GenAI in the Military: Trends and Opportunities

Lauri Vasankari; Aapo Koski

doi:10.31374/sjms.415

GenAI in the Military: Trends and Opportunities

Scandinavian Journal of Military Studies

Volume 8 (2025): Issue 1

By: Lauri Vasankari and Aapo Koski

Open Access

|Nov 2025

Abstract

Generative artificial intelligence offers the potential for significant, far-reaching applications within the military domain. This article surveys current trends and explores emerging opportunities for GenAI in military operations, focusing on its integration into strategic planning, decision-making, and operational efficiency. Key areas of application include information extraction, decision support, mission simulations and information warfare. This paper further examines critical challenges such as ethical considerations, bias mitigation, hallucination management, and secure deployment within classified environments. By addressing these challenges and leveraging the opportunities presented by GenAI, military organizations can enhance their capabilities, maintain strategic advantages, and ensure preparedness for future conflicts. The paper concludes with recommendations for research, development, and collaboration across NATO and allied forces to fully harness the power of GenAI in defense and security.

References

Ali, M., Fromm, M., Thellmann, K., Ebert, J., Weber, A. A., Rutmann, R., Jain, C., Lübbering, M., Steinigen, D., Leveling, J., Klug, K., Schulze Buschhoff, J., Jurkschat, L., Abdelwahab, H., Stein, B. J., Sylla, K.-H., Denisov, P., Brandizzi, N., Saleem, Q., Bhowmick, A., Helmer, L., John, C., Ortiz Suarez, P., Ostendorff, M., Jude, A., Manjunath, L., Weinbach, S., Penke, C., Filatov, O., Asaadi, S., Barth, F., Sifa, R., Küch, F., Herten, A., Jäkel, R., Rehm, G., Kesselheim, S., Köhler, J., & Flores-Herr, N. (2024). Teuken-7B-Base & Teuken-7B-Instruct: towards European LLMs. arXiv. 10.3233/FAIA251328
Open DOI Search in Google Scholar Back to article
Andreoni, M., Lunardi, W. T., Lawton, G., & Thakkar, S. (2024). Enhancing autonomous system security and resilience with Generative AI: A comprehensive survey. IEEE Access, 12, 109470–109493. 10.1109/ACCESS.2024.3439363
Open DOI Search in Google Scholar Back to article
Ateia, S., & Kruschwitz, U. (2024). Can open-source LLMs compete with commercial models? Exploring the few-shot performance of current GPT models in biomedical tasks. arXiv. https://arxiv.org/abs/2407.13511
Search in Google Scholar Back to article
Ba, L. J., & Caruana, R. (2014). Do deep nets really need to be deep? arXiv. https://arxiv.org/abs/1312.6184
Search in Google Scholar Back to article
Balis, C., & O’Neill, P. (2022). Trust in AI: Rethinking future command (Occasional paper). Royal United Services Institute for Defence and Security Studies.
Search in Google Scholar Back to article
Barzyk, C., Hickson, J., Ochoa, J., Talley, J., Willeke, M., Coffey, S., Pavlik, J., & Bastian, N. D. (2024). A generative artificial intelligence methodology for automated zero-shot data tagging to support tactical zero trust architecture implementation. Proceedings of the Annual General Donald R. Keith Memorial Conference. 10.37266/ISER.2025v12i2.pp83-88
Open DOI Search in Google Scholar Back to article
Beauchamp-Mustafaga, N., Green, K., Marcellino, W., Lilly, S., & Smith, J. (2024). Dr. Li Bicheng, or how China learned to stop worrying and love social media manipulation (Technical report). RAND Corporation.
Search in Google Scholar Back to article
Behrouz, A., Zhong, P., & Mirrokni, V. (2024). Titans: Learning to memorize at test time. arXiv. https://arxiv.org/abs/2501.00663
Search in Google Scholar Back to article
