Artificial Intelligence Resilience Data Readiness and Strategic Innovation in Countering Emerging Multidomain Security Threats

Authors

  • Ferdinand Hasudungan Siagian Universitas Pertahanan Republik Indonesia
  • Nefra Firdaus Politeknik Angkatan Darat (POLTEKAD)
  • Sigit Purwanto Universitas Pertahanan Republik Indonesia
  • Bangun Pandapotan Hutajulu Universitas Pertahanan Republik Indonesia

DOI:

https://doi.org/10.55927/fjas.v5i9.117

Keywords:

Artificial Intelligence, Resilience, Data Readiness, Strategic Innovation, Multidomain Security

Abstract

The increasing complexity of multidomain security threats requires adaptive defense strategies through the utilization of artificial intelligence (AI), data readiness, and strategic innovation. This study aims to develop an AI-based defense strategy framework by analyzing the relationship between AI resilience, data readiness, and strategic innovation in addressing emerging security threats. The research employs a Design Science Research (DSR) approach supported by literature review and expert validation involving five experts in artificial intelligence, cybersecurity, and information technology. Data analysis was conducted qualitatively through thematic analysis and framework validation evaluation. The results produce a strategic framework that integrates AI resilience, data governance, and innovation to enhance responses toward multidomain security threats. This study contributes to the development of adaptive and sustainable digital defense strategies.

References

Alshaikh, M. (2021). Developing cybersecurity culture to influence employee behavior: A practice perspective. Computers & Security, 98, 102003. https://doi.org/10.1016/j.cose.2020.102003.

Braun, V., & Clarke, V. (2021). Thematic analysis: A practical guide. SAGE Publications. https://uk.sagepub.com/en-gb/eur/thematic-analysis/book248481.

Bubeck, S., Chandrasekaran, V., Eldan, R., Gehrmann, S., Horvitz, E., Kamar, E., Lee, P., Lee, Y. T., Li, Y., Lundberg, S., Nori, H., Palangi, H., Ribeiro, M. T., & Zhang, Y. (2023). Sparks of artificial general intelligence: Early experiments with GPT-4. Microsoft Research. https://arxiv.org/abs/2303.12712.

Creswell, J. W., & Creswell, J. D. (2023). Research design: Qualitative, quantitative, and mixed methods approaches (6th ed.). SAGE Publications. https://us.sagepub.com/en-us/nam/research-design/book255675.

Dremel, C., Wulf, J., Herterich, M. M., Waizmann, J. C., & Brenner, W. (2021). How AUDI AG established big data analytics in its digital transformation. MIS Quarterly Executive, 20(2), 81–100. https://aisel.aisnet.org/misqe/vol20/iss2/5.

Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., Albanna, H., Albashrawi, M. A., Al-Busaidi, A. S., Balakrishnan, J., Barlette, Y., Basu, S., Bose, I., Brooks, L., Buhalis, D., ... Wright, R. (2023). Opinion paper: “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642.

Enholm, I. M., Papagiannidis, E., Mikalef, P., & Krogstie, J. (2022). Artificial intelligence and business value: A literature review. Information Systems Frontiers, 24, 1709–1734. https://doi.org/10.1007/s10796-021-10186-w.

Ferrag, M. A., Maglaras, L., Janicke, H., Shu, L., & Choo, K. K. R. (2024). Artificial intelligence for cybersecurity: A comprehensive survey. IEEE Access, 12, 1–30. https://doi.org/10.1109/ACCESS.2024.

Goh, E., & Vinuesa, R. (2021). Regulating artificial intelligence: The role of governance, ethics, and risk management. AI and Ethics, 1, 1–12. https://doi.org/10.1007/s43681-021-00055-1.

Hiniduma, K., Perera, C., & Bandara, H. (2024). Data readiness assessment for trustworthy artificial intelligence systems: Challenges and future directions. IEEE Access, 12, 1–15. https://doi.org/10.1109/ACCESS.2024.

