Data Usage Control: What Happens to Your Information After You Share It

Sharing data with a partner, supplier or public administration often raises the same question among data and compliance managers: once the file or access has been granted, what prevents the recipient from using it for something other than what was agreed, keeping it indefinitely, or passing it on to a third party? The traditional answer has been a paper contract and trust. The technical answer, increasingly demanded by companies and public administrations, is data usage control applied continuously, not just at the point of delivery.

This is precisely the problem that data spaces address compared with traditional data exchange methods: an email, an open API without traceability, or a file downloaded to a USB drive leaves no record of what happens afterwards. Once data leaves an organisation, it continues to exist but is no longer observable and, therefore, is no longer governable.

For data, security and compliance managers in Castilla y León, understanding how control can be maintained after data has been shared is the difference between sharing data with guarantees and sharing it on blind faith. In this article, we explain how usage policies, real-time policy engines and audit mechanisms make data usage control possible in a modern data space.

Why control ends too soon in traditional data exchange

In conventional data exchange, control is exercised at the point of decision: someone authorises the transfer, signs a confidentiality agreement and, from then on, relies on the other party to comply. There is no technical mechanism to verify, during actual use, whether the agreed conditions are being respected.

This creates several common blind spots:

• There is no way of knowing how many times data has been accessed or downloaded after the initial transfer.
• There is no straightforward way to revoke access if the circumstances of the agreement change.
• The purpose of use declared when the contract was signed is not checked against the actual use made of the data.
• If the data is redistributed to an unauthorised third party, the original organisation will rarely find out.

A data space addresses these blind spots through its technical design, rather than relying solely on contractual agreements: each access request goes through a connector and a policy engine that can verify conditions before delivering the data, not just before the agreement is signed.

Usage policies: what can be defined beyond “yes” or “no”

Data sovereignty —the idea that the owner of data retains the ability to make decisions about it even after sharing it— is put into practice through configurable usage policies. Instead of a binary access model (all or nothing), the data provider can define specific conditions:

• Number of uses: the data can be accessed a maximum number of times before access is exhausted.
• Expiry: access permission expires on a specific date or after a defined period, without anyone having to revoke it manually.
• Permitted purpose: the purpose for which the data may be used is specified (for example, “statistical research” or “public service planning”), and any use outside that purpose falls outside the policy.
• Prohibition of redistribution: the recipient is explicitly prevented from sharing the data with a third party without additional authorisation.
• Geographical or sectoral restrictions: access can be limited to organisations located within a specific geographical area or operating in a particular sector.

A practical example: an agri-food cooperative in Castilla y León, Cooperativa Duero Verde, publishes soil moisture sensor data in the data space so that a technology centre can use it for an agronomic research project. The cooperative can establish that access expires after twelve months, that the purpose is strictly limited to “non-commercial agronomic research”, and that the technology centre cannot share the data with any private company without additional explicit authorisation. All of this is defined as a policy rather than as a clause that depends on someone remembering to enforce it.

The language behind the policies: ODRL

To ensure that these conditions can be interpreted by machines and not only by humans, data spaces rely on standards such as ODRL (Open Digital Rights Language), a model that makes it possible to express permissions, prohibitions and obligations in a structured way. An ODRL policy can specify, for example, that a “read” action is permitted until a specific date and subject to the condition that the recipient has a declared purpose that is compatible with the authorised purpose. This enables a policy engine to evaluate the request automatically, without ambiguity.

The policy engine: real-time evaluation, not just an initial check

The component that turns a usage policy into effective control is the policy engine, which in data space platforms often relies on Open Policy Agent (OPA). This engine does not merely check requirements when access is initially granted: it evaluates each access request in real time, every time someone attempts to access or transfer the data.

This has an important practical implication: if a policy establishes a usage limit and that limit is reached, the next request is automatically denied, without manual intervention. If access has expired, the policy engine detects it at the moment of the request, rather than during a quarterly review. And if the purpose declared by the data requester does not match the authorised purpose, the request cannot be completed.

This approach shifts data usage control from a one-off event —the signing of an agreement— to a continuous process that is executed for every transaction. For a compliance manager, this significantly reduces the risk window: there is no longer a need to rely solely on periodic audits to detect non-compliance, because the system itself prevents it from occurring at the point where it would otherwise take place.

Recorded evidence: from trust to verifiable traceability

Even the best policy is of limited value if there is no evidence that it was actually enforced. This is where the second pillar of data usage control comes into play: the recording of evidence. In a data space with an IDS-type architecture, each transfer and agreement is recorded in smart contracts on a permissioned blockchain network, providing an immutable record of who accessed which data, when and under what conditions.

In addition, a clearing house —a specific function within the data space dedicated to compliance auditing— collects this evidence and makes it possible to reconstruct the complete history of an agreement: from the initial request through each subsequent access, including the policies that were applied at each point in time. This makes a genuine data audit possible, based not on statements from the parties involved but on verifiable records.

For a public administration in the province sharing waste-management data with a concessionaire, this means being able to demonstrate, during an inspection or internal audit, exactly what was shared, under what conditions and how it was used, without relying on the other party to provide that information in good faith.

Data governance as a process, not a document

All of the above forms what is commonly referred to as data governance: the set of rules, roles and technical mechanisms that ensure data is handled according to the agreed conditions throughout its entire lifecycle, not just when it is shared. The difference between an organisation with mature data governance and one that simply has policies written down in a document lies precisely in whether those policies are automatically enforced and leave an evidence trail, or whether they depend on occasional manual reviews.

Adopting this model does not mean giving up the agility of sharing data quickly. On the contrary, when conditions are clearly defined from the outset and the policy engine automatically enforces them, organisations can share data with greater confidence and less friction, because the risk of misuse no longer depends solely on the other party’s word.

Start controlling the use of your shared data

If your organisation needs to share data with genuine compliance guarantees, a modern data space provides an integrated policy engine, evidence recording and auditing mechanisms by design. If your organisation is considering taking this step, look for a technology partner with experience in data spaces to assess how this approach can be applied to your specific needs.