Ministry of Information Technology and Artificial Intelligence of Lebanon

A successful first Global Dialogue on AI Governance would move beyond broad principles and produce outcomes that are both actionable and credible across different contexts. First, it should establish a shared understanding of priority risks, especially around misuse, security, and rapidly advancing capabilities, without requiring full consensus on long-term trajectories. Even partial alignment would improve coordination. Second, it should generate practical governance pathways that countries with varying levels of capacity can adopt. This includes effective approaches to oversight, risk assessment, and responsible deployment, not just frameworks suited to well-resourced ecosystems. Third, it should address trust and information gaps between frontier developers, governments, and smaller states. This could include mechanisms for structured information sharing, independent evaluation, or verifiable reporting that do not rely solely on voluntary disclosure. Finally, success would be measured by continuity through working groups, pilot collaborations, or follow-on processes that sustain momentum beyond the event.

• Safe, secure and trustworthy AI
• Interoperability of governance approaches
• Transparency, accountability, and human oversight
• AI capacity-building

My selections reflect a focus on making AI governance actionable, especially across contexts with uneven capacity. Safe, secure and trustworthy AI is foundational as systems scale in capability and access. In practice, this means addressing concrete risks such as misuse, adversarial attacks, and vulnerabilities in how models are deployed and integrated. It also requires moving beyond principles to mechanisms that ensure systems are tested, monitored, and resilient in real-world conditions. Transparency, accountability, and human oversight are essential to make this possible. Without visibility into how systems behave and clear lines of responsibility, it is difficult to enforce safeguards or respond to failures. I see this as a bridge between technical safety work and governance, ensuring that oversight is not purely formal but operational. Interoperability of governance approaches is critical in a fragmented regulatory landscape. Diverging standards can create gaps that weaken protections and complicate compliance. Greater alignment, even at a baseline level, can help reduce risk while enabling cross-border collaboration and innovation. Finally, AI capacity-building is central to ensuring that countries like Lebanon can meaningfully participate in shaping AI governance. This includes developing local expertise, strengthening institutions, and creating pathways for informed policy engagement. Without this, governance risks becoming concentrated in a few regions, limiting its effectiveness and legitimacy globally.

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In Lebanon and the broader region, governance gaps in AI are most visible in the mismatch between rapid access to advanced systems and limited institutional capacity to manage their risks. One key challenge is exposure to misuse without corresponding safeguards. AI tools are increasingly used across sectors, including media, finance, and cybersecurity, but there are few established standards for secure deployment or risk assessment. This creates vulnerabilities to disinformation, fraud, and cyber-enabled harm, particularly in already fragile information environments. A second challenge is the lack of technical and regulatory infrastructure. Policymakers often have limited visibility into how these systems work or where risks emerge, while local developers and organizations lack clear guidance on responsible use. This widens the gap between global advances in AI safety and what can be implemented locally. At the same time, there are important opportunities. Because governance frameworks are still emerging, there is space to build more adaptive and context-aware approaches from the outset, rather than retrofitting rigid systems. Capacity-building can play a catalytic role by developing local expertise that bridges technical and policy domains. There is also an opportunity for the region to engage more actively in shaping interoperable governance approaches, ensuring that global standards reflect a wider range of contexts. With the right support, countries like Lebanon can move from being passive recipients of AI systems to active contributors to how they are governed.

The AI Dialogue can play a practical role in advancing international cooperation by creating a space that bridges technical expertise, policy discussions, and geopolitical realities. Its value lies in convening not only leading AI actors but also countries and regions that are often underrepresented in governance processes. It can help move cooperation from high-level principles to more operational alignment, for example by identifying shared risk priorities, common baselines for safety and security practices, and approaches to oversight that can work across different regulatory environments. The Dialogue can also reduce information asymmetries by enabling more structured exchanges between frontier developers, governments, and researchers. Importantly, it can support more inclusive cooperation by ensuring that capacity-constrained countries have a voice in shaping norms, rather than only adopting them. By fostering trust and sustained engagement, the Dialogue can act as a bridge between fragmented initiatives and contribute to more coordinated global governance.

The AI Dialogue should build on existing efforts such as the UN High-Level Advisory Body on AI, the Global Digital Compact process, the OECD AI Principles, and safety-focused initiatives like the UK AI Safety Summit and related frontier model discussions. These efforts have helped establish shared language and initial areas of alignment. However, many of these initiatives remain either high-level or limited to smaller groups of countries and actors. The added value of the AI Dialogue would be in connecting these processes, widening participation, and focusing on implementation. It can serve as a platform that links principles to practice by facilitating exchanges on what is actually working in areas such as risk assessment, oversight, and capacity-building. The Dialogue can also add value by elevating perspectives from regions that are not deeply represented in existing forums

A useful format would combine plenary sessions for alignment with smaller, thematic working groups tasked with producing concrete outputs such as draft guidelines, risk frameworks, or cooperation mechanisms. Including regional breakouts can ensure discussions are grounded in different contexts. To be effective, the Dialogue should prioritize continuity through follow-on working groups or pilot collaborations, rather than one-off exchanges.

Global AI governance discussions often underrepresent countries with limited technical and regulatory capacity, particularly in the Middle East, Africa, and parts of Asia. In these contexts, AI is often adopted faster than governance frameworks can develop, yet their perspectives are rarely reflected in standard-setting processes. There is also a gap in voices that sit between technical and policy domains, including practitioners working on implementation, security, and deployment in real-world environments. Inclusion requires more than invitations. It means enabling meaningful participation through targeted support, clearer pathways to contribute to outcomes, and formats that do not assume high baseline capacity. Regional representation, partnerships with local institutions, and mechanisms to carry input into decision-making processes are essential to ensure these perspectives shape, rather than follow, global governance.

One approach is scenario-based exercises where participants respond to realistic AI risk situations, such as misuse incidents or system failures, and work through governance responses. This can surface differences in assumptions and highlight practical constraints. Small, mixed-stakeholder design sprints could also be effective, where groups are tasked with developing concrete proposals on issues like risk thresholds, oversight mechanisms, or capacity-building models within a limited timeframe.

On the technical side, structured evaluations and red-teaming practices are becoming essential. Leading AI labs have begun implementing pre-deployment testing, external red-teaming, and staged release strategies, which help identify misuse risks and system vulnerabilities before wide deployment. Transparency mechanisms such as model cards and system documentation also contribute by improving visibility into how systems are trained, evaluated, and intended to be used. While still uneven in practice, they provide a foundation for accountability. There are also emerging approaches to controlled access, including API-based deployment, usage monitoring, and tiered access to more advanced capabilities. These can help limit misuse while still enabling innovation.