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.

The post IDSA Rulebook 2026-1: Structural clarifications for operational data spaces appeared first on International Data Spaces.

These questions are now the real bottleneck for AI at scale. And they are precisely where data spaces come into focus.

What data spaces already provide

Long before AI became a board‑level priority, data spaces were designed to address precisely these governance challenges. Through shared standards and specifications, they establish identity and participation rules, define machine‑readable usage policies, enable enforceable data contracts, and ensure traceable data provenance across organizations.

Data spaces operate as an operational trust layer, one that allows data to flow while remaining under the control of its providers.

Complementary, not competing, layers

At the same time, new AI‑specific protocols are emerging to solve technical problems. Protocols such as the Model Context Protocol describe how AI systems access tools, retrieve context, or interact with resources. They focus on interaction mechanics.

Data spaces address a complementary question: under which rules these interactions take place across organizational boundaries, and how accountability is maintained when systems act autonomously. Seen together, these layers do not compete. They complete each other.

The relationship is simple but often misunderstood. AI protocols explain how systems interact. Data spaces define the conditions under which those interactions are allowed, governed, and trusted. Better together is not a slogan, but an accurate description of this division of responsibility.

Making the connection visible

Within IDSA, this convergence of AI and data spaces has led to focused work. A dedicated Task Force on AI and Data Spaces has been established to document how existing data space standards apply to concrete AI scenarios already being deployed. These scenarios range from retrieval and inference to federated learning and agent‑based workflows.

The task force is not developing new protocols or architectures. Its mandate is to translate what already exists into guidance that is accessible to AI practitioners beyond the traditional data space community. Work is carried out transparently, with a public scoping paper planned for release by the end of June 2026.

Trust as infrastructure

Trust and data sovereignty are not features that AI systems can simply add at the end. They are infrastructure. And in the case of data spaces, that infrastructure is already in place.

“As AI continues to move deeper into operational environments, this perspective reframes the discussion away from future promises and toward present capabilities. It highlights that many of the governance challenges facing AI today have already been addressed elsewhere, quietly, through years of standardization and deployment.”

Reinhold Achatz, Chairman of the Board of IDSA

AI and data spaces are not separate conversations. They are part of the same system. And increasingly, they are better understood together.

The post Better together: Data spaces ground AI in trust and accountability appeared first on International Data Spaces.

When data becomes unavoidable

Manufacturers today depend on information that extends far beyond their own systems. Sustainability reporting, emissions accounting, circularity requirements, due‑diligence obligations, and digital product documentation all require data that spans the entire product lifecycle across companies, sectors, and borders.

At the same time, that data is sensitive. It reflects process know‑how, supplier relationships, and competitive advantage. Sharing it casually is not an option. This tension – needing data you do not control, while needing to stay in control – sits at the core of manufacturing’s digital transformation. And it is exactly the tension explored in the IDSA Executive Brief on Manufacturing Data Spaces.

Why data spaces fit manufacturing logic

The paper makes one point unmistakably clear: manufacturing does not need another platform. Instead, it needs governed environments that allow organizations to share data under transparent and enforceable rules, without centralizing ownership or dissolving responsibility.

Data spaces follow this logic. They enable collaboration while preserving data sovereignty. They are federated rather than centralized, interoperable rather than uniform, governed rather than ad‑hoc. This approach aligns with how manufacturing already works: distributed, specialized, and deeply dependent on trust.

A global movement with local realities

The brief situates manufacturing data spaces in a global context. Across Europe, Asia, and the Americas, industrial initiatives are converging on similar principles. Secure data flows, lifecycle transparency, and compatibility across multi‑tier value chains.

Yet the paper is careful not to present a single blueprint. Manufacturing evolves differently across regions and sectors. What matters is not identical solutions, but shared foundations that prevent fragmentation into incompatible ecosystems.

From funding programs to operational structures

One of the most valuable contributions of the paper is its sober view on Manufacturing‑X. The brief describes Manufacturing‑X as a public–private funding framework from which multiple independent project families emerged, each shaped by its domain, partners, and priorities.