Bishop, C. M., & Bishop, H. (2023). Deep learning: foundations and concepts. Springer Nature. 10.1007/978-3-031-45468-4
Open DOI Search in Google Scholar Back to article
Black, J., Eken, M., Parakilas, J., Dee, S., Ellis, C., Suman-Chauhan, K., Bain, R., Fine, H., Aquilino, M. C., Lebret, M., & Palicka, O. (2024). Strategic competition in the age of AI: Emerging risks and opportunities from military use of artificial intelligence. RAND.
Search in Google Scholar Back to article
Blanchard, A., & Bruun, L. (2024). Bias in military artificial intelligence. SIPRI Publications. 10.55163/CJFT9557
Open DOI Search in Google Scholar Back to article
Bode, I., & Bhila, I. (2024, September 3). The problem of algorithmic bias in AI-based military decision support systems. Humanitarian Law and Policy.
Search in Google Scholar Back to article
Boone, T., Fahimnia, B., Ganeshan, R., Herold, D. M., & Sanders, N. R. (2025). Generative AI: Opportunities, challenges, and research directions for supply chain resilience. Transportation Research Part E: Logistics and Transportation Review, 199, 104135. 10.1016/j.tre.2025.104135
Open DOI Search in Google Scholar Back to article
Brearcliffe, D. K. (2023). Computational military science: Complexity, broken symmetry, and artificial intelligence [Unpublished doctoral dissertation]. George Mason University.
Search in Google Scholar Back to article
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Girish, S., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D., Wu, J., Winter, C., … Amodei, D. (2020). Language models are few-shot learners. In H. Larochelle, M. Ranzato, R. Hadsell, M. Balcan, & H. Lin (Eds.), Advances in Neural Information Processing Systems (Vol. 33, pp. 1877–1901). Curran Associates, Inc. https://proceedings.neurips.cc/paperfiles/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf
Search in Google Scholar Back to article
Caballero, W. N., & Jenkins, P. R. (2025). On large language models in national security applications. Stat, 14(2), e70057. 10.1002/sta4.70057
Open DOI Search in Google Scholar Back to article
Cardillo, A. (2025). Best 39 large language models (LLMs) in 2025. Exploding Topics. https://explodingtopics.com/blog/list-of-llms
Search in Google Scholar Back to article
Chuang, H. M., & Cheng, D. W. (2022). Conversational AI over military scenarios using intent detection and response generation. Applied Sciences, 12(5), 2494. 10.3390/app12052494
Open DOI Search in Google Scholar Back to article
Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) (pp. 4171–4186). Association for Computational Linguistics.
Search in Google Scholar Back to article
Farabet, C., & Warkentin, W. (2025, May 12). Introducing Gemma 3: The most capable model you can run on a single GPU or TPU. Google. https://blog.google/technology/developers/gemma-3/
Search in Google Scholar Back to article
Feffer, M., Sinha, A., Deng, W. H., Lipton, Z. C., & Heidari, H. (2024). Red-teaming for generative AI: Silver bullet or security theater? In Proceedings of the 2024 AAAI/ACM Conference on AI, Ethics, and Society (Vol. 7, pp. 421–437). ACM. 10.1609/aies.v7i1.31647
Open DOI Search in Google Scholar Back to article
Garcia, D. (2024). Algorithms and decision-making in military artificial intelligence. Global Society, 38(1), 24–33. 10.1080/13600826.2023.2273484
Open DOI Search in Google Scholar Back to article
Geist, E., Frank, A. B., & Menthe, L. (2024). Limits of artificial intelligence for warfighters (Technical report). RAND Corporation.
Search in Google Scholar Back to article
Goecks, V. G., & Waytowich, N. (2024). COA-GPT: Generative pre-trained transformers for accelerated course of action development in military operations. In 2024 International Conference on Military Communication and Information Systems (ICMCIS) (pp. 1–10). IEEE. 10.1109/ICMCIS61231.2024.10540749
