Trust as a verifiable property

The paper frames trust as a precondition, not an outcome. An organization will allow an external agent to act on its data only if it can verify, before any action, that the agent, the data and the services involved are what they claim to be.

In a data space this verification is automated. Verifiable Credentials carry fitness attestations, accreditation proofs and conformity certificates alongside the assets they describe. A participant can check them at the point of use without retrieving documents manually and without gaps in the audit chain. The trust hierarchy that organizes these credentials means each check can be verified locally without re-involving whoever issued it.

This makes two distinct things verifiable. The first is the AI system itself: the quality and conformity of a model become transparent through data space credentials, and a bill of data, a bill of software and a bill of licences can document what went into its training. The second is data access: participants have full transparency over what data may be used for what purpose, which directly supports both intellectual property protection and compliance with data governance rules.

Data quality and what it means for AI

One of the most persistent challenges for AI is not a lack of algorithms but a lack of reliable, well-described and legally usable data. AI systems depend on data that is accurate, representative, timely and sufficiently complete for the purpose. Across organizational boundaries, this challenge is sharper: the relevant data often sits with competitors, public authorities, suppliers or research institutions, and even where it exists it may be withheld for reasons of confidentiality, commercial sensitivity or privacy.

A data space addresses this by creating a governed environment for trusted sharing without centralizing the data. Each dataset carries its provenance, collection method, update frequency, quality indicators, semantics, usage constraints and licensing — the context a developer needs to judge whether it is fit for a given purpose. Treated this way, a dataset becomes what the paper calls a data product: not raw signals, but information refined for reuse, carrying the content, quality, context and machine-readable format that a model or application can consume directly.

Observability and regulatory compliance

The paper covers the regulatory landscape in depth. Across the EU, Japan, China, South Korea, Brazil, India and the United States, the approaches to AI governance differ — from binding horizontal regulation under the EU AI Act (Regulation 2024/1689) to promotion-oriented soft law and sector-specific rules. The common thread is that whatever the regulatory style, data spaces supply the operational mechanisms that turn governance expectations into verifiable practice: identity, usage policies, provenance and audit logs.

For EU-based organizations, the alignment is direct. The EU AI Act’s requirements around data governance, documentation and traceability can be demonstrated in practice rather than only asserted, because data space infrastructure records what was exchanged, under which policies, by whom and for what purpose. GDPR compliance is supported in the same way. The European Health Data Space Regulation already mandates compute-to-data approaches for secondary use of health data, and data spaces such as genome.de and sphin-X are implementing these approaches in practice.

Observability and traceability — the ability to reconstruct where data came from, how it was modified, who accessed it and whether it was used according to agreed conditions — are core operational capabilities of a trustworthy data-sharing environment, as defined in ISO/IEC 20151-1. The paper is specific about what this enables: AI governance shifts from a static documentation exercise to an operational capability.

The limits of data space guarantees

The paper is careful about the limits of what data spaces can provide. A data space cannot make a model fair or a dataset unbiased. It can make the provenance, quality, permitted uses and accountability of the data verifiable to everyone who relies on it. That turns several of the EU High-Level Expert Group’s trustworthy AI requirements from stated commitments into properties that can be checked.

Where synthetic data is shared in a data space, its synthetic nature can be explicitly declared through metadata, provenance information, usage policies and quality indicators. Consumers can then assess suitability without assuming equivalence with the original data. That transparency is the contribution — not a guarantee of fitness, but a basis for informed judgment.

The third post in this series covers agentic participation: how AI agents can act inside data spaces under delegated identity, and what governance structures make this safe at scale.

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The relationship runs in both directions, and that bidirectionality is what makes it worth examining carefully.

The governed data foundation AI needs

Most valuable data sits inside organizations that will not share it without legal clarity about permitted uses, liability and the boundaries of data sovereignty. Web scraping and generic platform terms of service cannot reliably reach this data. A data space can. It lets providers and consumers negotiate access terms directly, for each dataset and each intended use, under machine-readable usage policies that make those terms explicit and enforceable.

