For AI Providers and AI Consumers
Roksnet for AI Providers and AI Consumers
Trusted X-Road®-based infrastructure for secure, decentralised and auditable AI API exchange.
AI APIs are becoming part of core digital infrastructure. Organizations increasingly use AI services for document processing, classification, translation, fraud detection, scoring, compliance review, customer support, RAG workflows and other business processes.
For simple technical integration, a direct API connection may be enough:
AI Consumer
API key
AI Provider
AI response
But for banks, public-sector bodies, insurers, healthcare providers, enterprise ecosystems and other regulated organizations, a simple API key often does not solve the main trust problem
They need to understand not only which model produced the answer, but also how the exchange itself was organised:
who sent the request, which legal entity received it,
which service was used, when the exchange happened,
whether the request and response can be correlated later,
whether the integrity of the event can be proven without unnecessary disclosure of sensitive content,
and whether an audit trail can be produced for internal review, clients, partners or regulators.
This is where Roksnet can help.
Roksnet provides trusted X-Road-based infrastructure for secure data exchange between verified organizations. It allows AI Providers and AI Consumers to interact through a high-trust inter-organizational exchange model, rather than relying only on isolated direct API integrations.
Roksnet does not need to become an AI provider or a universal AI compliance platform. Its role is different: to provide the trusted exchange layer between organizations that provide AI services and organizations that use them.
X-Road
Decentralised P2P exchange, not a central AI gateway
A key advantage of X-Road for AI scenarios is that it is not built around a central intermediary through which all data must pass.
X-Road uses a decentralised exchange model: messages are transmitted directly between the AI Consumer’s Security Server and the AI Provider’s Security Server. Central components are used to manage trust, membership, configuration and services — not to route AI requests and responses through a central data hub.
In simple terms, the exchange model looks like this:
AI Consumer
Business system / application
internal AI request
Consumer Security Server
trusted endpoint of Consumer
direct X-Road message no central AI data hub
Provider Security Server
trusted endpoint of Provider
internal provider call
AI Provider
Model / API / RAG / scoring
This is important for regulated AI.
In a centralised AI gateway model, sensitive data may pass through an additional intermediary. This creates another responsibility centre, another point of failure, another object for due diligence and another layer of trust that must be explained to clients, auditors and regulators.
The X-Road model is different. AI requests and responses move directly between the participating organizations through their Security Servers. Roksnet provides the trusted environment, Member Directory, Trust Services, certificates, timestamps, monitoring, onboarding and support — but it does not need to receive, read or store AI content.
This creates several advantages.
Data stays closer to the parties that are actually involved in the business process: the AI Consumer and the AI Provider.
Roksnet does not become a central AI data hub, which helps reduce legal, operational and reputational risks.
Each party retains control over its own infrastructure, logs, access policies, retention rules and internal governance processes.
This model better fits the expectations of regulated markets: fewer unnecessary intermediaries, verified participants, direct exchange, auditability and controlled access.
in short
— Direct trusted exchange between verified organizations.
Roksnet’s own service concept follows the same logic: data flows directly between the participant’s Security Server and the partner’s Security Server, while Roksnet provides the framework rather than acting as a data hub.
Exchange layer
Roksnet as the trusted AI exchange layer
In the Roksnet model, both parties are organizational participants of the framework.
An AI Provider may publish its AI API as a trusted X-Road service. An AI Consumer may access that service through its own Security Server. The exchange happens between verified parties, with organizational identity, controlled access, Trust Services, certificates, timestamps, monitoring and support.
Roksnet provides an environment in which AI services can be published and consumed as inter-organizational services, not merely as public endpoints protected by API keys. Existing REST or SOAP APIs can be published through a Security Server and consumed by partners without rebuilding the entire integration model.
This creates a clearer distribution of responsibilities.
the AI model, the service, output quality, model governance, safety controls, retention rules and contractual commitments.
ai consumer
the business process, lawful use, internal policies, end-user transparency, human oversight and use of AI results.
the infrastructure layer: trusted organizational identity, secure exchange, service discovery, certificates, timestamps, monitoring and operational support.
Honest framing
Why X-Road is relevant for AI, even if it is not the fastest way to call an API
X-Road was not designed as a low-level AI inference protocol. It is a trusted interoperability layer for secure inter-organisational data exchange.
This should be stated clearly.
A direct HTTPS API call may be faster and simpler than an X-Road-based exchange. But in regulated AI scenarios, speed is not the only criterion. The more important question is whether the exchange can be trusted, proven and reviewed later.
Many AI operations already involve meaningful processing time: model inference, document analysis, RAG retrieval, OCR, post-processing, safety checks or human review. In that context, the overhead of a trusted exchange layer is often not the main cost driver.
The main costs are usually associated with tokens, model infrastructure, GPU resources, storage, governance processes, integration work and legal or compliance risk. Compared with these factors, the additional cost of a trusted transport layer is often a small part of a regulated AI transaction.
Roksnet-based AI exchange can also be optimised technically. Large files do not always need to be transmitted directly through the same channel; secure references, metadata, hashes or asynchronous job patterns can be used instead. Long-running AI tasks can be handled through job IDs, callbacks or polling. High-volume integrations can use adapters, gateways, queues and deployment-level optimisation.
The point is not to use X-Road for every millisecond-sensitive AI interaction. The point is to use it where AI exchange must be trusted, inter-organizational, verifiable and suitable for audit.
For regulated AI, speed is not the only question.
The question is whether the exchange can be trusted, proven and reviewed later.
Market access
X-Road as a gateway to regulated and government-grade markets
For AI Providers, trust is often not only a technical issue. It is also a market-access barrier.