Open DOI Search in Google Scholar Back to article
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative adversarial nets. In Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence, & K. Q. Weinberger (Eds.), Advances in Neural Information Processing Systems (Vol. 27, pp. 2672–2680). Curran Associates, Inc.
Search in Google Scholar Back to article
Grand-Clément, S. (2023). Artificial intelligence beyond weapons: Application and impact of AI in the military domain. UNIDIR.
Search in Google Scholar Back to article
Guo, D., Yang, D., Zhang, H., Song, J., Zhang, R., Xu, R., Zhu, Q., Ma, S., Wang, P., Bi, X., Zhang, X., Yu, X., Wu, Y., Wu, Z. F., Gou, Z., Shao, Z., Li, Z., Gao, Z., Liu, A., Xue, B., Wang, B., Wu, B., Feng, B., Lu, C., Zhao, C., Deng, C., Zhang, C., Ruan, C., Dai, D., Chen, D., Ji, D., Li, E., Lin, F., Dai, F., Luo, F., Hao, G., Chen, G., Li, G., Zhang, H., Bao, H., Xu, H., Wang, H., Ding, H., Xin, H., Gao, H., Qu, H., Li, H., Guo, J., Li, J., Wang, J., Chen, J., Yuan, J., Qiu, J., Li, J., Cai, J. L., Ni, J., Liang, J., Chen, J., Dong, K., Hu, K., Gao, K., Guan, K., Huang, K., Yu, K., Wang, L., Zhang, L., Zhao, L., Wang, L., Zhang, L., Xu, L., Xia, L., Zhang, M., Zhang, M., Tang, M., Li, M., Wang, M., Li, M., Tian, N., Huang, P., Zhang, P., Wang, Q., Chen, Q., Du, Q., Ge, R., Zhang, R., Pan, R., Wang, R., Chen, R. J., Jin, R. L., Chen, R., Lu, S., Zhou, S., Chen, S., Ye, S., Wang, S., Yu, S., Zhou, S., Pan, S., Li, S. S., Zhou, S., Wu, S., Ye, S., Yun, T., Pei, T., Sun, T., Wang, T., Zeng, W., Zhao, W., Liu, W., Liang, W., Gao, W., Yu, W., Zhang, W., Xiao, W. L., An, W., Liu, X., Wang, X., Chen, X., Nie, X., Cheng, X., Liu, X., Xie, X., Liu, X., Yang, X., Li, X., Su, X., Lin, X., Li, X. Q., Jin, X., Shen, X., Chen, X., Sun, X., Wang, X., Song, X., Zhou, X., Wang, X., Shan, X., Li, Y. K., Wang, Y. Q., Wei, Y. X., Zhang, Y., Xu, Y., Li, Y., Zhao, Y., Sun, Y., Wang, Y., Yu, Y., Zhang, Y., Shi, Y., Xiong, Y., He, Y., Piao, Y., Wang, Y., Tan, Y., Ma, Y., Liu, Y., Guo, Y., Ou, Y., Wang, Y., Gong, Y., Zou, Y., He, Y., Xiong, Y., Luo, Y., You, Y., Liu, Y., Zhou, Y., Zhu, Y. X., Xu, Y., Huang, Y., Li, Y., Zheng, Y., Zhu, Y., Ma, Y., Tang, Y., Zha, Y., Yan, Y., Ren, Z. Z., Ren, Z., Sha, Z., Fu, Z., Xu, Z., Xie, Z., Zhang, Z., Hao, Z., Ma, Z., Yan, Z., Wu, Z., Gu, Z., Zhu, Z., Liu, Z., Li, Z., Xie, Z., Song, Z., Pan, Z., Huang, Z., Xu, Z., Zhang, Z., & Zhang, Z. (2025). DeepSeek-R1: Incentivizing reasoning capability in LLMs via reinforcement learning. arXiv. 10.1038/s41586-025-09422-z
Open DOI Search in Google Scholar Back to article
Gupta, M., & Agrawal, P. (2021). Compression of deep learning models for text: A survey. arXiv. https://arxiv.org/abs/2008.05221
Search in Google Scholar Back to article
Hinton, P. (2023). Generative AI and wargaming: What is it good for? The RUSI Journal, 168(7), 34–41. 10.1080/03071847.2023.2282863
Open DOI Search in Google Scholar Back to article
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. 10.1162/neco.1997.9.8.1735
Open DOI Search in Google Scholar Back to article
Hua, W., Fan, L., Li, L., Mei, K., Ji, J., Ge, Y., Hemphill, L., & Zhang, Y. (2024). War and peace (WarAgent): Large language model-based multi-agent simulation of world wars. arXiv. https://arxiv.org/abs/2311.17227
Search in Google Scholar Back to article
Huang, Z., Zhang, X., Tang, Z., Xu, F., Datcu, M., & Han, J. (2025). Generative artificial intelligence meets synthetic aperture radar: A survey. IEEE Geoscience and Remote Sensing Magazine. 10.1109/MGRS.2024.3483459
Open DOI Search in Google Scholar Back to article
