Citation
Dimitrov Petrov, Stoyanov Levski, Rakovski, 2026. "Sovereign Artificial Intelligence: Infrastructure, Governance, and National AI Ecosystems", International Journal of Review Computing and Information Technology (IJRCIT) 1(1): 1-17.
Abstract
This century is bastardized on AI, technology that could be the most disruptive in human history — changing how economies operate; how nations defend against internal and external threats while viewing opportunities to achieve a sustainable peace process; even the way public administration of governments prepare for empirical scientific research studying global competitive sustainability. AI systems – which need access to large-scale computing infrastructure, advanced semiconductor technologies, and vast datasets to run – are recognized as being strategic relative power over their sovereignty; therefore the nations of the world are awakening to endow Sovereign Artificial Intelligence or Sovereign AI. Sovereign AI — the ability for a nation to develop, deploy and regulate its own AI systems via domestic infrastructure, local data sources, human capacity, governance structure; all while retaining strategic independence in tech decisions and ownership over critical digital assetsAs the world became more integrated with foreign technology vendors and cloud platforms - so too did increased concerns around data privacy, cybersecurity threats, dependence on foreign individuals technologies - a weaker economy. This is why countries all over the globe are investing billions into sovereign AI abilities to create digital sovereignty, cater to domestic preferences or create future-proof technologies. These include data sovereignty, infrastructure sovereignty, governance sovereignty (Gajbhiye et al., 2023) and model sovereignty that together dictate which balanced Sovereign values can produce depreciated or low harmonised interpretations of the ways that a nation can develop AI according to its legalethical–cultural–economic priorities.It is a primer on what you need to have, i.e., the building blocks, infrastructure, governance frameworks and ecosystem components to support Sovereign AI systems. High-performance computing, sovereign cloud platform, a national data ecosystem, regulatory policy and promo talent coming together for AI independence success. It studies factors relating to the economy, security, and strategy for Sovereign AI implementation. It further analyses global trends and strategies adopted by the best performing nations to build a competitive AI ecosystem.
Keywords
Sovereign Artificial Intelligence
National AI Ecosystems
AI Governance
Digital Sovereignty
AI Infrastructure
Ethical Artificial Intelligence
Data Sovereignty
Public Sector AI
Strategic Technology Policy
References
- 1. Russell, S., & Norvig, P. (2021). Artificial Intelligence: A Modern Approach (4th ed.). Pearson.
- 2. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
- 3. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep Learning. Nature, 521(7553), 436–444.
- 4. Vaswani, A., Shazeer, N., Parmar, N., et al. (2017). Attention Is All You Need. Advances in Neural Information Processing Systems, 30.
- 5. Brown, T. B., Mann, B., Ryder, N., et al. (2020). Language Models are Few-Shot Learners. NeurIPS, 33, 1877–1901.
- 6. Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. NAACL-HLT, 4171–4186.
- 7. National Institute of Standards and Technology (NIST). (2023). AI Risk Management Framework (AI RMF 1.0).
- 8. UNESCO. (2021). Recommendation on the Ethics of Artificial Intelligence.
- 9. Organisation for Economic Co-operation and Development (OECD). (2024). OECD AI Policy Observatory Report.
- 10. World Economic Forum. (2024). Global Risks Report: Artificial Intelligence and Digital Sovereignty.
- 11. European Commission. (2024). Artificial Intelligence Act and Governance Framework.
- 12. United Nations. (2023). Governing AI for Humanity: Global Policy Perspectives.
- 13. Hennessy, J. L., & Patterson, D. A. (2019). Computer Architecture: A Quantitative Approach (6th ed.). Morgan Kaufmann.
- 14. Tanenbaum, A. S., & Bos, H. (2015). Modern Operating Systems (4th ed.). Pearson.
- 15. Kurose, J. F., & Ross, K. W. (2021). Computer Networking: A Top-Down Approach (8th ed.). Pearson.
- 16. Mell, P., & Grance, T. (2011). The NIST Definition of Cloud Computing. NIST Special Publication 800-145.
- 17. Armbrust, M., Fox, A., Griffith, R., et al. (2010). A View of Cloud Computing. Communications of the ACM, 53(4), 50–58.
- 18. Satyanarayanan, M. (2017). The Emergence of Edge Computing. Computer, 50(1), 30–39.
- 19. Cisco Systems. (2024). AI-Driven Networking and Infrastructure Automation.
- 20. NVIDIA Corporation. (2024). AI Infrastructure and Accelerated Computing Platforms.
- 21. Google Research. (2023). Tensor Processing Units and Scalable AI Systems.
- 22. Microsoft Research. (2024). Cloud AI Infrastructure and Responsible AI Governance.
- 23. IBM Research. (2023). Artificial Intelligence for Enterprise and National Infrastructure.
- 24. Intel Corporation. (2024). AI Accelerators and Edge Intelligence Technologies.
- 25. Linux Foundation. (2024). Open Source Technologies for AI Ecosystems.
- 26. International Telecommunication Union (ITU). (2023). AI Governance and Digital Transformation Frameworks.
- 27. World Bank. (2024). Digital Development and National AI Strategies.
- 28. McKinsey Global Institute. (2023). The Economic Potential of Generative AI.
- 29. Stanford University. (2024). AI Index Report 2024.
- 30. Harvard Kennedy School. (2023). Digital Sovereignty, National Security, and Artificial Intelligence Governance.