AUB

A successful first Global dialogueon AI Governance should deliver practical, implementable outcomes rather than broad principles such as at AI systems are accompanied by standard documentation (purpose, data sources, and limitations), have clearly assigned accountability, includehuman oversight for high risk use cases, and are subject to a risk based classification that determines the level of controls required. And as data is the foundational starting point for AI and other emerging technologies, robust data governance practices should be developed.

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

Safe, secure and trustworthy AI is essential requiring risk management, security and privacy by design and resilience across the lifecycle.

data governance as a foundational layer is not sufficiently emphasized. As AI systems are inherently data driven, weaknesses in data quality, integrity, ownership, and access control directly translate into risks in AI outputs. Strengthening data governance frameworks should therefore be treated as a core pillar rather than a supporting element. Also, integration with existing governance and risk frameworks is critical. AI governance should not evolve in isolation but be embedded within established structures such as enterprise risk management, information security (ISO 27001), and data protection frameworks. This integration is essential for scalability and sustainability.

hallenge relates to data governance and infrastructure. In many contexts, data management practices remain inconsistent, with gaps in data quality, classification, ownership, and access control. Given that AI systems are fundamentally data driven, these weaknesses directly affect the reliability, security, and trustworthiness of AI outcomes.

it can promote multi stakeholder coordination, bringing together governments, industry, academia, and civil society to co develop practical tools, guidance, and standards. Similar approaches have proven effective in initiatives such as the Internet Governance Forum , which demonstrate the value of inclusive dialogue in shaping globally relevant and implementable frameworks. Such collaboration is essential to ensure that governance approaches remain both technically informed and operationally feasible. Also, the Dialogue can facilitate knowledge sharing and exchange of good practices, allowing countries and organizations at different levels of maturity to learn from existing experiences especially or supporting developing countries in building institutional capacity

The AI Dialogue should build on existing initiatives such platforms such as the Internet Governance Forum which provides a well established model for inclusive dialogue, bringing together governments, private sector, academia, and civil society. International standardization efforts through the International Organization for Standardization which offer practical, implementable frameworks (such as risk management, information security, and emerging AI standards). Those frameworks are critical for translating l principles into operational controls that organizations can adopt. Alslo, learn from standards and Acts such as EU AI Act WHICH provide valuable insights into risk based approaches and governance structures

At the operational level, working groups should focus on specific areas (risk classification, transparency, data governance), with clear deliverables such as guidance, templates, and policy recommendations. These groups should include stakeholder representation and operate on defined timelines. the Dialogue should incorporate practical outputs, such as pilot initiatives, shared toolkits, and standardized approaches that organizations can directly apply. Similar to how EDUCAUSE works

scenario based simulations and multi stakeholder roundtables with defined outputs can ensure balanced participation while maintaining focus.

he EU AI Act offers a risk-based regulatory model, ISO/IEC 27001 (information security) and emerging AI management standards