Factory‑X, Semiconductor‑X, Chem‑X and other initiatives illustrate how manufacturing data spaces are forming around real industrial challenges. Diversity is not a weakness here, it reflects the sector itself. The risk lies elsewhere: in allowing this diversity to harden into incompatible islands.

The importance of shared building blocks

This is where the paper goes deeper than most discussions. It highlights the growing reuse of shared governance and technical building blocks across manufacturing data spaces: trust and identity mechanisms, usage‑control principles, semantic alignment practices, and operational functions such as onboarding, verification, and certification

These building blocks are what make scaling possible. Without them, data spaces remain pilots. With them, they become infrastructure.

IDSA’s role: alignment, not control

International Data Spaces Association is a partner in this multi‑actor environment. IDSA contributes mature concepts such as the IDS Reference Architecture Model, the IDSA Rulebook, and the Dataspace Protocol to help align diverse initiatives without overriding domain‑specific needs.

This is an important distinction. Manufacturing data spaces will not be built by one organization or one standard alone. They emerge through coordination, compatibility, and shared responsibility.

If you want to understand how manufacturing is organizing data on its own terms, this paper is the place to start.

Read the full IDSA Executive Brief: Manufacturing data spaces and the role of IDSA

The post Manufacturing data spaces: What’s actually taking shape appeared first on International Data Spaces.

The question is no longer if data will be shared, but how.

Industrial data spaces are no longer a future discussion. Across global value chains, companies face mounting pressure: transparency requirements, sustainability reporting, cross‑border collaboration, and digital interoperability. All without sacrificing control over their data.

Both Brazilian demonstrators showcased at Hannover Messe offer a clear answer: data sharing without centralization, without loss of sovereignty, and with governance built in by design.

ELDORADO’s BrasAuto Demonstrator: automotive coordination without data exposure

At the ELDORADO stand, visitors are invited into a simulated automotive logistics data space, one that feels strikingly close to real industrial operations.

The BrasAuto Demonstrator connects three fictional but realistic roles: a vehicle manufacturer, a parts supplier, and a logistics service provider. Each operates its own IDS Connector. Each decides which operational data is shared and under which usage policies.

What flows through the data space are confirmed plans, inventory levels, consumption data, and logistics execution events. What stays strictly local are forecasts, optimization logic, and analytical models.

For visitors, the value lies in seeing how an IDSA‑compliant data space can reduce coordination friction in complex supply chains while preserving competitive integrity. It is a clear, accessible learning environment designed to make data sovereignty understandable, not abstract.

ABINC’s Federated Industrial Data Space: trust across borders

Where ELDORADO zooms into automotive logistics, ABINC broadens the lens. Its Federated Industrial Data Space Demonstrator, developed in a Brazil–European collaboration, shows how industrial data can travel across companies, sectors, and even continents.

The demonstrator focuses on low‑risk, high‑value industrial indicators: machine states, operating hours, downtime reasons, and energy consumption. These indicators enable benchmarking, efficiency analysis, ESG reporting, and operational learning, while sensitive production data remains protected.

There is no central database. No platform lock‑in. Instead, governance rules, purpose limitation, and consent are embedded directly into the technical architecture.

At Hannover Messe, this demonstrator tells a compelling story for international visitors. Cross‑border industrial cooperation is possible today, if trust is engineered and not assumed.

Two paths, one shared principle

While the two demonstrators address different industrial contexts, they converge on the same core idea. Data spaces are not about sharing more data but about sharing the right data, under the right conditions.

Both were built primarily as educational environments to help Brazilian companies, institutions, and policymakers understand what data spaces mean in practice. At Hannover Messe, they now invite the global community to step inside functioning, IDSA‑aligned ecosystems.

These demonstrators allow visitors to:

• see IDSA principles applied end‑to‑end
• understand governance not as theory, but as a running system
• explore how trust can be embedded into technology—not negotiated case by case

Continue the conversation at Hannover Messe 2026

If you want to explore how industrial data spaces are moving from strategic vision to measurable business value, join IDSA at Hannover Messe in Hall 27, Booth F60 – partner pod at the OPC Foundation.