Iacob, A., Sani, L., Marino, B., Aleksandrov, P., Shen, W. F., & Lane, N. D. (2024). Worldwide federated training of language models. Proceedings of the FL@FM-NeurIPS.
Search in Google Scholar Back to article
Ji, S., Tan, Y., Saravirta, T., Yang, Z., Vasankari, L., Pan, S., Guodong, L., & Walid, A. (2024). Emerging trends in federated learning: From model fusion to federated X learning. International Journal of Machine Learning and Cybernetics, 15, 3769–3790. 10.1007/s13042-024-02119-1
Open DOI Search in Google Scholar Back to article
Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., Ishii, E., Bang, Y. J., Madotto, A., & Fung, P. (2023). Survey of hallucination in natural language generation. ACM Computing Surveys, 55(12), 1–38. 10.1145/3571730
Open DOI Search in Google Scholar Back to article
Kelly, P., & Smith, H. (2024). How to think about integrating generative AI in professional military education. Military Review.
Search in Google Scholar Back to article
Kosmyna, N., Hauptmann, E., Yuan, Y. T., Situ, J., Liao, X. H., Beresnitzky, A. V., Braunstein, I., & Maes, P. (2025). Your brain on ChatGPT: Accumulation of cognitive debt when using an AI assistant for essay writing task. arXiv. https://arxiv.org/abs/2506.08872
Search in Google Scholar Back to article
Lee, C. E., Baek, J., Son, J., & Ha, Y. G. (2023). Deep AI military staff: Cooperative battlefield situation awareness for commander’s decision making. The Journal of Supercomputing, 79(6), 6040–6069. 10.1007/s11227-022-04882-w
Open DOI Search in Google Scholar Back to article
Lee, Y., Park, T., Lee, Y., Gong, J., & Kang, J. (2025). Exploring potential prompt injection attacks in federated military LLMs and their mitigation. arXiv. https://arxiv.org/abs/2501.18416
Search in Google Scholar Back to article
Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M. N., Yih, W., Rocktäschel, T., Riedel, S., & Kiela, D. (2020). Retrieval-augmented generation for knowledge-intensive NLP tasks. In H. Larochelle, M. Ranzato, R. Hadsell, M. Balcan, & H. Lin (Eds.), Advances in Neural Information Processing Systems (Vol. 33, pp. 9459–9474). Curran Associates, Inc. https://proceedings.neurips.cc/paperfiles/paper/2020/file/6b493230205f780e1bc26945df7481e5-Paper.pdf
Search in Google Scholar Back to article
Li, X., Feng, J., Meng, Y., Han, Q., Wu, F., & Li, J. (2019). A unified MRC framework for named entity recognition. arXiv. 10.18653/v1/2020.acl-main.519
Open DOI Search in Google Scholar Back to article
Lin, S., Hua, W., Li, L., Chang, C.-J., Fan, L., Ji, J., Hua, H., Jin, M., Luo, J., & Zhang, Y. (2024). BattleAgent: Multi-modal dynamic emulation on historical battles to complement historical analysis. arXiv. 10.18653/v1/2024.emnlp-demo.18
Open DOI Search in Google Scholar Back to article
Liu, G., Van Huynh, N., Du, H., Hoang, D. T., Niyato, D., Zhu, K., Kang, J., Xiong, Z., Jamalipour, A., & Kim, D. I. (2 L. (2024). Generative AI for unmanned vehicle swarms: Challenges, applications and opportunities. arXiv. https://arxiv.org/abs/2402.18062
Search in Google Scholar Back to article
Liu, T., Liu, J., Zhang, T., Wang, J., Wang, J., Kang, J., Niyato, D., & Mao, S. (2025). Generative AI-driven cross-layer covert communication: Fundamentals, framework and case study. arXiv. 10.1109/MCOM.004.2500025
Open DOI Search in Google Scholar Back to article
Liu, X., Yu, Z., Liu, X., Miao, L., & Yang, T. (2024). Military equipment entity extraction based on large language model. Applied Sciences, 14(19), 9063. 10.3390/app14199063
Open DOI Search in Google Scholar Back to article
LLM Stats. (2025). LLM leaderboard 2025 – Verified AI rankings. Retrieved July 2, 2025, from https://llm-stats.com/
Search in Google Scholar Back to article
