakdowns but from compromised cognition, behavioural drift, and erosion of trust. This paper advances the concept of cognitive security, defined as the organizational capability to safeguard human judgment, ethical reasoning, communication integrity, and behavioural discipline in AI-enabled environments. Anchored in Organizational Behaviour, strategic Human Resource Management, and leadership practice, the paper positions HR as the strategic custodian of cognitive security. The paper proposes an Intelligent Human (Cognitive) Firewall, operationalized through the OWNER framework, to ensure that inputs from data, analytics, AI tools, and human stakeholders are filtered through human judgment, accountability, and command responsibility. Reinforced by BMW (Body–Mind–Words), Emotional Intelligence, and the military ethic of “walking the talk,” the framework aligns thought, communication, and action.  

References

1.Agudo, U., Liberal, K. G., Arrese, M., & Matute, H. (2024). The impact of AI errors in a human-in-the-loop process. Cognitive Research: Principles and Implications, 9(1), 1.

2.Bankins, S., Formosa, P., Griep, Y., & Richards, D. (2022). AI decision making with dignity? Contrasting workers’ justice perceptions of human and AI decision making in a human resource management context. Information Systems Frontiers, 24(3), 857-875.

3.Bankins, S., Ocampo, A. C., Marrone, M., Restubog, S. L. D., & Woo, S. E. (2024). A multilevel review of artificial intelligence in organizations: Implications for organizational behavior research and practice. Journal of organizational behavior, 45(2), 159-182.

4.Bashkirova, A., & Krpan, D. (2024). Confirmation bias in AI-assisted decision-making: AI triage recommendations congruent with expert judgments increase psychologist trust and recommendation acceptance. Computers in Human Behavior: Artificial Humans, 2(1), 100066.

5.Budhwar, P., Malik, A., De Silva, M. T., & Thevisuthan, P. (2022). Artificial intelligence–challenges and opportunities for international HRM: a review and research agenda. The InTernaTIonal Journal of human resource managemenT, 33(6), 1065-1097.

6.Charlwood, A., & Guenole, N. (2022). Can HR adapt to the paradoxes of artificial intelligence?. Human Resource Management Journal, 32(4), 729-742.

7.Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard business review, 96(1), 108-116.

8.Gong, Q., Fan, D., & Bartram, T. (2025). Algorithmic human resource management: toward a functional affordance perspective. Personnel Review, 54(5), 1150-1177.

9.Haque, A. B., Islam, A. N., & Mikalef, P. (2023). Explainable Artificial Intelligence (XAI) from a user perspective: A synthesis of prior literature and problematizing avenues for future research. Technological Forecasting and Social Change, 186, 122120.

10.Horowitz, M. C., & Kahn, L. (2024). Bending the automation bias curve: A study of human and AI-based decision making in national security contexts. International Studies Quarterly, 68(2), sqae020.

11.Kahneman, D. (2011). Thinking, fast and slow. macmillan.

12.Lee, M. K. (2018). Understanding perception of algorithmic decisions: Fairness, trust, and emotion in response to algorithmic management. Big data & society, 5(1), 2053951718756684.

13.Malin, C. D., Fleiß, J., Seeber, I., Kubicek, B., Kupfer, C., & Thalmann, S. (2024). The application of AI in digital HRM–an experiment on human decision-making in personnel selection. Business Process Management Journal, 30(8), 284-312.

14.Mikalef, P., Conboy, K., Lundström, J. E., & Popovič, A. (2022). Thinking responsibly about responsible AI and ‘the dark side’of AI. European Journal of Information Systems, 31(3), 257-268.

15.Mikalef, P., Islam, N., Parida, V., Singh, H., & Altwaijry, N. (2023). Artificial intelligence (AI) competencies for organizational performance: A B2B marketing capabilities perspective. Journal of Business Research, 164, 113998.

16.Papagiannidis, E., Mikalef, P., & Conboy, K. (2025). Responsible artificial intelligence governance: A review and research framework. The Journal of Strategic Information Systems, 34(2), 101885.

17.Prikshat, V., Malik, A., & Budhwar, P. (2023). AI-augmented HRM: Antecedents, assimilation and multilevel consequences. Human Resource Management Review, 33(1), 100860.

18.Przegalinska, A., Triantoro, T., Kovbasiuk, A., Ciechanowski, L., Freeman, R. B., & Sowa, K. (2025). Collaborative AI in the workplace: Enhancing organizational performance through resource-based and task-technology fit perspectives. International Journal of Information Management, 81, 102853.

19.Raisch, S., & Krakowski, S. (2021). Artificial intelligence and management: The automation–augmentation paradox. Academy of management review, 46(1), 192-210.

20.Rigotti, C., & Fosch-Villaronga, E. (2024). Fairness, AI & recruitment. Computer Law & Security Review, 53, 105966.

21.Rodgers, W., Murray, J. M., Stefanidis, A., Degbey, W. Y., & Tarba, S. Y. (2023). An artificial intelligence algorithmic approach to ethical decision-making in human resource management processes. Human resource management review, 33(1), 100925.

22.Rosenthal-von der Pütten, A. M., & Sach, A. (2024). Michael is better than Mehmet: exploring the perils of algorithmic biases and selective adherence to advice from automated decision support systems in hiring. Frontiers in Psychology, 15, 1416504.

23.Ruschemeier, H., & Hondrich, L. J. (2024). Automation bias in public administration–an interdisciplinary perspective from law and psychology. Government Information Quarterly, 41(3), 101953.

24.Shrestha, Y. R., Ben-Menahem, S. M., & Von Krogh, G. (2019). Organizational decision-making structures in the age of artificial intelligence. California management review, 61(4), 66-83.

25.Varma, A., Dawkins, C., & Chaudhuri, K. (2023). Artificial intelligence and people management: A critical assessment through the ethical lens. Human Resource Management Review, 33(1), 100923.

26.Vrontis, D., Christofi, M., Pereira, V., Tarba, S., Makrides, A., & Trichina, E. (2023). Artificial intelligence, robotics, advanced technologies and human resource management: a systematic review. Artificial intelligence and international HRM, 172-201.

Downloads

Published

2026-05-28