On Tuesday, April 21, 2026, IDSA hosts Industrial Data Spaces in Action – Simple, Scalable, Profitable, a dedicated event for industry leaders and IT decision‑makers who want to understand how sovereign data sharing moves from strategy to execution — and how it delivers measurable business value.

The event will take place on April 21, 13:00 to 16:30, in the Hannover Messe Convention Center, room 18.

Register free for the Journey & Event: forms.office.com

The post IDSA members from Brazil bring data spaces to life at Hannover Messe appeared first on International Data Spaces.

Data spaces as industrial infrastructure

The challenge is no longer whether data should be shared, but how. Companies need to exchange information without losing control over sensitive knowledge, without building a custom integration for every partner, and without navigating a different trust model each time. Traditional approaches struggle to scale in this environment.

Data spaces respond to this structural challenge. They are not platforms, and they do not centralize data. Instead, data spaces create governed environments in which organizations can share data under transparent and enforceable rules, while retaining sovereignty over how that data is used.

As regulatory expectations grow and supply chains become more interdependent, data spaces increasingly function as industrial infrastructure. Much like logistics networks or energy systems, they enable distributed production systems to operate reliably and transparently at scale.

Manufacturing data across boundaries

This transformation is not confined to Europe or to a single initiative. Across the world, industrial data spaces are emerging to solve similar problems: secure data flows, lifecycle transparency, and interoperability across complex value chains.

Different regions pursue different governance models, but the direction is shared. Manufacturing data must move across organizational and national boundaries in ways that align with real supply chains, not political borders. Forums such as the International Manufacturing-X Council reflect the growing recognition that compatibility across regions will be essential for long-term success.

In this landscape, IDSA contributes experience built over more than a decade of research and implementation. Through its work, such as the IDS Reference Architecture Model, the IDSA Rulebook, and the Dataspace Protocol, IDSA supports trusted, interoperable data sharing across diverse technical environments. This role is collaborative rather than centralizing. Manufacturing data spaces can only succeed if they remain interoperable across sectors while respecting domain-specific needs.

Continue the conversation at Hannover Messe 2026

Hannover Messe brings together the physical and digital realities of manufacturing. It is where strategic questions meet operational constraints, and where infrastructure thinking moves from concept to implementation.

On Tuesday, April 21, 2026, IDSA hosts Industrial Data Spaces in Action – Simple, Scalable, Profitable, a dedicated event for industry leaders and IT decision-makers who want to understand how sovereign data sharing moves from strategy to execution — and how it delivers measurable business value.

The event will take place on April 21, 13:00 to 16:30, in the Hannover Messe Convention Center, room 18.

Register free for the Journey & Event: forms.office.com

The post The factory is no longer enough appeared first on International Data Spaces.

From one book to many focused articles

The most visible change sits in the structure. The current chapter-based format will give way to a collection of individual articles. Think of it less as a single manual and more as a growing library.

This shift allows content to evolve faster. Therefore, updates no longer require reshaping an entire document. Instead, specific articles can change as requirements, practices, and standards move forward.

Meanwhile, content that no longer supports today’s work will quietly leave the stage. The result should feel leaner and easier to navigate.

Sharpening the focus on what matters now

Functional requirements take center stage in the upcoming version. Many of these topics have changed in recent months, and the IDSA Rulebook will reflect that reality more clearly. As a consequence, outdated sections will be removed. Readers should spend less time filtering information and more time applying it.

One topic surfaced quickly as both important and overdue: a dedicated IDSA glossary.

At the moment, the IDSA Rulebook relies on external glossaries, including those from other initiatives. While useful, this creates friction. Terms drift. Interpretations vary. Discussions slow down.

Therefore, several working groups will now collaborate on a shared IDSA glossary. This work cuts across domains, which makes coordination essential. However, it also offers a chance to align language across technical, legal, and organizational perspectives.

