That core is Eclipse Tractus-X, the open source project behind Catena-X, hosted at the Eclipse Foundation. It counts more than 330 active contributors and more than 79 repositories, ships quarterly releases and is licensed under Apache 2.0. The code implements the Dataspace Protocol (DSP) and the Decentralized Claims Protocol (DCP). It is the second largest open source project at the Eclipse Foundation.

Tractus-X is an operating system for data spaces. It is a core, built and maintained by a community, that contains the building blocks many data ecosystems need. Among them are trusted data sharing, digital identities, interoperability services, governance foundations and implementations of open standards. On top of this shared foundation, each industry, region or project adds its own capabilities, services and applications.

Tractus-X has a concept for this. It is called KIT, short for Keep It Together. An initiative builds its differentiating functionality as a KIT, contributes it back and lets others reuse, improve and extend it. Chem-X and Construct-X, launched this year with the Catena-X Association, show how industry variants grow on the common core. The intention is that national, European and international data spaces can do the same.

The Accelerator started in September 2026, runs for three years and is funded by the German Federal Ministry for Economic Affairs and Energy. ARENA2036 leads the programme and brings its demonstration environment. The Catena-X Association takes care of community operations and internationalisation. Fraunhofer ISST covers technology and governance.IDSA leads stakeholder management and supports internationalisation. The purpose of the programme is to grow the community behind the core, into more industries, more countries and more developers and contributors. Four contributions are open now.

First, tell the project what is missing. Communication in the programme runs in both directions. Data space initiatives, open source projects, industry associations and public bodies that have looked at Tractus-X and decided against it hold information the partners need. A frank reply about blockers, gaps or licensing concerns counts as a contribution.

Second, build your KIT and bring it in. Many organisations have built connectors, identity services or federated services for their own data spaces. Some of these could serve everyone who builds on Tractus-X. Which components belong in the shared core and which stay industry specific is decided together with the community, and that decision needs the components on the table. Development happens in the community, and the community is whoever shows up.

Third, help answer the financing question. Many organisations use a shared open source component. Few of them maintain it. The partners will work on a model, possibly a foundation or an association, to keep Tractus-X maintained beyond the funding period, and the community has been invited to contribute ideas. Organisations whose own data spaces depend on the core have the strongest reason to send them.

Fourth, join the onboarding programme, as mentee or as mentor. Guided classes of six months are planned that take a newcomer from first contact to a first use case, with experienced community members alongside. Until the classes start, interested organisations receive individual sessions on setting up a data space, federated services, Eclipse Dataspace Components (EDC) connectors or identities. Joining early means more individual attention.

The invitation extends across borders. The Accelerator connects with the Data Spaces Support Centre (DSSC), the international IMX community and partners in further countries, and IDSA will organise events around the globe to involve partners, supporters and contributors wherever they are. Two dates are set. On 6 November a hackathon works for one day on a new portal for Tractus-X. On 3 and 4 December the seventh Tractus-X Community Days bring users and contributors together around current developments, new use cases and the direction of the project.

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Why a training center

The venue itself said something. The organizers were explicit that the digital transformation of industry depends as much on trained people and on cooperation between companies, academia and research as on technology. SENAI’s director opened by recalling the institution’s long role in building a skilled workforce for the State of São Paulo. One example stayed with me. SENAI’s mobile units take training and technology to regions with no permanent site. The organizers drew the parallel themselves. In digital transformation, too, the task is to make knowledge and technology reach the places where industry actually operates.

An ecosystem with many builders

Before my keynote I listened to how the Brazilian community is organizing itself. Instituto Eldorado presented its projects in software and data spaces. ABINC’s president described the association’s sectoral verticals, its dialogue with federal ministries and agencies, and its connections with VDMA, Gaia-X and IDSA.

One thing stood out to me over the following days. Much of Brazilian industry organizes itself through associations. I spent a day with the team at Instituto Eldorado that works on data spaces, talking through their projects and where IDSA can support them. I met Brasscom, which represents the country’s information and communication technology companies, in a hybrid session with around thirty people joining online. And I met the board of ABES, the Brazilian Association of Software Companies, which represents more than 2,000 companies. I had not expected the depth of knowledge about data spaces that I found there. Through a handful of associations, the conversation reaches thousands of companies at once.

