Centre for Space Futures

A successful dialogue would deliver a small number of clear, actionable outcomes that can be adopted and implemented globally: • Define global priority areas for AI governance: A concise set of agreed priority issues, with a shared understanding of risks, opportunities, and trade-offs across different regions and levels of development. • An initial governance framework with practical pathways: not just principles, but a structured framework outlining how governments, industry, and researchers can implement and align on governance mechanisms, including roles and responsibilities. • Concrete mechanisms for coordination and accountability: agreed processes or platforms to ensure ongoing collaboration, information-sharing, and accountability across stakeholders beyond the dialogue itself. • Cross-sector integration of AI governance: clear recognition of how AI governance applies across critical sectors such as space, health, energy, and finance, with identified areas requiring tailored approaches. • A roadmap with milestones and follow-through: defined next steps, timelines, and success metrics to ensure continuity, avoid fragmentation, and track progress over time. • Incorporation of proven governance models: explicit alignment with lessons learned from exiting frameworks, such as interoperability in civil aviation and coordinated global-local action in climate governance.

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

Artificial Intelligence is identified as the number one most relevant emerging space technology. In the space sector, supporting functions such as autonomous navigation, mission planning, and data analysis. Given the dual-use nature of many space technologies, the integration of AI introduces critical risks that require proportionate governance responses. • Safe, secure and trustworthy AI is a central priority for the high-stakes, safety-critical space environments where system reliability, resilience, and security are essential for the sustainable development of the global space economy and maintaining confidence among public and private actors. • AI capacity-building is simultaneously important as AI system and dependent on access to high-quality data, computing infrastructure, and technical expertise that remain unevenly distributed. Strengthening capacity for emerging actors in space is necessary to support the development of reliable systems and to enable broader and more equitable participation in the space economy. • Interoperability of AI governance is critical in space, where operations are inherently transnational and increasingly interdependent. While AI's dual-use nature may limit full openness due to national security and strategic considerations, this does not negate the need for interoperability. Instead, it reinforces the importance of targeted interoperability focused on safety-critical functions, shared standards, and coordination mechanisms. Such an approach can reduce fragmentation and systemic risk while allowing states to retain control over sensitive capabilities. • Transparency, accountability and human oversight are imperative safeguards against the risks associated with autonomous decision-making in high-risk environments. The complex, potentially irreversible outcomes of space operations make it necessary that AI systems remain auditable and subject to human supervision/ control allowing for intervention where necessary.

AI governance will increasingly need to be integrated within existing sectoral governance frameworks. In practice, AI is embedded within complex systems governed by distinct legal and institutional regimes. Greater attention is therefore needed on how AI governance can align with and be operationalised through existing structures rather than being treated as a parallel or standalone framework.

Space technologies using AI are rapidly, while governance frameworks are evolving more gradually. This has created a widening gap between technological capability and effective oversight, particularly as AI systems are increasingly used to support or automate decision-making in space operations. A Key challenge is that existing legal and regulatory frameworks were designed with assumptions of continuous human control and do not fully account for autonomous or semi-autonomous systems. This is further complicated by the dual-use nature of space technologies and strong incentives for innovation and investment, particularly in areas linked to national security. In practice, gaps in requirements for human oversight, limited transparency around system use, and insufficient risk assessment mechanisms increase the likelihood of unintended or irreversible consequences. Given the transnational nature of the space environment, these risks extend beyond national boundaries. Addressing these governance gaps is therefore critical to enable informed regulatory and investment decisions and to support the safe and sustainable development of the global space economy. Opportunities: • AI can significantly enhance the safety, efficiency, and sustainability of space operations, including orbital traffic management, mission planning and data analysis. • AI Governance presents a rare opportunity for proactive design, enabling 'pre-emptive' governance that anticipates risks rather than reacting to crises. , Lessons from internet governance, climate frameworks, and established models such as civil aviation and maritime can inform this approach. • Exciting soft law frameworks provide a foundation for coordination, including instruments such as the OECD AI Principles and institutional policies. These can be leveraged to strengthen alignment across governments, industry, and research actors while preserving space for responsible innovation. • Early investment in governance mechanisms can shape long-term market stability, supporting investor confidence and sustainable growth of the global space economy. Challenges: • Technological advancement is outpacing regulatory development, creating gaps in oversight, accountability and risk allocation. This is highlights the need for adaptable policy tools, including guidelines, safeguards, and soft law mechanisms. • Autonomous and semi-autonomous systems raise unresolved questions around liability, human oversight and system assurance, particularly in safety-critical and transnational environments such as space. • The dual-use nature of space technologies complicates governance, as states balance innovation with national security and strategic interests. Fragmented approaches risk reinforcing geopolitical tensions and creating uneven standards across jurisdictions.

