What is a federated data model in trade?

A federated data model is an architecture that allows trusted, permissioned collaboration between supply chain partners while protecting sovereignty, privacy, and security. Each participant — a government, customs authority, enterprise, or logistics provider — operates in its own secure, isolated environment, and sensitive data never leaves that environment. AI models learn from patterns across the broader network and deploy those learnings back to each environment, acting as a translator between systems that could not otherwise speak to one another.

How does a federated data model protect data sovereignty?

A federated data model keeps each participant's sensitive information — trade records, Bills of Materials, sourcing relationships, pricing, and enforcement intelligence — inside its own dedicated environment, on sovereign soil and under sovereign control. What flows across the network is intelligence such as supply chain connections and risk signals, not the underlying data. This means governments and enterprises can collaborate while their private data never changes hands.

Why doesn't centralizing trade data solve the problem?

Centralizing trade data in a single sovereign location might offer simplicity, but it runs afoul of growing global expectations to protect a government's digital borders and an enterprise's most private information. Regulations like the EU Data Act legally and technically ensure that European entities — not foreign governments or technology providers — keep control over industrial data. A federated model preserves that control while still enabling collaboration.

What can governments and enterprises do with trusted trade intelligence?

Customs authorities can screen shipments against a global intelligence network without exposing their enforcement data to adversaries. Enterprises can trace multi-tier product value chains to N-tier accuracy without revealing trade secrets to competitors. Governments can collaborate with industry on sanctions enforcement, forced labor detection, and scaling production by sharing insights rather than data across borders.

Trade Analysis

As Sovereignty Concerns Rise, Governments and Enterprises Turn to a Federated Data Model for Trusted Trade

Key takeaways

Altana's federated data model lets sovereign governments, enterprises, and logistics providers share trade intelligence without surrendering their data. Each participant keeps sensitive information in its own secure environment while AI learns from the broader network — resolving the tension between data sovereignty and the collaboration that global commerce requires.

A federated data model lets sovereign governments and enterprises share intelligence across borders without their underlying data ever leaving their own secure environment.
Altana's federated data model is deployed today across sovereign environments in the U.S., UK, and Australia, where all data, analysis, and software run on government-controlled infrastructure.
The EU Data Act strengthens data sovereignty while also mandating interoperability, pushing manufacturers to stop locking insights in proprietary silos and share information within secure data spaces.
Across Altana's network, what flows between participants is intelligence — supply chain connections, risk signals, and insights — not the sensitive data itself, which stays on sovereign servers under sovereign control.

A surge of complex, component-based tariffs, the reshoring of critical industries, and the hardening of geopolitical alliances into economic blocs has made fragmentation the understood story of modern trade. Nations are pulling back behind sovereign borders to protect their economies, their industries, and their data.

But fragmentation is not the full story, even amid the ongoing trade war. The same governments building barriers still depend on global value chains to deliver the goods their economies need. Reindustrialization requires raw materials sourced from abroad. Economic security requires visibility into product value chains that cross countries and continents. Enforcement of trade regulations that govern activity far upstream in supply chains requires intelligence that no regulator possesses on its own.

This is the central trade challenge for global governments and enterprises: Sovereign nations need to regulate trade without concern that their data will be improperly accessed or their enforcement coerced by a foreign governments. Enterprises managing multi-tier, global value chains want to keep private their most sensitive information, such as price and trade secrets. But the ability to enforce trade regulations, to improve supply chain resilience, to remediate compliance risk, to calculate complex tariffs and collect duty payments, to have national security and a growing economy — all depend on translating secure trade data and sharing intelligence across jurisdictions.

It is possible to have prosperous global commerce and national security, and to protect data sovereignty while collaborating across the boundaries that have traditionally divided nations, regulators, enterprises, and logistics providers. Learn why data sovereignty goes hand-in-hand with interoperability and collaboration — and how a federated data model serves as a trusted trade translator.

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Data sovereignty expectations get stronger — and so does the need for interoperability and collaboration

Every nation has its own trade policies, customs systems, and regulatory priorities. And a single product may contain materials and components sourced from dozens of countries, assembled across multiple facilities, and shipped through a complex logistics chain before it ever reaches a port of entry.

The result is a translation problem. Non-standard data formats and enforcement priorities mean a lack of a common trade architecture or language. Information attached during stops along a product’s multi-jurisdictional journey — customs declarations, sanctions screens, tariff classification, origin determination — are inputted in different languages and structures. This makes it hard to move goods efficiently, enforce tariff and compliance frameworks fairly, or catch bad actors operating across jurisdictions.

Centralization will not solve this challenge. A single environment for trade data, housed in one sovereign location, might offer simplicity, but would run afoul of growing global expectations to protect a government’s digital borders and infrastructure and an enterprise’s most private information. Among the most prominent data sovereignty regulations: the EU Data Act, which legally and technically ensures that European government and business entities — not foreign governments or technology providers — maintain control over industrial data.

Headlines about the EU Data Act emphasize how it strengthens data sovereignty by ensuring that technology providers implement safeguards against illegal third-country data transfers. Less realized is the Data Act’s mandate for interoperability and intelligence sharing, which pushes legacy manufacturers to stop locking insights in proprietary silos by working within secure “data spaces” to share important information with business and government counterparts.

