Editor’s note: This article is based on insights from a podcast series. The views expressed in the podcast reflect the speakers’ perspectives and do not necessarily represent those of this publication. Readers are encouraged to explore the full podcast for additional context.
In a special Women’s Day episode of the “Regulating AI” podcast, host Sanjay Puri sat down with cybersecurity expert and policy leader Hoda A. Alkhzaimi to explore the deeper forces shaping the future of artificial intelligence governance.
Dr. Hoda A. Alkhzaimi is Associate Vice Provost for Research Translation and Innovation at New York University Abu Dhabi and co-chair of the cybersecurity council at the World Economic Forum. In the conversation, she offered a rare perspective that blends mathematics, cybersecurity, economics, and geopolitics arguing that AI governance must look far beyond algorithms alone.
One of the most fascinating aspects of Dr. Alkhzaimi’s career journey is her transition from sovereign wealth fund management into cryptography research.
While the shift might appear dramatic, she explained that both fields share a common foundation: managing uncertainty. Sovereign wealth funds allocate capital based on probabilistic risk models and strategic foresight. Cryptanalysis, similarly, requires understanding mathematical complexity and identifying structural weaknesses in encryption systems.
For Alkhzaimi, both disciplines revolve around reducing uncertainty through rigorous analysis. This intellectual curiosity eventually led her to co-author independent research analyzing cryptographic systems designed by the National Security Agency.
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During the discussion, Alkhzaimi emphasized that independent verification is essential for building trust in any technological system, “we have to have these kinds of adversarial stress testing approaches that are independent in order to verify the level of governance and trust that we have within the system and to avoid any fragility, any kind of vulnerabilities that might exist.”
Whether the system is cryptographic infrastructure or an AI model, transparency and auditability are critical. Technologies designed by governments or corporations must withstand rigorous external testing. Without independent scrutiny, claims of security or reliability cannot truly be trusted.
This principle, she noted, directly applies to the governance of artificial intelligence.
A major theme during the conversation was what Dr. Alkhzaimi calls “AI myopia.” Too often, policymakers regulate AI in isolation without considering the broader technological ecosystem.
In reality, AI is deeply interconnected with many other domains from semiconductors and supply chains to energy systems and biotechnology. Alkhzaimi advised, “understanding convergence, convergence of different technology and how these convergences of the over 200 emerging fields of industries and technologies out there impacting the AI development infrastructure” would help to regulate the agenda.
Ignoring this convergence can lead to incomplete regulations that fail to address real risks, such as resource concentration, geopolitical dependencies, or infrastructure vulnerabilities.
Another critical issue discussed was the idea of sovereign AI. While many governments focus on building national AI capabilities, Alkhzaimi argues that sovereignty extends far beyond developing models.
True AI sovereignty includes control over compute infrastructure, energy resources, data governance, mineral supply chains, and talent development. Countries must also consider the sovereignty of datasets, algorithms, and the workforce required to maintain AI systems.
At the same time, she emphasized, “the conversation around sovereignty is a bittersweet conversation, because as much as you need that to be developed in the mix, since it’s a frontier technology, it’s emerging, and every national capacity, they need to have this kind of competency being developed on a local structure, you should still be able to collaborate globally in order to build that at a level of acceptable, mature status quo development.”
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For countries with fewer resources, Alkhzaimi offered practical guidance. Instead of trying to replicate large-scale AI ecosystems from scratch, emerging economies should focus on strategic governance frameworks.
This includes building transparent AI systems, implementing strong audit standards, ensuring lifecycle accountability for models, and investing in talent development. By combining these policies with economic incentives and cross-border partnerships, nations can still participate meaningfully in the AI economy.
The conversation concluded with insights into how the United Arab Emirates is positioning itself as a leader in AI governance.
According to Alkhzaimi, the UAE’s key strategy is agility, creating flexible regulatory frameworks and innovation sandboxes that allow experimentation while maintaining accountability.
She mentions, “The best trade-off when it comes to UAE approach is that UAE is prioritizing agility, because it’s a faster growing field. The way we develop technology today is quite on a fast track. It used to take us maybe around 20 years to develop those technologies, but today, within two to three months, you have faster structures around you.”
At the end of the discussion, Dr. Alkhzaimi made clear, the future of AI governance will depend not only on regulating algorithms but also on understanding the complex technological, economic, and geopolitical systems surrounding them.