That matters because a data space runs on shared rules, standards, interoperability and trust mechanisms that many participants have to agree on. No single organization can provide all of them. What I heard in São Paulo matched the principle at the core of the IDS Reference Architecture Model (IDS-RAM) and the IDSA Rulebook. Participants cooperate and exchange data while keeping control over how their data is used.

Data that carries value

Two use case talks stayed with me. The lead of ABINC’s Open Industry Committee made the point that data has to be worth sharing before it is shared. Machines, processes and logistics chains produce signals continuously. Turned into reliable, contextualized and interoperable information, the same data first used to track productivity can later feed artificial intelligence, advanced maintenance, traceability, the Digital Product Passport and data spaces. His advice was pragmatic. Start with one machine or one use case with a measurable economic result, then extend from there.

ABINC’s vice president showed decarbonization projects supported by data spaces. Carbon indicators depend on information held by several organizations in one production chain, and for audits or emissions calculations they need to be verifiable across company boundaries. This is where a data space does work that no company can do alone, while each participant keeps governance over its own data.

What Brazil can do with Europe’s experience

In my keynote I traced how data spaces developed in Europe, what we learned and how the European data economy is taking shape. My message to the room was that Brazil does not need to walk the same road. The standards, technologies, lessons and known difficulties are available, and they can be adapted to Brazil’s own industrial characteristics. The path the organizers described fits what I have seen work elsewhere. Begin with concrete use cases that show economic value, connect them, widen the circle of participants and then scale.

The organizers summed up the day in a sequence of eight words. Paper, digitalization, data, information, intelligence, sharing, data spaces, data economy. Each step is a transformation in its own right. Digitalizing a process is more than replacing paper with a screen. Sharing data is more than uploading it to a platform. At every step, information needs context, quality, interoperability, security and clear rules of use.

Brazil has the industrial base, the production chains, the training and research institutions and a growing technology ecosystem. The next step, as the organizers put it, is to connect these elements. I left São Paulo convinced that this connection is already under way.

This article draws on a report published by Open Industry, an initiative of the Brazilian Association of Internet of Things (ABINC): Economia de Dados — do papel aos Data Spaces, no SENAI Vila Mariana — Open Industry

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Neither operates well without the other

Data labs hold no commercial interest in the data they prepare. That neutrality defines them. They are not repositories and they are not AI development platforms. They act as trusted intermediaries and invest considerable capability in making that data useful. They bring together technological capabilities for data management, curation and generation with the regulatory orientation that AI training increasingly requires under the EU Artificial Intelligence Act (EU AI Act) and related frameworks.

The wider ecosystem has several parts. AI Factories supply large-scale compute. Testing and Experimentation Facilities (TEFs) generate experimental and validation data. European Digital Innovation Hubs (EDIHs) are user-facing contact points. They diagnose needs and direct organizations toward specialized services. Data labs curate and standardize what moves between these structures and AI developers.

Four-layer pipeline

IDSA set out the same arrangement in its position paper Data Spaces and AI. The paper describes a four-layer pipeline. Data spaces source the data. Data labs prepare it in secure processing environments. AI Factories train models on the result. TEFs validate the trained model before it is deployed. The paper calls data labs the “last mile” intermediary in that chain. They federate raw data without extracting it and apply curation, pseudonymization and synthetic data generation.

The primary source of real-world, cross-organizational data for a data lab is the data space. Governed access, consent management and usage policy enforcement take place there, before a single dataset reaches the lab. The value chain runs from secure data access through preparation to AI development and deployment. Data spaces anchor the first stage.

Ecosystem analysis points to fragmented governance models, trust deficits and insufficient incentives for data sharing. Those challenges are concentrated at that first stage. Addressing them requires standardized roles, contractual frameworks and an interoperable Connector architecture of the kind the IDS Reference Architecture Model (IDS-RAM), the IDSA Rulebook and the Dataspace Protocol establish. A data lab inherits the quality of the governance that precedes it.

A framework already exists

The next phase of data lab development calls for reference frameworks, governance protocols and formal interoperability with data spaces. None of that has to start from a blank page. The standards IDSA maintains already cover the four layers of interoperability, from the technical and semantic to the organizational and legal. As data labs scale under the AI Continent Action Plan and the European Data Union Strategy, alignment with those standards becomes part of the structure. Trust and interoperability are conditions, not features added later. They decide whether the data reaching an AI developer is fit for use at all.