For AI development, this means access to curated, domain-specific datasets with verifiable provenance. Shared catalogues make data findable across organizational boundaries. Federated identity mechanisms extend trust between parties that have no prior relationship. Usage policies expressed in open standards such as the Open Digital Rights Language (ODRL) specify what data may be used for and under which conditions. These are the governance properties a general AI stack assumes are already settled. In most cross-organizational settings, they are not. Data spaces provide them.

The automation data spaces need to scale

Building a data space means integrating its components into the existing systems of many different participating organizations. That integration work is expensive. AI substantially reduces the cost by generating metadata, aligning schemas across heterogeneous backends, translating human-readable legal terms into machine-readable policies and monitoring policy compliance across participants. These are tasks that would otherwise require sustained manual effort for every new participant onboarded and every new dataset described.

The FAIR principles — Findability, Accessibility, Interoperability and Reusability — provide a practical way to map out where each direction of the relationship applies. On the findability dimension, AI supports metadata generation and semantic search, making catalogues more useful. On the accessibility dimension, the Model Context Protocol (MCP) provides a standardized interface through which AI systems can connect to heterogeneous data services. On interoperability, AI assists in aligning vocabularies and bridging terminology differences without requiring full upfront standardization. On reusability, AI can help translate legal terms into enforceable policies and assess whether datasets are fit for a given purpose.

Three patterns of collaboration

Research by Fujitsu and Fraunhofer ISST describes three patterns by which AI workloads draw on a data space. In collaborative model development, organizations train a shared model by exchanging data or model parameters, with the data space providing the usage policies and provenance records that govern what enters training. In inter-organizational model inference, one organization enriches its own model at inference time with data held by others, with the data space providing discovery, access control and usage conditions. In inter-organizational agent collaboration, autonomous agents from different organizations accomplish a task together, with the data space providing participant identity, contract negotiation and an auditable record.

These patterns recur across the pilots documented in the paper, from AI-enabled robotics in Germany to data marketplace negotiation in Japan. They are not theoretical. They are working in practice.

Implications for organizations

For AI practitioners, the paper is a caution against treating API connections as automatically trustworthy. A model-connected interface is not automatically compliant. A retrieval-augmented generation pipeline is not automatically authorized. Data spaces supply the missing layer: identity, contract, usage control, semantic interoperability and provenance.

For data space practitioners, the paper is a preparation guide. The familiar primitives — participants, credentials, catalogues, data products, Connectors, usage policies — are sufficient. What changes is that some participants will now be autonomous agents. Handling that requires making agent identity, scope, tool access and audit explicit. The foundations are already in place.

The next post in this series looks at how data spaces make AI trustworthy across organizational boundaries — covering the trust framework, verifiable identity and what the regulatory landscape requires.

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The collaboration will focus on aligning Tractus-X with core IDSA assets. These include the IDS Reference Architecture Model, governance frameworks, standards and protocols. Both organizations will support the evolution and implementation of essential data space standards such as DSP and DCP, along with data space components including connectors, data planes and digital twins. The partnership also extends to industry-specific KITs, with the goal of ensuring interoperability across industries and ecosystems.

The cooperation spans several strategic areas.

• Architecture and standards harmonization
• Governance, certification and security-by-design approaches
• Community building through workshops, hackathons and enablement activities
• Open-source infrastructure and development environments
• International scaling of data spaces and stronger collaboration across global initiatives

ARENA2036 and IDSA also intend to unlock synergies between Tractus-X, Manufacturing-X, the Data Spaces Support Centre (DSSC) and other European initiatives. The aim is to accelerate trusted data sharing and digital sovereignty at scale.

Trusted and interoperable data exchange forms the foundation for connected value chains, industrial AI and digital ecosystems. By combining their strengths, ARENA2036 and IDSA are taking a further step toward building a globally connected data economy.

Both organizations thanked everyone involved in the partnership and expressed confidence that the shared ambitions will translate into tangible results.

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Company Certificate Management in Catena-X addresses exactly this: a certificate is uploaded once and shared securely with all business partners, with auditable access. It is the entry use case of the Data Space Accelerator and a concrete way to experience what productive data exchange looks like in day-to-day operations.

What the Data Space Accelerator is

The Data Space Accelerator is a study investigating how companies move from initial readiness to productive data exchange in Catena-X. The potential of data spaces is widely recognized, while there is still limited empirical evidence on which factors explain successful onboarding and where the gaps behind slower adoption lie.