Luukkonen, R., Burdge, J., Zosa, E., Talman, A., Komulainen, V., Hatanpää, V., Sarlin, P., & Pyysalo, S. (2024). Poro 34b and the blessing of multilinguality. arXiv. https://arxiv.org/abs/2404.01856
Search in Google Scholar Back to article
Marcellino, W., Beauchamp-Mustafaga, N., Kerrigan, A., Chao, L. N., & Smith, J. (2023). The rise of generative AI and the coming era of social media manipulation 3.0: Next-generation Chinese astroturfing and coping with ubiquitous AI (Technical report). RAND Corporation.
Search in Google Scholar Back to article
Martins, P. H., Alves, J., Fernandes, P., Guerreiro, N. M., Rei, R., Farajian, A., Klimaszewski, M., Alves, D. M., Pombal, J., Boizard, N., Faysse, M., Colombo, P., Yvon, F., Haddow, B., de Souza, J. G. C., Birch, A., & Martins, A. F. T. (2025). EuroLLM-9B: Technical report. arXiv. https://arxiv.org/abs/2506.04079
Search in Google Scholar Back to article
Martins, P. H., Fernandes, P., Alves, J., Guerreiro, N. M., Rei, R., Alves, D. M., Pombal, J. P., Farajian, A., Faysse, M., Klimaszewski, M., Colombo, P., Haddow, B., de Souza, J. C., Birch, A., & Martins, A. (2024). EuroLLM: Multilingual language models for Europe. arXiv. 10.1016/j.procs.2025.02.260
Open DOI Search in Google Scholar Back to article
Maslej, N., et al. (2024). Chapter 4: Economy. In Artificial Intelligence Index Report 2024 (pp. 46–68). Stanford Institute for Human-Centered Artificial Intelligence.
Search in Google Scholar Back to article
Matarazzo, A., & Torlone, R. (2025). A survey on large language models with some insights on their capabilities and limitations. arXiv. https://arxiv.org/abs/2501.04040
Search in Google Scholar Back to article
McMahan, H. B., Moore, E., Ramage, D., Hampson, S., & y Arcas, B. A. (2017). Communication-efficient learning of deep networks from decentralized data. arXiv. https://arxiv.org/abs/1602.05629
Search in Google Scholar Back to article
Menthe, L., Zhang, L., Geist, E., Steier, J., Frank, A. B., van Hegewald, E., Briggs, G. J., Scholl, K., Ashpari, Y., & Jacques, A. (2024). Limits of artificial intelligence for warfighters (Technical report). RAND Corporation.
Search in Google Scholar Back to article
Mikhailov, D. (2023). Optimizing national security strategies through LLM-driven artificial intelligence integration. arXiv. 10.14293/PR2199.000136.v1
Open DOI Search in Google Scholar Back to article
Moy, W. R., & Gradon, K. T. (2023). Artificial intelligence in hybrid and information warfare. In M. Cristiano, et al. (Eds.), AI and international conflict in cyberspace (pp. 48–74). RAND Corporation. 10.4324/9781003284093-4
Open DOI Search in Google Scholar Back to article
Nadibaidze, A., Bode, I., & Zhang, Q. (2024). AI in military decision support systems: A review of developments and debates (Review). Center for War Studies, University of Southern Denmark.
Search in Google Scholar Back to article
Narajala, V. S., & Narayan, O. (2025). Securing Agentic AI: A comprehensive threat model and mitigation framework for Generative AI agents. arXiv. https://arxiv.org/abs/2504.19956
Search in Google Scholar Back to article
Narayanan, P., Vindiola, M., Park, S., Logie, A., Waytowich, N., Mittrick, M., Richardson, J., Asher, D., & Kott, A. (2021). First-year report of ARL directors strategic initiative (fy20–23): Artificial intelligence (AI) for command and control (C2) of multi-domain operations (MDO) (ARL-TR-9192). DEVCOM Army Research Laboratory.
Search in Google Scholar Back to article
Oniani, D., Hilsman, J., Peng, Y., Poropatich, R. K., Pamplin, J. C., Legault, G. L., & Wang, Y. (2023). Adopting and expanding ethical principles for generative artificial intelligence from military to healthcare. NPJ Digital Medicine, 6(1), 225. 10.1038/s41746-023-00965-x
Open DOI Search in Google Scholar Back to article