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Why data spaces could be a gamechanger for the Global South

Across many resource-constrained settings in the Global South, limited data and statistical coverage hold back political decision-making, business insights, and data-driven innovation. Data spaces can help solve that. As a brownfield solution, they don’t replace legacy systems but build on what already exists, keeping entry costs low. Its interoperability layer makes it easy to integrate any type of stakeholder at very little cost but great benefit: each contributes much-needed data to build a more complete and reliable picture of the national economic ecosystem. The result is better data for sharper policymaking, stronger business insights, and innovations like AI applications or digital twins.

However, none of this happens automatically. For data spaces to deliver on this potential in the Global South, the following insights should guide the design.

Success starts with a committed public sector anchor

While many data spaces in Europe emerge from associations of leading enterprises, such coalitions are often absent or too thin in the Global South. A public sector entity, such as a ministry, can instead take on this anchor role. Doing so lends legitimacy to the initiative, improves its long-term sustainability, and strengthens the business case, since public sector data is often highly valuable in its own right. But this responsibility should not be taken on lightly: the anchor institution needs a thorough understanding of how data spaces function and what they can deliver. At the same time, it must build high-level political alignment with other institutions to move the design and development process forward together.

Building an inclusive community sustains momentum

A data space flourishes around its community. In the Global South, that community should reflect the reality of the (data) ecosystem and include small and big players alike, spanning very different degrees of digital maturity and digital capabilities. Accommodating precisely this diversity is what allows a data space to attract the broad base of participants needed to gain traction and sustain momentum over time. This starts with governance, not technology: an inclusive governance setup, one that gives smaller or less advanced actors a genuine voice alongside larger, more established ones, is what makes a data space accessible and attractive in the first place. Technology can then reinforce this inclusivity. In-built AI tools, for instance, can make data analytics accessible even to those without technical expertise. And targeted data and AI training can help build the skills participants need to engage meaningfully and benefit from what the data space has to offer.

A sound financing model secures the future

Public sector leadership and a diverse, engaged community are essential — but neither is enough if the data space cannot sustain itself financially over time. In a resource-constrained context, sustainability cannot be left to a few well-resourced association members. Too often, initial funding covers the “capex”, the upfront investment needed to build the infrastructure, but overlooks the “opex”, the ongoing running costs of keeping it operational: maintenance, staffing, updates, support. Governance structures and data sharing agreements need to be designed with this in mind from day one, building in cost-recovery mechanisms together with the community. Marketplace transaction fees, subscription models, or public-private cost-sharing need to reflect the needs and expectations of the ecosystem in order to sustain day-to-day operations well beyond the initial investment.

Ultimately, our two years of implementation in Côte d’Ivoire and Senegal point to a single overarching takeaway. Every design choice — from the public sector anchor to the sustainability model — should be guided by this principle: the data space exists to serve people, not the other way around. Technology remains the means; people are always the end.

This work was made possible by the “Data Governance in Africa” Team Europe Action, co-financed by the European Commission and five EU Member States. The authors gratefully acknowledge the constructive collaboration and invaluable contributions of the Ministry of Digital Transformation and Technological Innovation (MTNIT), the Ministry of Agriculture and Rural Development (MINADERPV) as well as the Tony Blair Institute and DigiFemmes in Côte d’Ivoire throughout the course of this project.

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Standardization has become the critical next step for data spaces. Across the European Union and internationally, technical committees and regulatory bodies are developing the shared rules that will enable interoperable, sovereign, and trusted data sharing at scale. This article traces the key standardization efforts underway, explains what they cover, and shows how IDSA connects implementation experience with the formal standard-setting process.

The mandate for common standards

Data spaces depend on shared rules. Without agreed specifications for identity verification, data discovery, contract negotiation, and access control, interoperability between organizations and across sectors remains aspirational. That is the argument driving a growing wave of standardization activity, as technical committees at European and international level translate the design principles established by IDSA and its partners into certifiable, broadly applicable standards.

The EU Data Act, the Data Governance Act (DGA), and the EU AI Act each introduce requirements for interoperability, trust, and technical infrastructure that data spaces are well-positioned to meet, but only if common technical foundations exist. Standardization is the mechanism that turns regulatory expectation into operational reality.