The study addresses this with a before-and-after survey design: participating companies are assessed at the start of onboarding and again after completing their first productive data exchange. This makes it possible to diagnose typical readiness gaps along the data value chain, identify company profiles and use cases with a higher likelihood of successful adoption, and derive targeted levers to accelerate onboarding. The reference data space is Catena-X, and the use cases are standardized Catena-X use cases documented in the open-source repository Tractus-X.

For participating companies, the study is designed to deliver:

• empirical evidence on the economic and organizational benefits of data sharing,
• actionable recommendations to strengthen digital readiness,
• case-based insights from multiple use cases.

What participation involves

Participation focuses on one or more concrete use cases implemented in real operations, supported by certified partners who guide companies through each step. The path follows six stages:

1. Submit an offer and choose the program path.
2. Review and acceptance against the tender documents.
3. Select the use case (entry: Company Certificate Management).
4. Guided onboarding into the Catena-X data space.
5. Complete the first productive data exchange.
6. Receive the milestone-based payout.

Two paths are available. The Basic Path covers Company Certificate Management. The Advanced Path adds one further Catena-X use case of the company’s choice — such as Product Carbon Footprint, Digital Product Passport, Traceability, Data Driven Quality, Demand and Capacity, Short-Term Supply (PURIS), or Circular Economy. Prior experience with data spaces is not required, and internal effort is designed to stay limited and predictable.

Remuneration based on verified results

Remuneration is milestone-based and tied to verified results: successful onboarding, implementation of the selected use case, and the first productive data exchange along that use case. The Basic Path provides €15,000 (net), and the Advanced Path adds a further €15,000 (net), up to €30,000 (net) in total. The amount reflects the milestone-based structure of the program rather than a reimbursement of a company’s actual implementation costs. For participants based outside Germany, it is recalculated using a purchasing-power index, as set out in the tender documents.

Who can take part

The study primarily addresses SMEs and Mid-Caps in automotive and adjacent industries, including machine building, that are not yet active in Catena-X. Companies of all sizes may apply, provided the program requirements are met. Eligibility extends to EU member states and to countries that have signed a Government Procurement Agreement with Germany, listed in the DSA Remuneration PPP Index.

Timeline and how to apply

All program activities and milestones are completed within 2026, and applications are open until the end of September 2026. Onboarding and the first productive data exchange need lead time, so an early start gives teams room to work through the steps with their onboarding partner.

Full details, participation requirements, and the application are available on the official program website. Questions on participation, eligibility, onboarding, remuneration, timelines, or study requirements can be directed to the program team.

Learn more and apply: data-space-accelerator.com

Contact: dsa@internationaldataspaces.org

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One participant captured the spirit of the discussion in a simple yet powerful statement:

If data spaces did not exist today, we would have to invent them.

The remark resonated because it reflected a broader realization shared across the roundtable. The conversation was not primarily about technology. Technical interoperability, standards, and federated architectures continue to advance at an impressive pace around the world. Rather, the discussion focused on a complementary challenge: How to create the governance, trust, and economic incentives necessary to make large-scale data sharing work in practice.

This challenge has become particularly urgent in the age of AI.

Artificial intelligence is dramatically increasing demand for high-quality, trustworthy, and context-rich data. At the same time, organizations are becoming more cautious about how their data is accessed, combined, used, and monetized. As AI systems become more powerful, questions about accountability, transparency, provenance, and control are moving from the margins to the center of the debate.

The rise of AI is therefore exposing a critical governance gap. While the world has made significant progress in developing technologies for data exchange, the governance mechanisms required for a functioning global data economy remain unresolved.

Who determines the conditions under which data can be used? How can compliance be ensured? How can data contributors retain meaningful control? And how can value generated from data be distributed fairly?

These questions are no longer theoretical – they are central to whether large-scale data sharing can work in practice.

This is where data spaces increasingly come into focus. Rather than serving merely as technical infrastructures for data sharing, they emerge as governance frameworks that combine interoperability, trust mechanisms, and shared rules for participation. Sovereignty is not an obstacle to data exchange – it is a prerequisite. Organizations are willing to share valuable data only when they can rely on clear rules, transparent governance, and enforceable usage conditions. Data spaces establish exactly these conditions, enabling responsible and scaled data sharing while preserving the control and trust that participants require.

