Data sovereignty: how to protect know-how and control production data

Check whether your company has real control over production data: where it is stored, who can access it, under what rules it can be shared, and what needs to be organized before implementing AI, cloud solutions, supplier integrations, or new service models. In manufacturing, data sovereignty comes down to one thing: a company should know what happens to its data before it starts building decisions on top of it.

For a production director, IT/OT manager, or anyone responsible for industrial systems development, this is not a topic to ignore. Data shows machine operating parameters, batch quality, recipes, production traceability, failure rates, energy consumption, changeover times, and line performance.

If a supplier, integrator, or external tool sees more than the project requires, the company may unknowingly give away information about its know-how. Sometimes an exported file, service access, or a dataset sent for analysis is enough. Later, it becomes difficult to determine who used it, where copies were created, and whether access was ever revoked.

Most companies do not see this in a contract or in a single system. They see it only when they map the flow of data.

EU regulations have raised the priority of conversations about industrial data. The Data Act, applicable in the EU from September 12, 2025, organizes the rules for access to data from connected products and related services. In simple terms, data from machines, devices, and systems cannot be treated as a closed asset of one party if the user has the right to access it and share it further.

For manufacturing companies, this changes day-to-day decisions. Data from the shop floor, service operations, sensors, MES, ERP, SCADA, CMMS, and IoT platforms becomes an asset that needs to be described, protected, and governed by clear rules.

Data sovereignty means the ability to decide who can see data, to what extent, for how long, and for what purpose. Without that, every integration increases dependence on a supplier, and every AI implementation can move sensitive information outside a controlled data flow.

Companies gain the most when they treat data as part of the production process. Data has an owner, context, sensitivity level, and usage rules. This helps launch analytics projects faster, work with partners more safely, and reduce the risk of losing knowledge about the company’s own production process.

Data fragmentation threatens control. ERP stores information about products, orders, and batches. MES shows the course of production, while SCADA records process signals. Spreadsheets often hold quality data, operator comments, or temporary failure logs. Machine suppliers use their own service portals, and integrators work with data copies for analysis or diagnostics.

Each element can look correct on its own. The full picture gets lost when no one sees the data flow from machine to report, from report to partner, and from partner to the next system.

You risk losing control over production data when your company:

The most deceptive data often looks purely technical. Machine operating parameters, quality deviations, alarm sequences, cycle times, and process settings can reveal more than a standard business report. For a competitor, technology provider, or unauthorized recipient, this is knowledge about how production really works.

Warning sign: if you cannot identify within one day who outside the company has access to machine, quality, or service data, data sovereignty is only a declaration.

Data sovereignty should be translated into decisions that can be checked. Each one affects security, costs, and the pace of system development.

Technology is often the first step, but a different order may deliver faster results: first a data map, then access rules, then integration. A system should not preserve accidental paths for transferring information.

Data from suppliers and data sent to suppliers moves through many channels: APIs, portals, PDF reports, spreadsheets, machine files, service tickets, or manual exports. Each channel requires a decision about which data is truly needed.

A maintenance example makes the difference clear. A machine supplier may need information about temperature, vibration, and work cycles to support diagnostics. They do not need to see the full production plan, customer data, margins, recipes, or parameters from other lines. Without access rules, it is easy to send more than necessary.

Production data sharing should meet three conditions:

Understood this way, manufacturing data sovereignty makes collaboration easier. Partners receive clear rules, and the company does not need to debate access, format, and responsibility every time.

AI models in manufacturing attract attention with fast results, such as failure prediction, defect analysis, changeover optimization, energy consumption forecasts, quality control, and faster reporting. Many projects still end at the pilot stage because the data is incomplete, inconsistent, or too risky to share outside the company.

IBM’s 2025 report states that the global average cost of a data breach was USD 4.44 million. This does not mean every manufacturing company will pay that amount, but it does show the scale of consequences when an organization does not know where its controlled data flow ends.

Cybersecurity is also highly relevant for manufacturing. In its Threat Landscape 2025 report, ENISA indicated that ransomware claims most often affected the manufacturing sector, with a 14.9% share. An attack on production can stop a line, delay deliveries, and expose process information that is difficult to rebuild.

That is why data sovereignty should come before broad AI adoption. First, the company needs to decide which data can be used to train a model, which data is for internal analysis only, and which data requires anonymization or restricted access.

The simplest audit does not require a large project. Start by answering the questions below honestly and specifically.

Not all data carries the same weight. A sensor temperature reading should be treated differently from a recipe, process parameter, quality result, or machine failure history.

The list should include machine suppliers, integrators, service companies, cloud operators, consultants, logistics partners, and AI tools.

Copies in spreadsheets, emails, and project folders are a common source of lost control. They are difficult to secure, update, and delete.

A number from a sensor is often not very useful on its own. Its value increases when you know which machine it came from, which batch it relates to, when it was collected, and which event it is connected to.

If a partner cannot be quickly cut off from data after cooperation ends, control is only apparent.

This kind of review often shows that the company does not need a new platform right away. It needs order: a data dictionary, access rules, integration between systems, and clear responsibility.

Start with one area, such as quality, maintenance, service, or a specific line. This makes it easier to see where data loses context, where copies circulate, and who has broader access than needed.

During the first week, take these seven steps:

This is a small scope, but it gives you a fast picture of the situation. If it is already difficult to identify sources, copies, and data recipients in one process, a larger integration or AI project will stand on weak ground.

This is usually where the conversation should turn to data architecture, system integration, and control over information flows between production, IT, and partners. If you want to check where your production data loses context, access control, or governance, start with a short data flow diagnosis. At explitia, we help organize production data across production, IT/OT systems, and partner integrations so your company can safely develop AI, analytics, and supplier collaboration.

Manufacturing companies will share more data with suppliers, service partners, customers, AI systems, analytics platforms, and cloud solutions. This direction will not reverse, because data helps reduce downtime, improve quality, and plan production more effectively.

The difference lies in control. Some companies will send data wherever a project currently requires it. Others will build rules that make every data share intentional, limited, and revocable.

Data sovereignty can give you an advantage because you can develop production, work with partners and new technologies, while protecting knowledge about your own process. That knowledge often decides quality, costs, and competitive strength.

The first step is enough: choose one process and check the data flow from source to recipient. If you cannot describe it clearly, you have found a real area for improvement. If you can describe it, you have a good starting point for safe integration, AI, or supplier collaboration.

Data sovereignty in manufacturing means that a company maintains control over production data: it knows where the data is stored, who can access it, why it is used, and how it can be shared safely.

No. Clear rules make collaboration easier because the supplier receives access to the data needed for the service, while the company keeps control over scope, time, and traceability.

The most protected data should be data that reveals company know-how: process parameters, recipes, quality data, failure history, machine settings, test results, batch information, and data used for production optimization.

Start by mapping the data flow for one process. Check sources, systems, recipients, file copies, external access, and places where data loses context.

Yes, if the company wants to use AI without losing control over process information. Before implementing models, it should define which data can be analyzed, which data must be restricted, and which data should not leave the organization.