Hu, X., Chen, H., & Zhang, Y. (2021). Security and privacy challenges in artificial intelligence systems: A comprehensive review. IEEE Access, 9, 114290–114306. https://doi.org/10.1109/ACCESS.2021.3105000.

Huang, W., Zhang, Z., & Li, J. (2022). Artificial intelligence security: Threats, vulnerabilities, and defense mechanisms. Future Generation Computer Systems, 128, 1–15. https://doi.org/10.1016/j.future.2021.09.012.

Kitchenham, B., & Charters, S. (2020). Guidelines for performing systematic literature reviews in software engineering. Keele University and Durham University.

Mikalef, P., Conboy, K., & Krogstie, J. (2021). Artificial intelligence capability: Conceptualization, measurement calibration, and empirical investigation. Information Systems Frontiers, 23, 1–19. https://doi.org/10.1007/s10796-020-10088-9.

Mikalef, P., Islam, N., Parida, V., Singh, H., & Altwaijry, N. (2020). Artificial intelligence (AI) competence: Conceptualisation, measurement calibration, and empirical study on its impact on organizational creativity. Information & Management, 58(3), 103434. https://doi.org/10.1016/j.im.2020.103434.

Moser, A., & Korstjens, I. (2021). Series: Practical guidance to qualitative research. Part 3: Sampling, data collection and analysis. European Journal of General Practice, 27(1), 9–18. https://doi.org/10.1080/13814788.2021.1873651.

National Institute of Standards and Technology. (2023). Artificial Intelligence Risk Management Framework (AI RMF 1.0). U.S. Department of Commerce. https://doi.org/10.6028/NIST.AI.100-1.

Nguyen, T. T., Reddi, V. J., & others. (2022). Artificial intelligence security: Threats and countermeasures. IEEE Communications Surveys & Tutorials, 24(3), 1–35. https://doi.org/10.1109/COMST.2022.

Peffers, K., Tuunanen, T., Rothenberger, M. A., & Chatterjee, S. (2020). A design science research methodology for information systems research. Journal of Management Information Systems, 37(3), 799–823. https://doi.org/10.1080/07421222.2020.1790839.

Sarker, I. H. (2022). AI-based modeling: Techniques, applications and research issues towards automation, intelligent and smart systems. SN Computer Science, 3, 158. https://doi.org/10.1007/s42979-022-01043-x.

Tabassi, E. (2023). Artificial Intelligence Risk Management Framework (AI RMF 1.0). National Institute of Standards and Technology. https://doi.org/10.6028/NIST.AI.100-1.

Taddeo, M., McCutcheon, T., & Floridi, L. (2021). Trusting artificial intelligence in cybersecurity is a double-edged sword. Nature Machine Intelligence, 3, 705–706. https://doi.org/10.1038/s42256-021-00377-y.

Vinuesa, R., Azizpour, H., Leite, I., Balaam, M., Dignum, V., Domisch, S., Felländer, A., Langhans, S. D., Tegmark, M., & Fuso Nerini, F. (2020). The role of artificial intelligence in achieving the Sustainable Development Goals. Nature Communications, 11, 233. https://doi.org/10.1038/s41467-019-14108-y.

Wamba-Taguimdje, S. L., Fosso Wamba, S., Kala Kamdjoug, J. R., & Tchatchouang Wanko, C. E. (2020). Influence of artificial intelligence on firm performance: The business value of AI-based transformation projects. Business Process Management Journal, 26(7), 1893–1914. https://doi.org/10.1108/BPMJ-10-2019-0411.

Wieringa, R. J. (2020). Design science methodology for information systems and software engineering. Springer. https://doi.org/10.1007/978-3-030-46781-4.

Zhang, C., & Lu, Y. (2021). Study on artificial intelligence: The state of the art and future perspectives. Journal of Industrial Information Integration, 23, 100224. https://doi.org/10.1016/j.jii.2021.100224.

Published

2026-09-28