The discussion also highlighted the importance of ensuring that the future data economy is truly global.

Participants stressed that global interoperability cannot be achieved through technology alone. It also requires governance approaches capable of bridging different regulatory environments, economic realities, and societal expectations.

This perspective became particularly visible in discussions about equitable participation in data-driven value creation. Several participants raised concerns about scenarios in which data from less economically mature regions contributes to global innovation without corresponding participation in the value generated. The challenge, therefore, is not only to enable global data flows, but also to ensure that trust, data sovereignty, and value creation can be shared more broadly across regions and communities.

Another area of strong consensus concerned business value.

Participants repeatedly emphasized that the long-term success of data spaces will depend on their ability to create tangible economic benefits. Organizations will not participate because a technology is elegant or because a standard exists. They will participate when data spaces help them solve real problems, unlock new opportunities, reduce costs, improve compliance, or create new forms of collaboration.

The question is therefore shifting from “How do we build data spaces?” to “What value do data spaces create?”

The Global Data Spaces Roundtable pointed to a broader conclusion: the future of the data economy will not be defined solely by our ability to move data across boundaries. It will be defined by our ability to establish trusted governance for how data is shared, used, and transformed into value.

As AI accelerates this transformation, data spaces are increasingly being viewed not merely as a technological concept, but as a governance framework for the next generation of the global data economy – one that can integrate emerging markets, respect sovereignty, and create equitable value for all participants.

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Horizontal data space that connects sector-specific data spaces

The strategic relevance lies in how this base layer is designed to support domain-specific extensions. The Geo Data Space is conceived as a horizontal data space: a spatial “ground layer” onto which sector-specific data spaces can be connected. Energy data, mobility data, or health-related aggregates are added as domain layers, all aligned through a shared geospatial reference model. This layered approach reduces fragmentation and enables interoperability across domains that traditionally operate in silos.

The initiative reflects a key principle of data spaces: sovereignty through structure. Data is not pooled indiscriminately. Instead, it is shared under clear licensing terms, aggregated where necessary, and governed through data space connectors. This approach highlights a strong focus on aggregation and depersonalization, particularly for sensitive domains such as mobility and health. Spatial aggregation is used as a practical mechanism to enable data sharing while respecting legal and ethical boundaries.

A multi-dimensional trust model

Another notable element is the planned integration of trust and data quality indicators into the connector layer. The initiative foresees a multi-dimensional trust model that allows users to assess data quality, reliability, and uncertainty. This responds directly to a growing challenge in data spaces: decision-makers and AI systems increasingly depend on understanding not just data access conditions, but also the quality and limitations of the data itself.

The Geo Data Space Germany is explicitly framed as a collaborative effort. While the initial focus is national, the ambition extends to Austria, Switzerland, and other European countries. The need for a common data model is considered a prerequisite for cross-border geospatial data spaces. Without shared semantics and structure, technical interoperability alone remains insufficient.

Lowering entry barriers for sector-specific ecosystems

Foundational data spaces such as the Geo Data Space can lower entry barriers for sector-specific ecosystems by providing trusted base data and governance structures upfront. They also illustrate how public-sector data, private expertise, and data space standards can converge in a bottom-up, scalable way. As Europe advances its data strategy, such horizontal data spaces may become critical enablers for federated, cross-domain data sharing.

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Participating companies receive structured onboarding into the Catena-X data space, with optional support from certified onboarding and service partners. No prior experience with data spaces is required. Remuneration is linked to two verified milestones: up to €15,000 for successful onboarding and productive implementation of Certificate Management, and up to an additional €15,000 for implementation of a second predefined Catena-X use case. In both cases, participating companies are required to take part in the accompanying survey.

Applications and full implementation must be completed within the year 2026.

SMEs form the backbone of the automotive supply chain. Their integration into data spaces is essential to achieve the critical mass required for a fully connected global network. Many SMEs still lack access to data spaces or the resources needed to invest in advanced digital solutions. At the same time, awareness of the potential of data spaces remains limited.

