Awoke Technologies

In my view, a successful first Global Dialogue on AI Governance should go beyond principles and deliver practical, inclusive, and implementable outcomes. First, we need a shared global foundation for AI governance—anchored in transparency, accountability, fairness, and data protection—but flexible enough to reflect different national realities. However, what would truly define success is how we address the growing divide between developed and developing nations. Today, the conversation often focuses on access to compute and infrastructure. While important, this is not sufficient. If countries only consume AI built elsewhere, the gap will continue to widen. We need to shift the focus from access to capability and ownership. This means enabling countries to develop, adapt, and deploy AI solutions locally, supported by skills development, open and affordable technologies, and strong knowledge transfer. Countries should not just use AI—they should be able to shape it. A critical part of this is data sovereignty. Nations must have the ability to host, manage, and govern their data within their own trusted environments, including local or regional data centers. This allows them to apply governance and privacy frameworks aligned with their legal and cultural contexts, while also ensuring that the value generated from their data benefits their own economies. Success would also require clear implementation pathways—practical guidance on risk management, auditing, and responsible AI use—along with sustained capacity-building efforts. Ultimately, success means moving from discussion to equitable global action, where AI is not only accessible, but empowering—enabling all countries to build, govern, and benefit from it on their own terms.

Safe, secure and trustworthy AI;AI capacity-building;Open-source software, open data and open AI models;Transparency, accountability, and human oversight;

In my view, the success of the first Global Dialogue on AI Governance will depend on how effectively it translates key priorities into practical and inclusive outcomes. Advancing safe, secure, and trustworthy AI, supported by transparency, accountability, and human oversight, is essential to building trust. However, governance should not only focus on managing risks-it should also create the conditions for broader and more equitable participation in AI development and use. A central measure of success will be meaningful progress in AI capacity-building. This must go beyond access to infrastructure and instead enable countries to develop, adapt, and apply AI within their own contexts. Leveraging open-source software, open data, and open AI models can play a critical role in lowering barriers to entry, supporting innovation, and reducing dependency on a limited number of global providers. At the same time, it is important to fully consider the social, economic, ethical, cultural, linguistic, and technical implications of AI. AI systems should reflect the diversity of the communities they serve, ensuring that different languages, cultures, and societal needs are represented. Without this, AI risks reinforcing existing inequalities rather than addressing them. In this context, strengthening local capacity to manage and govern data responsibly becomes equally important. Enabling countries to operate within trusted data environments allows them to align governance and privacy approaches with their own frameworks, while supporting sustainable and locally relevant AI ecosystems. Ultimately, success will be defined by whether these efforts come together to ensure that AI is not only safe and accountable, but also accessible, inclusive, and supportive of local development across all regions.

While the listed themes cover many critical areas, there are a few cross-cutting issues that deserve more explicit attention. One key area is data sovereignty and data governance infrastructure. While elements of this are implied under human rights and accountability, there is a growing need to explicitly address how countries can store, manage, and govern their data within trusted environments, including local or regional data centers. This is essential not only for privacy and security, but also for ensuring that the economic value generated from data remains within local ecosystems. Another important issue is the unequal distribution of compute and digital infrastructure. AI capacity-building is highlighted, but without addressing structural disparities in access to compute, connectivity, and energy resources, many countries will remain dependent rather than empowered. This raises questions of long-term sustainability and equitable participation in AI development. A further emerging area is operationalizing governance. While interoperability of governance approaches is important, there is a gap in translating principles into practical, implementable mechanisms-such as auditing frameworks, risk classification standards, and enforcement models that can be adopted across different contexts. Additionally, there is a need to consider the concentration of AI capabilities among a small number of actors. This has implications for competition, innovation, and global equity, and intersects with the role of open-source and open models in democratizing access. Finally, the environmental impact of AI systems-including energy consumption and resource use-is an emerging concern that cuts across technical and policy discussions. Addressing these cross-cutting issues would strengthen the dialogue by ensuring that AI governance is not only principled, but also practical, equitable, and sustainable in the long term.

The AI Dialogue can play a critical role by moving international cooperation from alignment in principle to coordination in practice. First, it can serve as a platform to build shared understanding and trust across countries with different levels of technological maturity. By creating space for open exchange, it helps ensure that governance discussions are not dominated by a few actors, but instead reflect a broader range of perspectives, including those of developing regions. Second, the Dialogue can advance cooperation by supporting interoperability of governance approaches. Rather than enforcing uniform models, it can help identify common building blocks—such as risk frameworks, accountability mechanisms, and standards—that countries can adapt to their own contexts while still enabling cross-border collaboration. A key contribution of the Dialogue is in strengthening AI capacity-building as a shared global effort. International cooperation should not be limited to policy discussions; it should include knowledge transfer, technical partnerships, and access to open resources such as open-source tools, open data, and open models. This helps reduce fragmentation and enables more countries to actively participate in AI development. The Dialogue can also play an important role in promoting responsible data governance practices, including approaches that support trust, security, and appropriate levels of data control. This is essential for enabling collaboration while respecting national priorities and legal frameworks. Finally, the Dialogue can help translate global discussions into practical actions, such as pilot initiatives, collaborative frameworks, and ongoing working groups that continue beyond the initial engagement. Ultimately, its value lies in ensuring that international cooperation is not only about coordination among advanced economies, but about creating a more inclusive and balanced global AI ecosystem, where all countries can contribute to and benefit from AI responsibly.

There are already several important initiatives shaping AI governance, including frameworks from OECD, UNESCO, and regulatory and standards work led by organizations such as ITU and ISO/IEC. In addition, multi-stakeholder communities like ISOC and ICANN provide established, trusted structures that operate across global, regional, and country levels. The key gap is not the absence of initiatives, but the lack of connection between global frameworks and local implementation. The AI Dialogue, particularly under ITU's leadership, can play a critical role in bridging this gap. Rather than creating new principles, it can leverage existing ecosystems—such as ISOC chapters, ICANN communities, and regional ICT associations—to extend governance discussions into country and continent-level engagement. This would ensure that governance is informed by real needs on the ground, including data management, infrastructure readiness, and local language and cultural contexts. The added value of the Dialogue lies in enabling continuous, community-driven implementation, strengthening capacity-building, and promoting access to open technologies. Ultimately, it can connect global coordination with local action, making AI governance more inclusive, practical, and sustainable.

Different stakeholders should contribute through continuous, multi-level engagement, not just representation. Governments can provide policy direction and enable capacity-building; the private sector can share technical expertise and tools; academia and civil society can ensure ethical oversight and inclusion. The AI Dialogue should be structured as an ongoing, multi-layered process. Global forums can drive alignment, while existing regional and national networks—such as ISOC chapters, ICANN communities, and ICT associations—extend engagement locally. A combination of plenary sessions, focused working groups, and community-based dialogues can create a feedback loop between global frameworks and local realities, ensuring AI governance is practical, inclusive, and continuously evolving.

AI governance must recognize the agentic and rapidly evolving nature of AI systems. The understanding and frameworks we create today cannot fully serve tomorrow's realities. As AI agents gain autonomy and new capabilities, they introduce both opportunities and emerging risks. This requires governance to shift from static rules to adaptive, continuous approaches-including ongoing monitoring, auditing, and human oversight throughout the lifecycle. Open collaboration and shared learning are essential to keep pace with these changes. Ultimately, AI governance should function as a living system, evolving alongside the technology to remain effective, responsive, and aligned with societal needs.