It is difficult to approach a bank, public-sector body, healthcare organization or critical-sector enterprise with a new private API gateway and simply ask them to trust it. Even if the technology is secure, the customer still needs to assess the identity model, trust model, access control, logging, evidence and legal reliability of the exchange.
X-Road gives Roksnet a stronger starting point.
X-Road is not experimental middleware. It is a government-grade interoperability model, proven primarily in European public-sector and regulated digital environments, and internationally understood as a high-trust approach to secure data exchange between organizations.
Even where X-Road is not used as the single national data exchange layer, its architecture is familiar to regulated-market stakeholders: verified participants, decentralised data exchange, Security Servers, trust services, logs, certificates, timestamps, controlled access and no central data hub.
In countries where X-Road or similar data exchange layers are used as national interoperability infrastructure, the requirements for security, participant identification, access control, logging and evidence are among the highest. For AI Providers, the ability to work through such a model may become not a technical complication, but a gateway to markets that are harder to enter with an ordinary AI API gateway.
Roksnet allows an AI Provider to tell customers:
We do not ask you to send data to an unknown central AI hub.
We do not replace your existing systems.
We publish AI services through a trusted organizational exchange layer.
We support verified participants, controlled access and auditable exchange.
We use an infrastructure model that is familiar to public-sector and enterprise environments.
For AI Consumers, this means they can adopt AI without losing control over organizational identity, access, traceability and evidence.
Reference architecture concept
RAILS: a reference architecture concept for AI governance layers
Roksnet does not only provide infrastructure. It has also applied its X-Road competence to a concrete AI problem: how AI Consumers and AI Providers can exchange AI requests and AI responses in a way that supports traceability, evidence and audit-readiness.
This work is represented by RAILS.
RAILS should be understood carefully.
RAILS is not
- a finished product,
- a mandatory Roksnet component,
- a universal protocol,
- a certification mechanism,
- or a legal guarantee of AI Act compliance.
RAILS is a reference architecture concept. It demonstrates how AI Providers, AI Consumers, system integrators or governance vendors may build an additional governance and evidence layer on top of Roksnet infrastructure.
A RAILS-style implementation may support request/response correlation through a correlation ID, evidence-only logging without storing AI content, cryptographic hashes of inputs and outputs, linkage between Consumer-side, Provider-side and X-Road transport events, different logging profiles, optional content escrow, retention policies, legal hold, selective export and audit or regulator packages.
The RAILS reference flow already defines a Controller/Provider model, System Log Bridge, Retention & Regulator Kit, logging profiles, correlation identifiers, hash-based records, X-Road transport events and auditor/regulator export logic.
The purpose of RAILS is not to force one architecture on every AI use case. Different organizations will have different requirements. Some may only need trusted exchange and timestamps. Others may need metadata logs, hash-based evidence, content escrow, regulator export or their own governance layer.
RAILS shows what can be built on top of Roksnet. It gives AI Providers and AI Consumers a practical starting point, while leaving the final implementation, legal assessment and operational responsibilities with the parties that build and use the concrete AI system.
Example: RAILS-style flow
How a RAILS-style layer may add correlation, evidence, retention and audit logic on top of Roksnet’s trusted exchange infrastructure.
AI Consumer
Controller / process owner
1. Creates AI request
Consumer-side Bridge
- correlation_id
- input_hash
- policy metadata
- evidence log
2. Sends request through X-Road
X-Road Layer
- verified members
- Security Servers
- certificates
- timestamps
- transport logs
3. Provider receives request
Provider-side Bridge
- receives correlation_id
- records model metadata
- output_hash
- processing status
4. Calls AI model / service
AI Provider
model / API / RAG / scoring
5. Response returns through the same route
Consumer Evidence Package
- timeline
- hashes
- X-Road message references
- selected provider export
- verification instructions
This illustrates the core idea: Roksnet provides the trusted exchange infrastructure, while a RAILS-style layer may add correlation, evidence, retention and audit logic.
The content of AI requests and AI responses may remain under the control of the AI Consumer and the AI Provider. Roksnet does not need to become the holder of AI content. The RAILS concept itself also follows this logic: it records metadata and hashes, while avoiding content storage except where the parties deliberately choose an escrow profile.
AI Act positioning
AI Act positioning
RAILS may be relevant to AI Act-related implementation work, but it should not be presented as automatic compliance.
The correct position is:
RAILS demonstrates how Roksnet infrastructure may support the implementation of certain governance-related requirements and expectations, especially around traceability, logging, auditability, evidence generation, retention and documentation workflows.
Actual compliance with the AI Act or other applicable requirements depends on the concrete AI system, the role of each party, the risk category, the data processed, contractual arrangements, internal governance and the final technical implementation.
Roksnet provides
infrastructure.
RAILS provides
a reference architecture concept.
Compliance remains
implementation-specific.
Summary
AI Providers
build the models.
AI Consumers
own the business processes.
Roksnet
provides the trusted exchange infrastructure.
X-Road
brings a proven government-grade interoperability model.
Roksnet
makes this model available for organizational AI API exchange.
RAILS
shows how the infrastructure can be extended for audit-sensitive and regulated AI scenarios.
RAILS is not a finished protocol or compliance guarantee. It is a reference architecture concept demonstrating how providers, consumers and partners may build governance, logging, evidence and audit layers on top of Roksnet.
Roksnet is
the infrastructure.
X-Road is
decentralised P2P exchange between verified participants.
RAILS is
the applied reference architecture.
Compliance remains
implementation-specific.