A European effort in full swing

The most consequential near-term development in Europe is the European Commission’s Standardization Request M/614, issued under Article 33 of the Data Act. The request tasks CEN, CENELEC, and ETSI with producing seven standardization deliverables by 2027, covering trusted data transactions, semantic asset frameworks, data catalogue implementation, internal data governance quality, and a maturity model for Common European Data Spaces. Two of the resulting standards are intended for citation in the Official Journal of the European Union, giving them a direct role in legal compliance.

The committee responsible for much of this work is CEN-CENELEC Joint Technical Committee 25 (JTC 25), titled “Data Management, Dataspaces, Cloud and Edge.” Launched in September 2024, JTC 25 is developing the Trusted Data Transactions (TDT) standard series in parallel with work on data quality, trust frameworks, and interoperability architecture. The first part of the harmonized TDT standard is in publication. Parts two and three are advancing through approval and drafting stages respectively.

The European Telecommunications Standards Institute (ETSI) is contributing through its Technical Committee on Data (TC DATA), with a focus on data discovery mechanisms, metadata exchange, and semantic interoperability, directly supporting the Article 33 standardization request in collaboration with JTC 25.

International dimensions

The standardization effort extends well beyond Europe. Within ISO/IEC Joint Technical Committee 1, Subcommittee 38 (ISO/IEC JTC 1/SC38), which covers cloud computing and distributed platforms, several data space standards are advancing. ISO/IEC DIS 20151-1, which defines core data space concepts and characteristics, is expected to be published later this year.

The ISO/IEC DIS 26450 Dataspace Protocol (DSP) and ISO/IEC DIS 26451 Decentralized Claims Protocol (DCP), both derived from Eclipse Dataspace Working Group specifications that are based on IDSA’s work, are under DIS ballot with publication also anticipated in late 2026. Together, DSP and DCP form an interoperability stack covering communication, identity management, and trust verification.

Beyond ISO, the IEEE P3800 series addresses data trading systems and data space discovery protocols, while IEEE P1988 is developing a data space architecture framework integrating privacy technologies. Japan and China are both advancing domestic frameworks aligned with these international efforts, and initiatives such as the Japan Data Space Alliance and China’s SAC/TC609 technical committee reflect growing momentum outside Europe.

IDSA’s role in the process

IDSA occupies a specific position in this landscape: it is both a contributor to standards and a connector between industry implementation and the formal standardization process. The IDS Reference Architecture Model (IDS-RAM), the IDSA Rulebook, and the Dataspace Protocol served as foundational inputs to ISO/IEC 20151-1 and the Eclipse-derived standards now progressing through ISO. IDSA’s engagement with JTC 25 and ISO/IEC JTC 1/SC38 ensures that the experience of real-world deployment informs the specifications being written.

At the same time, IDSA has identified persistent gaps in the current standardization landscape. Metadata governance, usage control policies, ontology alignment, and cross-sector interoperability remain areas where further formal work is needed. These gaps inform the agenda of CEN-CENELEC JTC 25 and are being addressed through new preliminary work items covering data quality and trust frameworks.

Toward operational standards

The convergence of IDSA’s work with emerging ISO, CEN-CENELEC, and IEEE standards marks a transition from specification to norm. Organizations building data sharing infrastructure today are increasingly doing so against a backdrop of standards that are approaching publication.

The period ahead will determine how well those standards serve the practical needs of deployment, and IDSA’s continued involvement in that process, alongside the broader stakeholder community convened through initiatives like the Data Space Adoption Forum, will be central to the answer.

On 10 September 2026, 9:00–10:00 am CET, IDSA hosts a Tech Talk introducing the second version of its Position Paper “Data Spaces Standardization Landscape,” mapping how the IDS Reference Architecture Model (IDS-RAM), the IDSA Rulebook and the Dataspace Protocol (DSP) align with key standardization bodies and the EU regulatory framework.

The session also looks beyond Europe at emerging activity in regions such as Japan and China, and closes with an open call for community contributions to shape Version 3.0. Register here.