Over time, this glossary should reduce misunderstandings and speed up collaboration.

A living and growing rulebook

Taken together, these changes signal a shift in how the IDSA Rulebook lives and grows. It becomes more modular. It stays closer to current practice. It speaks a clearer language.

Most importantly, it invites participation. The IDSA Rulebook is no longer something that updates occasionally in the background. It becomes a shared working space that reflects how data spaces actually operate.

The post A new chapter for the IDSA Rulebook appeared first on International Data Spaces.

The UN Commission on Science and Technology for Development (CSTD) created the Working Group on Data Governance to confront one of the defining challenges of our time: how to enable safe, secure and trusted data flows that support sustainable development across the globe.

Its mandate spans all levels of governance, international, national, regional, and local, and reflects the diversity of the global community. Nations from every continent, representatives of Indigenous peoples, and organizations with specialized expertise are all part of the conversation. And that makes sense because development needs data, and data needs rules – and those rules only work if they work for everyone.

A working group with a wide lens

The Working Group is structured around four thematic tracks, each addressing a core dimension of global data governance:

• Data governance principles
• Interoperability of data systems
• Sharing the benefits of data
• Cross‑border data flows

Taken together, the tracks represent both the promise and the tension of the global data sharing landscape. How do we protect sovereignty while enabling exchange? How do we ensure fairness while fostering innovation? How do we build trust across borders, sectors, and cultures? These questions are the blueprint for the digital world we are building.

Where data spaces fit in

For IDSA, the conversations at the UN sound remarkably familiar. The idea of data spaces as federated, interoperable models for secure and sovereign data sharing resonates deeply with the Working Group’s priorities. It is perhaps no surprise that many participants are openly supportive of the data space approach.

Across the discussions, voices from Europe, Canada, Brazil, Japan, Switzerland, Austria, and others regularly emphasize the need for interoperable frameworks and governance models that respect data sovereignty while enabling collaboration. The data space concept fits neatly into that intersection.

And increasingly, it is part of the Working Group’s own thinking: IDSA’s work and principles are referenced directly within the group’s documentation. In a forum of nations, this visibility is a recognition that technical communities and standards bodies have a role in shaping global policy.

IDSA’s role: bringing expertise

IDSA participates in the Working Group as an official observer. While the UN is primarily a forum for member states, selected organizations with relevant expertise are invited to contribute. This position gives IDSA the opportunity to inject practical knowledge from real‑world data space implementations into a dialogue often dominated by policy perspectives.

It is, in many ways, a quiet but essential piece of diplomacy. The visibility, the influence, and the ability to ensure that the global narrative includes trusted and interoperable data spaces that is invaluable.

The Working Group’s efforts will culminate in a set of recommendations submitted to the United Nations General Assembly. Once adopted, these recommendations become part of the global reference frame, much like the Internet Governance Forum before it.

That means the ideas taking shape today may influence policy frameworks for years to come. With data spaces already appearing in the drafts and recommendations, IDSA’s contributions are helping define internationally recognized best practices for trusted data sharing.

And that matters for governments, for industry, for innovation, and for the future architecture of the digital economy.

The post A seat in a global room appeared first on International Data Spaces.

A distributed model aligned with dataspace principles

Data spaces create environments where organizations share data without giving up control. They rely on policies, identity frameworks, shared governance, and interoperable protocols. According to ISO/IEC 20151, a data space is a governance framework and a set of supporting services designed to build trustworthiness for data sharing.

X‑Road already operates with many of these characteristics. Its architecture allows data to move directly between service providers and consumers, with no central party accessing the content. Each organization retains ownership of its data, enforces its access policies, and participates in a shared trust framework.

The upcoming X‑Road 8 release is a significant step. It will implement the Dataspace Protocol (DSP) and the Decentralized Claims Protocol (DCP), integrating elements widely used in data space initiatives. It also adopts components from the Eclipse Dataspace Components (EDC) project, aligning X‑Road with implementations emerging across Europe and beyond. This shift replaces custom gateways with standardized mechanisms for interoperability.