Pearl, J. (1988). Probabilistic reasoning in intelligent systems: Networks of plausible inference. Morgan Kaufmann. 10.1016/C2009-0-27609-4
Open DOI Search in Google Scholar Back to article
Perez, F., & Ribeiro, I. (2022). Ignore previous prompt: Attack techniques for language models. arXiv. https://arxiv.org/abs/2211.09527
Search in Google Scholar Back to article
Radford, A., Metz, L., & Chintala, S. (2016). Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv. https://arxiv.org/abs/1511.06434
Search in Google Scholar Back to article
Rashid, A. B., Kausik, A. K., Al Hassan Sunny, A., & Bappy, M. H. (2023). Artificial intelligence in the military: An overview of the capabilities, applications, and challenges. International Journal of Intelligent Systems, 2023(1), 8676366. 10.1155/2023/8676366
Open DOI Search in Google Scholar Back to article
Rettore, P. H., Zißner, P., Alkhowaiter, M., Zou, C., & Sevenich, P. (2024). Military data space: Challenges, opportunities, and use cases. IEEE Communications Magazine (pp. 70–76). 10.1109/MCOM.001.2300396
Open DOI Search in Google Scholar Back to article
Rivera, J. P., Mukobi, G., Reuel, A., Lamparth, M., Smith, C., & Schneider, J. (2024). Escalation risks from language models in military and diplomatic decision-making. In The 2024 ACM Conference on Fairness, Accountability, and Transparency (pp. 836–898). ACM. 10.1145/3630106.3658942
Open DOI Search in Google Scholar Back to article
Ruiz, D. C., & Sell, J. (2024). Fine-tuning and evaluating open-source large language models for the Army domain. arXiv. 10.48550/arXiv.2410.20297
Open DOI Search in Google Scholar Back to article
Sani, L., Iacob, A., Cao, Z., Marino, B., Gao, Y., Paulik, T., Zhao, W., Shen, W. F., Aleksandrov, P., Qiu, X., & Lane, N. D. (2024). The future of large language model pre-training is federated. Proceedings of the FL@FM-NeurIPS.
Search in Google Scholar Back to article
Schneider, J. (2024). Explainable generative AI (GenXAI): A survey, conceptualization, and research agenda. Artificial Intelligence Review, 57(11), 289. 10.1007/s10462-024-10916-x
Open DOI Search in Google Scholar Back to article
Schneider, J. (2025). Mental model shifts in human-LLM interactions. Journal of Intelligent Information Systems. 10.1007/s10844-025-00960-6
Open DOI Search in Google Scholar Back to article
Shavit, Y., Agarwal, S., Brundage, M., Adler, S., O’Keefe, C., Campbell, R., Lee, T., Mishkin, P., Eloundou, T., Hickey, A., Slama, K., Ahmad, L., McMillan, P., Vallone, A., Passos, A., & Robinson, D. G. (2023). Practices for governing agentic AI systems. OpenAI. https://openai.com/index/practices-for-governing-agentic-ai-systems/
Search in Google Scholar Back to article
Shayea, G., Zabil, M., Habeeb, M., Khaleel, Y., & Albahri, A. (2025). Strategies for protection against adversarial attacks in AI models: An in-depth review. Journal of Intelligent Systems, 34(1), 20240277. 10.1515/jisys-2024-0277
Open DOI Search in Google Scholar Back to article
Shazeer, N., Mirhoseini, A., Maziarz, K., Davis, A., Le, Q., Hinton, G., & Dean, J. (2017). Outrageously large neural networks: The sparsely-gated mixture-of-experts layer. arXiv. https://arxiv.org/abs/1701.06538
Search in Google Scholar Back to article
Simon, H. A. (1977). The new science of management decision (Rev. ed.). Prentice Hall.
Search in Google Scholar Back to article
Sprague, R. H. (1980). A framework for the development of decision support systems. MIS Quarterly, 4(4), 1–26. 10.2307/248875
Open DOI Search in Google Scholar Back to article
Stebbins, D., Girven, R. S., Parker, T., Deen, T., De Bruhl, B., Ryseff, J., Paige, J. W., Kleiman, A. Y., Bhatt, S. D., Sousa, É. M., Kepe, M., & Fay, M. (2024). Exploring artificial intelligence use to mitigate potential human bias within US Army Intelligence Preparation of the Battlefield processes (Report No. RRA2763-1). RAND Corporation.