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The Data Space Adoption Forum

The DSAF is a long-term, cross-ecosystem coordination body. It operates as an IDSA Working Group and brings together Cloud Service Providers (CSPs), Managed Service Providers (MSPs), data space operators, trust framework providers, and industry partners to co-develop the services and infrastructure required for scalable data space participation.

The Adoption Forum’s central contribution is the managed service model for data space connectivity. This includes the technical foundation provided by Eclipse Dataspace Components Virtual (EDC-V), governance alignment across trust frameworks, and the shared go-to-market foundations that allow multiple service providers to offer compatible onboarding services across multiple data spaces.

The DSAF is domain-agnostic. Its outputs are designed to be reusable across ecosystems. When the Adoption Forum defines a shared onboarding architecture or a federated certification model, that work applies to Catena-X, to Eona-X, to SCSN, and to any other data space that adopts compatible standards. This is what makes the Forum’s work structurally important for the data spaces ecosystem as a whole.

The Data Space Accelerator

The Data Space Accelerator is a programme currently operating within the Catena-X automotive data space. It is an incentive and study programme designed to bring a significant number of companies, primarily small and medium-sized enterprises (SMEs), into active data space participation within a defined timeframe.

The Accelerator supports participating companies through the onboarding process and collects structured data on what it actually takes for an SME to join a data space, what barriers they encounter, what business value they realize, and what the economics of participation look like in practice. This is deliberately framed as a study, not simply a subsidy. The insights generated will provide the first statistically relevant evidence base on SME adoption at scale.

The DSAF builds the infrastructure and the service model. The Accelerator creates immediate opportunity in Catena-X: companies are actively looking to onboard, there is structured support for the process. Catena-X’s upcoming Neptune release will provide the technical environment to deliver managed services. CSPs with EDC-V deployments ready for production can enter this market as the program operates.

The DSAF’s independent value

The DSAF’s coordination work means that capabilities built for Catena-X are designed from the outset to extend to other ecosystems. Learnings from the Accelerator, on what SMEs need, which service models work, feed back into the Adoption Forum and improve the shared foundations available to all members.

The Adoption Forum does not depend on any single deployment program to fulfil its purpose. Its value lies in the sustained, neutral coordination it provides across ecosystems, trust frameworks, and service provider communities. When the Adoption Forum defines shared onboarding architecture or aligns governance models, that work is available to all participants across all compatible data spaces.

What this means for Cloud and Managed Service Providers

CSPs and MSPs are positioned as the primary distribution channel for data space services. They already have direct relationships with the SMEs that data spaces need to reach. They have the infrastructure expertise to operate managed services. They have the sales and support capability to bring customers onto new services at scale.

The DSAF provides the neutral, cross-ecosystem coordination that makes investment in this capability commercially viable across multiple sectors and data spaces. Participation in the Adoption Forum gives access to shared technical foundations, alignment with trust and governance frameworks, joint go-to-market planning, and a community of operators and data space authorities who need the services that CSPs can provide.

Contact: DSAF@internationaldataspaces.org | data-space-adoption-forum.org

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EDC-V, the Eclipse Dataspace Components Virtual connector project, addresses this directly.

What EDC-V is

EDC-V is a multi-tenant extension of the EDC open-source project, maintained under the Eclipse Foundation and available under the Apache 2.0 licence. It is designed to allow a single operator, such as a cloud or managed service provider, to run data space connector infrastructure on behalf of many participants simultaneously.

In practical terms, EDC-V is the infrastructure layer that makes Connector-as-a-Service possible. A CSP or MSP deploys one EDC-V instance and can onboard many participant companies into a data space through that single deployment. Each participant gets an isolated, secure tenant within the shared infrastructure. They can manage their own assets, policies, and contracts through a participant-facing interface, without having access to any other participant’s data or configuration.

How the architecture works

EDC-V organizes access around a set of defined roles. The Operator role covers infrastructure setup and configuration. The Admin role is reserved for initial setup and emergency access. The Provisioning System role handles the automated creation and management of participant tenants. The Participant role represents each individual company in the data space.

All administration through EDC-V uses OAuth 2.0-based authentication. When a new company is onboarded, the provisioning system creates a participant context, generates identity credentials, and registers the participant with the relevant data space trust infrastructure. This process is designed to be automated and fast.