Where IDSA fits into the evolution

Multiple international organizations work on data space specifications, including IDSA, Gaia‑X, the Eclipse Dataspace Working Group, and the Data Spaces Support Centre. These groups coordinate to keep standards aligned and avoid fragmentation. X‑Road directly references this ecosystem and is evolving in the same direction.

IDSA’s frameworks – the Reference Architecture Model, the IDSA Rulebook, and the Dataspace Protocol – form essential guidance for building sovereign and interoperable data‑sharing environments. They describe how roles, processes, policies, and technical functions come together so participants maintain control while collaborating securely.

A stronger foundation for public‑sector data spaces

X‑Road’s move toward a standardized data space architecture demonstrates how public‑sector infrastructure can modernize without losing the guarantees that matter: autonomy, security, and accountability. It also creates new opportunities for aligning national systems with international standards, reducing integration complexity and supporting more consistent governance models.

This direction benefits citizens and public agencies alike. It creates a trusted environment for cross‑border services, sector‑specific data exchange, and long‑term interoperability. With X‑Road 8 joining the global data space community, the public sector gains a powerful reference for implementing open, sovereign, and standards‑based digital infrastructure.

The post X‑Road and the shift toward standards‑based data spaces appeared first on International Data Spaces.

The situation today

Many industries have advanced substantially in data‑related capabilities. Digital threads link product and production information. IT, OT, and engineering systems are becoming more aligned. Industrial data management gains prominence as companies explore real‑time insights and early industrial‑AI applications. These developments create favorable conditions for data spaces.

At the same time, organizational readiness remains inconsistent. Internal data governance differs between business units. Local technology stacks evolve independently. Responsibilities for data and digitalization are distributed across several roles. These realities shape how fast data‑space initiatives can scale, regardless of their technical promise.

What the Gartner Hype Cycle indicates

The Gartner Hype Cycle positions manufacturing data spaces on the innovation path. This placement reflects strong conceptual agreement and increasing standardization efforts, while also signaling that broad adoption depends on improved internal alignment.

The Hype Cycle illustrates how a specific technology moves from early promise to operational use: vendor activity intensifies, standardization gains traction, and reference implementations start to appear. For data spaces, this phase indicates that the foundations are forming, but widespread use still requires more clarity in governance, responsibilities, and interoperability.

Within the IDSA community, this insight strengthens the emphasis on a common architectural foundation and the trust mechanisms built into it. Globally standardized building blocks and solutions that enable simple and quick onboarding to a data space support organizations that want to participate without reinventing essential components.

Strategic opportunities

Several technology trends support the climb toward broader adoption:

• Digital threads and lifecycle transparency create consistent data flows that benefit from a data‑space structure.
• IT‑OT‑engineering convergence increases the relevance of shared governance models.
• Industrial AI raises expectations for reliable, well‑governed data.

IDSA contributes by providing validated frameworks that define how trusted data sharing can function across companies and sectors. IDSA’s assets establish a common foundation for building data spaces. Reference architecture, rulebook, certification, protocols and reference implementations enable organisations to align on a shared understanding of how interoperability and trust must be implemented. This clarity allows solution providers, integrators, and SMEs to position themselves early while the ecosystem continues to take shape.

Risks to monitor

Adoption challenges remain visible. Governance structures often lack the consistency required for cross‑company collaboration. Identity and access management must address sensitive operational data and ensure differentiated rights. Smaller organizations need practical, accessible guidance to start. Differences in maturity between industries create further fragmentation.

To address these obstacles in a structured way, IDSA established the Data Space Adoption Forum. This working group focuses on practical, low‑cost, and seamless onboarding approaches that support SMEs, data‑space operators, and service providers. Its work aims to ensure that all companies and organizations, regardless of size or technological sophistication, can participate in trusted data sharing.

Addressing these issues early strengthens the conditions needed for progress along the Gartner Hype Cycle. Greater clarity in governance, identity management, and onboarding processes creates a more predictable environment in which data spaces can evolve from early innovation into stable operational practice.