Search in Google Scholar Back to article
Tian, Y., Lin, F., Li, Y., Zhang, T., Zhang, Q., Fu, X., Huang, J., Dai, X., Wang, Y., Tian, C., Li, B., Lv, Y., Kovács, L., & Wang, F. Y. (2025). UAVs meet LLMs: Overviews and perspectives toward agentic low-altitude mobility. Information Fusion, 122, 103158. 10.1016/j.inffus.2025.103158
Open DOI Search in Google Scholar Back to article
Uddin, M., Irshad, M. S., Kandhro, I. A., Alanazi, F., Ahmed, F., Maaz, M., Hussain, S., & Ullah, S. S. (2025). Generative AI revolution in cybersecurity: A comprehensive review of threat intelligence and operations. Artificial Intelligence Review, 58(8), 1–39. 10.1007/s10462-025-11219-5
Open DOI Search in Google Scholar Back to article
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention is all you need. In I. Guyon, U. von Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, & R. Garnett (Eds.), Advances in Neural Information Processing Systems (Vol. 30). Curran Associates, Inc.
Search in Google Scholar Back to article
Vinyals, O., Ewalds, T., Bartunov, S., Georgiev, P., Vezhnevets, A. S., Yeo, M., Makhzani, A., Küttler, H., Agapiou, J., & Schrittwieser, J. (2017). StarCraft II: A new challenge for reinforcement learning. arXiv. https://arxiv.org/abs/1708.04782
Search in Google Scholar Back to article
Wang, S., Sun, X., Li, X., Ouyang, R., Wu, F., Zhang, T., Li, J., & Wang, G. (2023). GPT-NER: Named entity recognition via large language models. arXiv. https://arxiv.org/abs/2304.10428
Search in Google Scholar Back to article
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E. H., Le, Q. V., & Zhou, D. (2022). Chain-of-thought prompting elicits reasoning in large language models. In Proceedings of the 36th International Conference on Neural Information Processing Systems (NIPS ’22). Curran Associates Inc.
Search in Google Scholar Back to article
Weizenbaum, J. (1966). ELIZA—A computer program for the study of natural language communication between man and machine. Communications of the ACM, 9(1), 36–45. 10.1145/365153.365168
Open DOI Search in Google Scholar Back to article
Xu, X., Li, M., Tao, C., Shen, T., Cheng, R., Li, J., Xu, C., Tao, D., & Zhou, T. (2024). A survey on knowledge distillation of large language models. arXiv. https://arxiv.org/abs/2402.13116
Search in Google Scholar Back to article
Yigit, Y., Buchanan, W. J., Tehrani, M. G., & Maglaras, L. (2024). Review of Generative AI methods in cybersecurity. arXiv. https://arxiv.org/abs/2403.08701
Search in Google Scholar Back to article
Zarrar, H., & Kakar, S. A. (2024). Generative artificial intelligence & its military applications by the US and China – lessons for south Asia. Journal of Computing & Biomedical Informatics.
Search in Google Scholar Back to article
Zhang, P., Zeng, G., Wang, T., & Lu, W. (2024). TinyLlama: An open-source small language model. arXiv. https://arxiv.org/abs/2401.02385
Search in Google Scholar Back to article

Articles in this issue

DOI: https://doi.org/10.31374/sjms.415 | Journal eISSN: 2596-3856

Journal RSS Feed

Language: English

Page range: 416 - 434

Submitted on: Apr 4, 2025

Accepted on: Oct 30, 2025

Published on: Nov 24, 2025

Published by: Scandinavian Military Studies

In partnership with: Paradigm Publishing Services

Publication frequency: 1 issue per year

Keywords:

generative AI,

military,

artificial intelligence,

defense technology

© 2025 Lauri Vasankari, Aapo Koski, published by Scandinavian Military Studies
This work is licensed under the Creative Commons Attribution 4.0 License.

Volume 8 (2025): Issue 1