The participant-facing Administration APIs allow each company to manage its own data offerings, access policies, contracts, and verifiable credentials. Strict isolation between participant contexts is enforced by the architecture. A participant can only access its own data; the provisioning system cannot manipulate participant-owned data; and the admin role is not used for day-to-day operations.

This separation between operational roles and participant access is what makes multi-tenant deployment viable for production use.

Benefits for cloud and managed service providers

Without EDC-V, a CSP that wanted to offer data space connectivity services would need to deploy and manage a separate connector instance for each customer. At small scale, this is manageable. At the scale required for meaningful supply chain adoption, it becomes operationally and commercially unviable.

EDC-V turns data space connectivity into something that can be offered as a standard managed service. The provider deploys and operates the shared infrastructure. Customers are onboarded through an automated provisioning process. Compliance and certification are managed at the infrastructure level, not at the individual customer level. The result is a service that a CSP can deliver at scale, with predictable operational costs, through existing customer relationships.

This is the model that CSPs and MSPs already apply successfully in other domains. EDC-V brings the same operating model to data space participation.

The current status

The EDC-V architecture has been defined and developed. Infrastructure readiness for deployment at scale is targeted for summer 2026. The Catena-X Neptune release, which will formally support EDC-V-based solutions, is planned for September 2026. Managed service providers are expected to have their first solutions ready for commercial deployment and SME onboarding before the end of 2026.

The source code is available at https://github.com/Metaform/edc-v.

An open foundation for multiple data spaces

EDC-V is designed to serve multiple data space ecosystems, not just automotive. The architecture is domain-agnostic. A CSP that deploys EDC-V to serve Catena-X participants can, through the same infrastructure, extend services to participants in other data spaces that adopt compatible governance and trust frameworks.

This is consistent with the Data Space Adoption Forum’s broader objective: to build a shared, open foundation that multiple ecosystems can adopt. EDC-V is that foundation. It is the technical layer on which the managed service model depends.

For cloud and managed service providers, this means that investing in EDC-V-based capabilities is a position in the infrastructure layer of a growing family of data spaces.

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This is the problem the Data Space Adoption Forum (DSAF) was set up to solve. 

What the Data Space Adoption Forum is 

The DSAF is an IDSA Working Group, convened by the International Data Spaces Association (IDSA) as a neutral, non-commercial coordination ground. It brings together data space operators, cloud and managed service providers (CSPs and MSPs), trust framework providers, and industry representatives to co-develop the services and distribution mechanisms that make data space participation accessible at scale. 

IDSA does not deliver data space services itself. Its role is to provide the neutral coordination layer where players with complementary capabilities can align, co-develop, and agree on shared technical foundations without the risk of one participant’s commercial interests distorting the outcome. 

The founding partners of the DSAF include IDSA, Gaia-X, CISPE (Cloud Infrastructure Services Providers in Europe), leading automotive and manufacturing ecosystems, and cloud and managed service providers. This combination of standards bodies, infrastructure providers, and data space operators reflects the full chain required to move from technical specification to market-ready service. 

The problem to solve  

Industrial data spaces need to scale to millions of participating companies across global supply chains. The current onboarding model cannot support this. Each participant today deploys its own connector stack. Each company procures, configures, and maintains its own infrastructure. Compliance with data space governance requirements must be established from scratch for each organization. The time and cost involved means that, in practice, only large companies can participate. 

The DSAF works on an alternative model: participation as a managed service. Under this model, a cloud or managed service provider hosts the data space infrastructure on behalf of its customers. An SME subscribes to a data space onboarding service through a provider it already works with, accesses a compliant and certified environment, and participates in the data space without needing to build or maintain anything itself. 

This shifts data space participation from a capital-intensive IT project to a subscription service. It is the same shift that cloud computing made for general enterprise IT, applied to trusted data sharing. 

How the forum works 

Work inside the DSAF is organized into parallel workstreams that progress solutions from definition through to implementation. These workstreams cover joint technical coordination, shared onboarding foundations, and go-to-market planning. Solutions are not only specified collectively; they are owned and deployed by the participating actors. This means the Forum produces real implementations in operational environments, not specifications that sit on a shelf. 

IDSA coordinates the workstreams and ensures alignment across the participating organizations. The Forum operates under IDSA’s governance model, which guarantees neutrality and ensures that outputs can be adopted across multiple data space ecosystems. 