Without broad participation, key network effects cannot materialize

The Data Space Accelerator directly addresses this gap by supporting companies across the supply chain in digitizing their operations based on Catena-X standards—aligned with customer requirements and regulatory frameworks.

If Catena-X only works for OEMs and Tier-1 suppliers, it does not work for the industry as a whole. The ecosystem is already demonstrating its value, but the automotive value chain is built on SMEs. Without their participation, we cannot realize the industry-wide data collaboration that will benefit every level of the value chain. The Data Space Accelerator makes it easier and faster for them to get started, while addressing cost concerns by linking remuneration directly to results: real data exchange with real partners and real business value. This is not a pilot—it is a structured pathway into productive participation in the data space.

Hanno Focken, Managing Director Activation, Governance and Operations at Catena-X

The program addresses one of the most pressing structural challenges in the automotive industry. Seamless and trusted data exchange across the entire value chain is becoming essential—not only for regulatory compliance and supply chain resilience, but also for competitiveness. Data spaces enable companies to share data securely and sovereignly, forming a key building block for a resilient, AI-enabled, and sovereign European data economy.

Eight of the world’s ten largest automotive suppliers are already exchanging data via the Catena-X ecosystem, and leading OEMs are increasingly mandating participation in new supplier contracts. However, maximum efficiency and value can only be achieved when all tiers of the supply chain participate. Many SMEs—representing the majority of companies in the supply chain—continue to face barriers to entry, including limited resources, varying levels of digital maturity, high perceived upfront investments, and uncertainty about return on investment.

The Data Space Accelerator is designed to change this. Rather than subsidizing costs, the program rewards concrete results. Remuneration—up to €15,000 or up to €30,000 depending on the level of integration—is released once companies demonstrate verified productive data exchange with at least one supply chain partner. This makes it a practical entry point into the data space rather than a theoretical exercise.

How the program works

Certificate Management has been selected as the primary use case because it replaces the inefficient and repetitive process in which suppliers upload the same compliance certificates—such as ISO 9001 or IATF 16949—to multiple different customer portals.

Through Catena-X, certificates are uploaded once via a standardized interface, automatically shared with all relevant partners, and updated upon renewal. This significantly reduces administrative effort for SMEs while providing customers with a reliable, real-time view of compliance status.

Additional use cases address some of the industry’s most pressing operational and regulatory challenges: Product Carbon Footprint, Digital Product Passport, traceability, quality management, demand and capacity management, short-term supply management, and circular economy.

Companies are free to choose their implementation approach.

Proven results across the ecosystem

The use cases available through the Data Space Accelerator are already delivering measurable results for companies active in the Catena-X ecosystem. For SMEs in particular, shared Catena-X standards have reduced the effort required for due diligence reporting by a factor of 100— transforming a substantial administrative burden into a manageable and repeatable process.

In sustainability, a mid-sized supplier reports that PCF calculations can be performed three to five times more efficiently than with manual methods, saving more than €10,000 per calculation. In quality management, a leading OEM and its Tier-1 suppliers report detecting errors an average of four months earlier through standardised data exchange. In one case, an investigation into a critical electrical component—previously requiring inspection of 1.4 million vehicles—was narrowed down to just 14, highlighting the significant cost-saving potential of end-to-end data flows.

These results demonstrate that the business value of participation in the Catena-X ecosystem is tangible and can be realised in the short term. The Data Space Accelerator is specifically designed to help SMEs unlock this potential. This is supported by an Accenture study showing that 67% of companies expect new collaboration opportunities through data spaces, 54% anticipate new revenue potential, and 50% expect reduced costs and risks.

Part of a broader initiative

The Data Space Accelerator is part of the German government’s Manufacturing-X framework for scaling industrial data ecosystems. While the initial focus is on the automotive sector through Catena-X, the study is intended to generate transferable insights for data space initiatives in other industries, including chemicals and semiconductors.

Until now, the value of industrial data spaces has largely been based on theoretical models and isolated pilot projects. The Data Space Accelerator marks a fundamental shift in this approach—one that IDSA has been working towards for over ten years through its reference architecture. By integrating large numbers of Tier-n suppliers into the Catena-X ecosystem, it will generate robust empirical evidence of how network effects, data maturity and economic value interact in open data spaces. These insights will be directly transferable to data spaces in other industries now following Catena-X.