Recommended actions

Clear governance structures improve preparedness. Harmonized roles, responsibilities, and data‑handling principles reduce internal barriers. Identity and access mechanisms should enable controlled sharing of IT, OT, and engineering data. Practical guidance and shared examples help newcomers engage confidently.

As these elements mature, data spaces advance from early innovation toward more stable application. The Gartner Hype Cycle highlights this trajectory and underscores the importance of coordinated action across the IDSA community.

The post Where data spaces stand on the Gartner Hype Cycle appeared first on International Data Spaces.

What changed in Madrid: adoption became a service

The session began with a direct observation: current onboarding speeds do not align with real supply chain scales. But instead of dwelling on the problem, the focus moved immediately to the tools and structures that can change this.

Two short demos, each roughly ten minutes, demonstrated far more than any slide deck could. They showed an onboarding flow that looked like a standard digital service journey: a provider offers a ready‑made service, a partner guides the customer, the participant follows a structured onboarding path, and data sharing begins without a custom integration project.

This was the essential point: Joining a data space must become as operationally normal as activating any modern SaaS product. One participant captured this shift by describing how data spaces should move SMEs away from project based, point-to-point integrations toward a click-and-connect model. That framing landed strongly in the room.

The “last mile” solution: Scale through existing trust networks

Across the panel, there was a striking consensus: SMEs enter digital ecosystems through people they already trust.

Several contributors emphasized that local SMEs tend to rely on their established IT partners and managed service providers. These partners are day-to-day trust anchors. When they are part of the solution, onboarding accelerates naturally. Onboarding SMEs in a single day through just a few phone calls is something that could have scaled to hundreds if more participants had been available.

This mirrors what we hear regularly inside the Data Space Adoption Forum: SMEs do not want to become experts in connector operations or distributed trust frameworks. They want a managed service, provided by someone they already know, with predictable effort and predictable cost.

This is why the approach presented in Madrid places distribution at the center. Cloud infrastructure providers and MSPs already support thousands of SMEs. They operate established partner networks. And they know how to deliver repeatable onboarding processes. The DSAF work connects this proven distribution model with the requirements of data spaces.

The operational stack: Cutting costs, hiding complexity, improving compatibility

For executives in the room, the most relevant shift was the transition from install and integrate to provision and govern. What hindered that shift were three recurring constraints across deployments – cost, complexity, and compatibility. The work unveiled in Madrid responded to each with tangible components:

Multi‑tenant operation to reduce cost
Virtualization developments around the connector architecture now allow multiple participants to be served in a single controlled runtime. This significantly reduces overhead and aligns with SME usage patterns.

Automated provisioning to remove operational complexity
A repeatable onboarding journey requires standardized, automated provisioning. The Connector Fabric Manager concept supports this, handling tenant creation and resource orchestration without each organization having to build its own DevOps environment.

Pluggable data planes for compatibility
Rather than forcing every organization to rebuild integrations, reusable data plane components and standardized signaling create an ecosystem of interoperable options. This directly reduces barriers for domains with specific protocols or formats.

For SMEs, this simplifies the equation. The mechanics are manageable, effort is predictable, and onboarding becomes a service rather than a project.

A group effort, by design

One point is important to state explicitly: Nothing presented in Madrid came from a single organization. The DSAF session was the product of many actors working in alignment – IDSA convening the ecosystem, cloud and infrastructure providers bringing service models, dataspace initiatives defining operational requirements, research organizations providing governance and modelling structure, and open-source communities enabling transparent, shared components.

Our session summarized this dynamic with the observation that broad adoption accelerates when a diverse community drives it. Not a handful of large players, but thousands of organizations, connected through shared standards and trusted partners.

And that is ultimately what the DSAF represents: not a single tool or platform, but a coordinated operating model that makes trusted data sharing accessible, scalable, and genuinely usable.

The post Who Onboards the Millions? Inside the Data Space Adoption Forum session at DSS 2026 appeared first on International Data Spaces.