Where things stand 

The DSAF launched in February 2026. The first production-ready managed service infrastructure is on track for availability by the end of 2026. 

This is the right moment for cloud and managed service providers to engage. The technical foundation is being finalized. The first production deployment is scoped and under development, anchored in a real and large-scale use case in the automotive sector. Providers who join now can shape the service model, participate in the first commercial deployments, and position themselves in a distribution channel that will serve all data spaces, across all domains. 

How to get involved 

There are three ways to participate in the DSAF. The first is full IDSA membership, which gives access to all IDSA Working Groups including the Forum, voting rights on direction, and full ecosystem access. The second is direct sponsorship, which provides visibility and participation rights in Forum activities and joint go-to-market. The third is a subscription model, which gives seat-based access to Forum workstreams and materials. 

For cloud and managed service providers, the Forum is a direct route into a market that their existing customer relationships already position them to serve. The technical work is progressing. The deployment window is opening. The question is whether your organization is in the room. 

Contact: DSAF@internationaldataspaces.org | data-space-adoption-forum.org   

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An academic partner with reach

IIT Dharwad is an ambitious institution, it operates within a network that spans established industry partners, government bodies and other academic institutions across India. That connectivity is part of what makes it a useful anchor for this kind of initiative. Building a data space ecosystem is as much an organisational challenge as a technical one, and it requires someone who can convene people across sectors over a sustained period.

What is actually planned

The MoU sets out a practical work programme that covers several concrete areas. IDSA and IIT Dharwad will develop business-driven use cases across sectors including telecommunications, automotive, manufacturing, agriculture, smart mobility and railways, with telecommunications and automotive as the initial focus. Alongside that, the partners will run joint projects designed to demonstrate the economic value of data spaces in practice.

Building broader awareness is also part of the plan. That includes outreach to organisations across India that are considering data sharing approaches, as well as support for connecting corporates, government agencies and industry bodies to accelerate implementation.

The collaboration will also produce joint publications: white papers, case studies, market research and technical reports. A national data space think tank is a stated goal, bringing together universities, public-private organisations and government bodies.

The longer-term aim is an IDSA Hub and a dedicated Competence Centre in India. The India Dataspace Task Force, to be established through an upcoming bootcamp workshop, will set the priorities and sequence for what gets built and when.

The people involved

The MoU was signed by Lars Nagel, CEO of IDSA, and Thorsten Hülsmann, CFO of IDSA, together with Dr. Kalyan Bhattacharjee and Dr. Koteswararao Kondepu from IIT Dharwad. Shanawaz Sheik (IDSA Ambassador), Dr.-Ing. Ahmad J. Rusumdar, Christoph Mertens and Anna Derevianko were instrumental in bringing the collaboration to this point.

“India has the scale, the sectors and the institutional appetite to build something significant here. What has been missing is a structured way to connect those pieces. This partnership is a starting point for that.”
Christoph Mertens, Head of Adoption | IDSA

Where things stand

India’s data space ecosystem is taking shape. Early institutional commitment is building, and the partnership between IDSA and IIT Dharwad adds a formal foundation to that momentum. The bootcamp workshop is the next concrete step, and the task force it produces will set the direction for what follows.

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The governance challenge agentic AI creates 

Traditional access control was built for human users and fixed service accounts. It does not capture an autonomous agent whose model, tools and purpose can change between sessions. Agentic participation creates six distinct pressures on data space governance: speed (agents issue far more requests than humans and act without manual approval), ambiguity (natural-language instructions can be underspecified), composability (a permitted first step can lead to an impermissible later one), malleability (behavior shifts with model updates or adversarial inputs), epistemic risk (agents can produce plausible but false outputs) and a widened attack surface (prompt injection, data exfiltration and agent impersonation all become possible when agents reach external systems). 

These six pressures share one root. Autonomous action that can cross organizational boundaries must stay bound to an accountable participant, an explicit purpose, a policy decision and an auditable record — even when the agent is fast, adaptive and only partly predictable. 