…‘interoperability’ means the ability of two or more data spaces or communication networks, systems, connected products, applications, data processing services or components to exchange and use data in order to perform their functions… High-quality and interoperable data from different domains increase competitiveness and innovation and ensure sustainable economic growth.

Just as the EU Data Act codifies data security and interoperability, global trade actors need to preserve sovereignty and enable collaboration by making vital, secure trade information intelligible across borders. This is possible with a federated data model.

A trusted translator: the federated data model

A federated data model is an architecture that allows for trusted, permissioned collaboration between supply chain partners while protecting sovereignty, privacy, and security.

Think of federated data in terms of the way your phone's keyboard learns how you type. The predictive text on your device understands your vocabulary, your patterns, the way you communicate. But your personal data never leaves your phone. The AI model improves by learning from patterns across millions of devices, without any single user's private information being shared with anyone else. You get the intelligence of a broader network while maintaining privacy on your own phone.

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Altana’s federated data model for global trade works on the same principle as your phone, at a much larger scale. Each participant — a sovereign government, a customs authority, an enterprise, a logistics provider — operates within its own secure, isolated environment. Sensitive data, such as trade records, bills of materials, sourcing relationships, pricing, and enforcement intelligence, never leaves that dedicated spoke. It stays on sovereign soil, on sovereign servers, under sovereign control.

What flows across Altana’s product network is not data, but intelligence: Supply chain connections, risk signals, and insights derived from the collective activity of all participants, which include the world’s biggest public and private sector organizations. AI models learn from the broader network's patterns and relationships, then deploy those learnings to each sovereign environment — acting as a trusted translator between systems that would otherwise be unable to speak to one another.

“Federated learning offers a third way, enabling shared information without commingled data or centralisation. This approach allows connection to and learning from sensitive, siloed information across a federated network. While privacy-preserving, federated learning enables extraction of signals from siloed data and facilitates shared intelligence across the participating network.”

"Managing Supply and Value Chains in a New Era of Global Trade: The Promise of Federated Learning", World Customs Journal, 2025

This is not a theoretical concept. For governments, Altana's federated data model is deployed today across sovereign environments in the U.S., UK, and Australia. In each deployment, all data, analysis, and software operate on government-controlled, sovereign infrastructure. Data ingress and egress are monitored and controlled by that sovereign government. The AI learns from the global network. The data never leaves home.

For enterprises and logistics providers, the model works the same way. Each participant gets a private, dedicated environment. Their most sensitive information stays local. But their supply chain connections — the relationships between facilities, products, and companies — contribute to a shared network that benefits everyone. Participants choose what they share and with whom through permissioned collaboration. The network gets smarter. The proprietary data that feeds it never changes hands.

The result is that data sovereignty moves from an obstacle to the foundation of secure information-sharing, collaboration, and trusted trade.

Data sovereignty as the foundation for trusted trade

When sovereign governments, enterprises, and logistics providers can speak to one another through a trusted translation layer, facilitating trusted trade becomes possible.

Customs authorities can screen shipments against a global intelligence network without exposing their enforcement data to adversaries. Enterprises can collaborate with suppliers to trace multi-tier product value chains to N-tier accuracy without revealing vital trade intelligence to competitors. Governments can collaborate with industry on sanctions enforcement, forced labor detection, and scaling production — sharing insights, not data — across allied and non-allied borders alike.

Fragmentation will continue to be a part of the story of global trade, and sovereign nations will continue to advance their own trade policies, but national and economic security can exist alongside prosperous global commerce and economic growth. With a federated data model, governments and businesses can share intelligence, collaborate, and build a more resilient, secure global economy that keeps trusted trade flowing.

Learn more about Altana’s network for trusted trade.

FAQs

A federated data model is an architecture that allows trusted, permissioned collaboration between supply chain partners while protecting sovereignty, privacy, and security. Each participant — a government, customs authority, enterprise, or logistics provider — operates in its own secure, isolated environment, and sensitive data never leaves that environment. AI models learn from patterns across the broader network and deploy those learnings back to each environment, acting as a translator between systems that could not otherwise speak to one another.
A federated data model keeps each participant's sensitive information — trade records, Bills of Materials, sourcing relationships, pricing, and enforcement intelligence — inside its own dedicated environment, on sovereign soil and under sovereign control. What flows across the network is intelligence such as supply chain connections and risk signals, not the underlying data. This means governments and enterprises can collaborate while their private data never changes hands.
Centralizing trade data in a single sovereign location might offer simplicity, but it runs afoul of growing global expectations to protect a government's digital borders and an enterprise's most private information. Regulations like the EU Data Act legally and technically ensure that European entities — not foreign governments or technology providers — keep control over industrial data. A federated model preserves that control while still enabling collaboration.

Altana's federated data model works on the same principle as predictive text on your phone. Your keyboard learns your vocabulary and patterns, but your personal data never leaves your device — the model improves by learning from patterns across millions of phones without sharing any single user's private information. Altana applies this at a much larger scale, so participants gain the intelligence of a broader network while their sensitive data stays local.
Customs authorities can screen shipments against a global intelligence network without exposing their enforcement data to adversaries. Enterprises can trace multi-tier product value chains to N-tier accuracy without revealing trade secrets to competitors. Governments can collaborate with industry on sanctions enforcement, forced labor detection, and scaling production by sharing insights rather than data across borders.