Lars Nagel, CEO of the International Data Spaces Association

Get involved

The Data Space Accelerator is open to automotive companies, particularly European SMEs looking to take their first step into the Catena-X data space. Companies can register now to participate in the study.

More information and registration: www.data-space-accelerator.com

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Background: why an update was necessary

Several external developments motivated the revision. International standardization has progressed, most notably through ISO/IEC 20151 on data space concepts, providing a stable terminology and reference model. In parallel, open specifications and reference implementations have matured within the Eclipse Foundation, moving core building blocks from experimental to operational quality. Finally, real deployments have generated empirical feedback on governance models that scale and those that do not. The earlier Rulebook increasingly reflected an earlier phase of the ecosystem. Release 2026-1 updates this baseline and reconnects the document more explicitly to the IDSA Manifesto principles of participant autonomy, decentralization, and trust.

Decentralization as a default system property

One of the most consequential changes is the explicit treatment of decentralization as the default architectural assumption. The Rulebook now frames participant autonomy as a primary requirement, rather than an optional design preference. While alternative patterns are acknowledged, decentralized interaction models are positioned as the reference case against which deviations must be justified.

This clarification matters because decentralization affects identity management, policy enforcement, catalogues, and governance responsibilities. By making this assumption explicit, the Rulebook reduces interpretive flexibility that previously allowed centralized platforms to be labelled as data spaces without meeting the underlying intent.

Trust as a continuous, runtime concern

Another structural refinement concerns trust. Earlier interpretations often treated trust as a static onboarding decision. The new Rulebook reframes trust as a continuous, context-dependent process that must be evaluated and maintained during runtime. This includes clearer guidance on claims, policies, and mechanisms for reconciling them in concrete interactions. Trust is no longer implied by membership alone but is operationalized through verifiable behavior and enforceable agreements. For implementers, this shifts attention from certification events to ongoing monitoring and policy evaluation, with direct implications for connector design and governance processes.

Clear separation of mandatory and recommended requirements

Release 2026-1 introduces a more disciplined distinction between mandatory requirements and recommended practices. This separation provides governance bodies and implementers with unambiguous guidance on what is required for conformance and what remains discretionary. The absence of such clarity in earlier versions often led to over-engineering or inconsistent interpretations across data spaces. The revised structure supports proportional implementation, allowing organizations to priorities compliance efforts while still benefiting from best-practice guidance where appropriate.

Release 2026-1 maps more directly to ISO/IEC 20151, the Dataspace Protocol, the Decentralized Claims Protocol, and Eclipse Dataspace Components. This alignment reduces conceptual translation work for architects and developers and makes the Rulebook easier to integrate into existing design and compliance processes.

Functional requirements, not architecture

A deliberate clarification concerns scope. The Rulebook defines functional requirements for governance and participation, not architectural prescriptions. Architectural guidance remains the responsibility of the Reference Architecture Model (RAM). This distinction is reinforced in the 2026-1 release to avoid conflating governance obligations with technical design choices. For practitioners, this means the Rulebook should be used to frame decisions about roles, trust models, and obligations, while concrete system architectures are derived elsewhere. The separation preserves architectural flexibility without weakening normative consistency.

Forward-looking extensions: AI agents in data spaces

The inclusion of a dedicated chapter on AI agents signals an expansion of the Rulebook’s conceptual horizon. The chapter establishes initial requirements and considerations for autonomous or semi-autonomous actors participating in data spaces. This acknowledges emerging usage patterns where AI systems negotiate access, evaluate policies, or act on behalf of participants. By addressing this early, the Rulebook provides a foundation for future refinement without delaying current implementations.

Key takeaways for the data spaces community

The IDSA Rulebook 2026-1 delivers clarity of direction. Decentralization is treated as a default condition, trust as an operational process, and requirements as explicitly tiered. The document aligns more tightly with standards and tooling that organizations already use, while preserving a clear separation between governance requirements and architectural design. For working groups, implementers, and decision-makers, the Rulebook is best approached as a basis for concrete governance and participation decisions, supported by technical specifications elsewhere in the IDSA Knowledge Base. Used in this way, it provides a stable foundation for interoperable and trustworthy data spaces.

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