A blueprint for a new and equitable global data trade

COMMENTARY By Camille Stewart Gloster

In order to foster equitable global innovation in artificial intelligence, every country—no matter its size or location—needs a way to participate. That is why it’s imperative to create a new and accessible global trade in data, the essential ingredient for any advancement in AI. A global data market will not just accelerate innovation but create opportunities for progress in areas such as health care, climate-change mitigation, international security and beyond. 

At the moment, most countries are grappling with whether to treat data like air or oil. Treating it like oil recognizes the commoditization of data; treating it like air frames it as a ubiquitous resource vital for collective well-being that should therefore be freely shared and collaboratively used for the common good. 

In practice, many countries find themselves balancing the two perspectives—seeking to maximize the economic potential of data while recognizing the societal benefits of treating it as a shared resource. The key lies in developing frameworks that respect data sovereignty and privacy while encouraging collaboration and innovation. 

A new model for global digital trade would enable countries to use data as a valuable asset to barter for necessary resources such as technology, energy and other commodities. To preserve data sovereignty and privacy, this system could operate through a secure global digital marketplace to which countries contribute anonymized datasets. The value of the data would be determined using standardized metrics such as quality, volume and potential for generating insights or driving innovation in specific areas. Transactions could be facilitated using blockchain for transparency, and smart contracts would enforce terms of use and protect against misuse.

When multiple entities collaborate to train AI models by sharing data, it’s known as federated learning. Federated learning can play a pivotal role in the global-market model by enabling countries to share the value of their data without transferring the raw data itself—particularly important for highly sensitive datasets. In this system, algorithms would analyze a country’s local data, and only aggregated, anonymized insights or model updates would be shared. This ensures that sensitive information remains secure and compliant with local regulations, while still allowing everyone to contribute to and benefit from global advancements. For example, a country with rich biodiversity data could use federated learning to train global AI models for climate adaptation, and in return, gain access to technology or funding to protect its ecosystems. This would allow us to advance collective sustainable development goals while acknowledging the value of the data and protecting the interests of the country and its citizens. 

Importantly, this system would prioritize inclusivity. It would enable smaller or resource-scarce nations to participate meaningfully by offering high-value datasets in areas like renewable energy potential. Such a system would not only democratize access to critical resources but also foster global collaboration, with shared data driving innovations. Equally important, models like this allow for security, privacy and other safety principles to be part of the negotiations for data use and governance. By treating data as a strategic asset, this model could redefine international trade and promote equitable economic growth.

International laws would need to adapt to enable data to be bartered for necessary resources as part of digital trade agreements. Current frameworks governing data—such as privacy regulations, intellectual property laws and international trade agreements—are not designed to treat data as a tradable commodity on par with traditional goods and services. For instance, privacy laws, like the EU’s General Data Protection Regulation or data localization mandates in countries such as India and China, impose restrictions on how data can be transferred or used across borders. 

Additionally, international trade agreements, including those governed by the World Trade Organization, lack specific provisions for data as a barterable resource. Defining data as a tradable asset within these frameworks would require legal classifications and mechanisms for dispute resolution, taxation and valuation. Intellectual property laws would also need to be updated. Harmonizing these legal changes across jurisdictions would be critical to ensuring fairness, trust and accountability in a global data barter system, allowing countries to exchange data for resources while addressing concerns related to privacy, security and equitable access.

The work to form new digital trade agreements can begin now through bilateral or regional trade agreements. Countries can initiate pilot programs to showcase the feasibility and value of data barter. For example, one country might partner with private-sector entities or international organizations to create a secure and ethical data-sharing model. These initiatives could serve as proof of concept, strengthening a nation’s position in negotiations. 

Additionally, as countries seek to modernize trade rules, like-minded nations should band together in coalitions—such as the G-77 or regional blocs like the ASEAN or the Caribbean—to advocate for their interests in larger multilateral forums like the WTO or the Organisation for Economic Co-operation and Development. In a future driven by advancements in AI, data is both air and oil—creating prosperity for everyone. 

Camille Stewart Gloster is a global technology and cybersecurity leader specializing in bridging policy and practice to enhance resilience, trust and security. As CEO of CAS Strategies, LLC, she advances a sociotechnical approach that ensures organizations worldwide can build adaptive, defensible ecosystems to navigate the evolving threat landscape and maximize opportunity.