AI trust, security and diffusion in small states and low- and middle-income countries

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  • Date
    28 April 2026
    Timeframe
    16:00 - 17:00 CEST
    Duration
    60 minutes

      AI systems are transforming our societies, but without proper security evaluation protocols and context aware safeguards, they risk undermining public trust  and introducing new vulnerabilities into existing public and private systems.

      In many contexts, the ability to scale AI beyond pilots is increasingly tied to whether trust and security practices are in place to support trusted deployment by both governments and the private sector.

      Low- and middle-income countries (LMICs) and Small Island Developing States (SIDS) act as stress-tests for global AI systems. As AI diffuses into digital public infrastructure and service delivery, these contexts expose where systems fail to perform reliably, where oversight mechanisms are insufficient, and where responsibility across the AI lifecycle remains unclear.

      At the same time, these countries reflect the broader reality of global AI adoption: most are adopters, not builders. Strengthening safeguards in these environments is a prerequisite for enabling sustainable AI adoption, unlocking private sector participation, and supporting the development of local AI markets and broader economic opportunity. 

      Session Objectives:

      The objectives of this webinar, organized jointly by ITU, UNDP and the AI Hub for Sustainable Development, are to: 

      • Surface the key challenges and emerging opportunities in procurement, market development, and deployment of AI responsibly in   challenges and opportunities for innovation facing low- and middle-income countries and the global south when procuring AI systems; 
      • Discuss AI model performance accuracy and security exacerbated by linguistic and digitized data inequality;
      • Explore scalable, proportionate approaches to AI security and Policy, leveraging international standards, multistakeholder cooperation, and capacity building for skills and institutional readiness to avoid a widening AI trust divide; and
      • Identify pathways for responsible AI diffusion, enabling countries to scale beyond pilots adopt AI for development impact and integrate AI into systems while strengthening trust, accountability, and institutional readiness.

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