Delegated identity solves the attribution problem 

The paper’s solution is the Delegated Agent Participant ID: a Verifiable Credential that links an executing agent back to its legally liable organization. Before any data transaction begins, the data space Connector verifies six elements within it: the organization (as a cryptographically verified Decentralized Identifier of the legal entity), the agent instance (a transient runtime identifier for the specific session), the model powering the agent’s reasoning, the tool registry profile listing the external capabilities the agent may use, the purpose scope (a machine-readable declaration of intent that must align with the provider’s ODRL usage policies) and the assurance level (the agent’s trust tier backed by testing certificates or accreditations). 

This structure separates three identity layers that have different lifecycles and different accountability owners. The legal participant identity is static and permanently anchored to a corporate entity. The agent identity is transient, tied to a specific task and session. The tool and service identity is tied to version control and software development. Keeping these three layers distinct is what makes the whole system auditable. 

When an authorized agent tasks a secondary agent across organizational boundaries, the data space governs this through cascading delegation. The secondary agent inherits the boundaries of the primary agent’s credential — specifically its purpose scope and assurance level. Usage policies, sandboxing constraints and legal liability flow down the chain. No sub-agent can reach data or take actions the primary organization was not authorized to handle. 

Agent roles in practice 

The paper describes seven distinct agent roles that emerge in data space operation. A discovery agent continuously searches the catalogue for datasets and offerings that match a standing need. A data-gap analysis agent compares an organization’s requirements against what is available and identifies what is worth acquiring. A negotiation agent conducts the contracting process, proposing and counter-proposing usage policies, pricing and terms through the data space’s negotiation flow, with a human approving the final agreement. A cataloguing and curation agent enriches metadata and keeps the knowledge graph current. A conformity and compliance agent checks offerings and policies against verifiable credentials and the regulatory context. A provenance and observability agent reconstructs lineage and monitors usage. A matchmaking agent pairs supply and demand across the ecosystem. 

These roles compose in practice. A typical workflow chains them: a scout agent finds candidate partners, a gap-analysis agent confirms the data closes a real need, a conformity agent verifies the offering’s credentials and a negotiation agent settles terms. Each acts through standard data space interfaces under its delegated identity. 

Evidence from live pilots 

The paper documents pilots that show this working in practice. NEC and EverySense Japan demonstrated automated negotiation of data-trade terms, where two agents — one representing a data buyer, one a data seller — negotiated terms across three dimensions (data characteristics, sharing conditions and price) without human intervention, reaching agreement in around 80 seconds with approximately a 95% automated agreement rate. The terms negotiated by the agents are explicit and auditable; the conclusion of any legally binding contract remains with the participating parties. 

The RoX consortium, a German initiative for data-ecosystem infrastructure in AI-based robotics, uses data space technology as the foundation for sovereign exchange of machine, process and quality data across competing organizations. Its outlook is explicitly agentic: the same governed data supply that feeds robotic applications is intended to feed an agentic layer that automates and orchestrates tasks under the data space’s existing controls. 

The trust formula 

The paper proposes a trust formula as a practical design heuristic: the model may propose, the knowledge engine checks, the policy engine authorizes, the data space records and the human steward remains accountable. The formula is multiplicative. If any factor is absent, trust collapses. A fast AI system without policy is not trustworthy. A policy system without semantics cannot decide correctly. A knowledge base without verification can turn errors into executable rules. A data space provides the environment in which these factors can be assembled and enforced together. 

The path ahead 

The Dataspace Protocol (DSP) — currently undergoing international standardization as ISO/IEC DIS 26450 — separates the control plane (identity, access negotiation, policy decisions) from the data plane (where exchanges happen and where agent-specific protocols such as MCP and A2A can operate). Keeping the two separate is what allows agent tooling to evolve on the data plane without loosening the governance that holds on the control plane. 

Several items remain open. There is not yet an agreed specification for how agent delegation is declared, how a model and its tests are published as governed assets, or how cascading delegation is governed at scale across organizations. The paper’s task force is carrying this work forward, including into the IDSA Rulebook’s AI Agents chapter. An international testbed for data spaces and AI, positioned within the EU Data Union Strategy and Apply AI Strategy, is planned as the environment in which safety, compliance and cross-border interoperability are validated before capabilities reach production. 

The paper is available at Data Spaces and AI: Trustworthy Agentic Participation in Data Spaces. Organizations working on AI integration or data space adoption are invited to engage with the IDSA task force and contribute pilots, infrastructure and standards feedback through the community. 

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