The Dialogue can play an important role in advancing international cooperation by delivering a focused set of outcomes: • Align governance approaches across jurisdictions, particularly in transnational domains such as space, to support interoperable and coherent frameworks. • S Developing practical, sector-specific tools, including standards, risk assessment frameworks, and implementation guidelines to support regulatory and operational decision-making. • Enable inclusive, multi-stakeholder engagement, ensuring that emerging space actors and private sector participants contribute to shaping governance alongside governments. • Establishing mechanisms for sustained coordination, including shared timelines, milestones and follow-up processes to ensure continuity and refinement over time. These contributions are essential to bridge the gap between the pace of technological advancement and evolution of governance frameworks.

The Dialogue should build on a growing ecosystem of AI governance initiatives across regulatory, policy, and sectoral domains, while positioning itself to reduce fragmentation and enhance coherence. Key efforts include: • Regulatory frameworks, such as the EU AI Act, which introduces a risk-based classification of AI systems based on risks. • Principles-based approaches, including the OECD AI Principles, which provide widely adopted guidance on trustworthy AI. • National policy models, such as the UK's pro-innovation approach to AI regulation, balancing innovation with risk management. • Sector-specific guidelines, such as CIArb's 2025 Guidelines, which take an ethics-based approach to governing the use of AI by arbitration, helping preserve professional integrity and trust. • Space-specific mechanisms, such as the United Nations Office for Outer Space Affairs' (UNOOSA)2025 Earth observation initiative , which reflects an emerging multilateral response to governance challenges associated with AI-enabled space applications. While still limited in scope, it provides an early reference point for how sector-specific governance approaches can evolve. The added value of the Dialogue lies in its ability to connect and align exiting approaches, identify gaps, and translate them into practical, cross-sector governance tools without duplicating ongoing efforts. In this context, ongoing efforts across convening platforms and expert-led initiatives, including work by the Centre for Space Futures highlighting AI as a key technology shaping the future of the space sector, can serve as important inputs to the Dialogue. Drawing on such insights will help ensure that governance approaches are informed by real-world developments and can be translated into practical, sector-relevant outcomes.

Effective AI governance requires structured, multi-stakeholder engagement, particularly in transnational and dual-use domains such as space. Stakeholder contributions: • Governments provide policy direction, regulatory frameworks, and oversight. • International organisations (e.g the Organisation for Economic Co-operation and Development(OECD), and the World Bank Group) support coordination, standard-setting, and cross boarder alignment. • Private sector actors, such as developers, operators, and professional bodies such as the International Bar Association, contribute technical expertise and implementation insights. • Academia, research institutions, and think-tanks provide independent analysis and evidence-based recommendations, including inputs to the International Scientific Panel on AI. • Emerging actors, including developing nations and youth communities, bring critical perspectives to ensure inclusivity and adaptability across different contexts. Recommended format and structure: • Thematic working groups with diverse representation to enable in-depth, technical discussions. • . Plenary sessions to define priorities, align outputs, and maintain coherence • Public consultation mechanisms, including digital platforms, to broaden participation beyond formal attendees. • Structured follow-up processes, with defined milestones and review points to ensure continuity and accountability.

While the space sector is increasingly recognised, its perspectives remain insufficiently integrated into broader AI governance discussions, particularly given the safety-critical and transnational nature of its applications. Space represents a convergence point for key AI governance challenges, including autonomy, interoperability, and fragmentation. However, governance discussions often do not fully reflect the complexity and pace of developments within the sector, particularly as commercialisation accelerates and the diversity of actors expands. This includes perspectives from emerging spacefaring nations, private operators, and technical communities working on AI-enabled space systems, whose inputs are not yet consistently reflected in global governance processes. Addressing this gap requires more deliberate integration of space-sector considerations into AI governance frameworks, including through dedicated thematic tracks, targeted working groups, and stronger engagement with sector-specific stakeholders.

Given the multi-dimensional nature of AI applications, incorporating cross-sectoral roundtables to explore how governance approaches can be adapted and operationalised in different contexts to bring sectoral realities into general discussions.

One concrete example is the National Institute of Standards and Technology (NIST) AI Risk Management Framework. The framework's focuses on risk mapping, measurement and management making it a highly operational mechanism in practice and transferable to different sectors while supporting consistency. The risk-focused, flexible approach is important for high-risk sectors where risk management tools can support more informed design, deployment and oversight in the absence of regulatory safeguards. Moreover, the NIST framework illustrates the value of aligning soft law mechanisms with sector-specific applications to remain practical and interoperable. However, such mechanisms simultaneously act as buffers for when regulation can step in for greater consistency and long-term applicability. While the framework provides a concrete example, it is prudent not to rely on soft law mechanisms to fill regulatory gaps, contributing to continued fragmentation and discrepancies. Rather such examples should be used to inform global AI governance approaches as the Dialogue is the platform capable of sustainably fulfilling the vision of responsible, equitable, ethical and innovative use of AI.