Drupal AI・8 min read # Digital sovereignty: Knowing what must stay under your control ![Data sovereignty hero image](/sites/default/files/2026-09/Digital-sovereignty-in-2026_-Knowing-what-must-stay-under-your-control-1-%281%29.png) Table of contents - [In a nutshell](#in-a-nutshell) - [Why the issue has become harder to ignore](#why-the-issue-has-become-harder-to-ignore) - [Data residency is not the same as digital sovereignty](#data-residency-is-not-the-same-as-digital-sovereignty) - [What large organizations are doing differently](#what-large-organizations-are-doing-differently) - [Why AI sovereignty is only one part of the issue](#why-ai-sovereignty-is-only-one-part-of-the-issue) - [The harder part is workflow sovereignty](#the-harder-part-is-workflow-sovereignty) - [The downsides are real](#the-downsides-are-real) - [What enterprises should do next](#what-enterprises-should-do-next) ## In a nutshell Digital sovereignty is becoming a more important enterprise concern because the systems businesses rely on are no longer passive infrastructure. They store data, shape workflows, support customer-facing services, connect teams across regions, and increasingly provide the foundation for AI. For a long time, digital sovereignty was mostly discussed as a policy or compliance issue. The common question was where data was stored and whether that storage met local regulatory requirements. That question still matters, but it is no longer enough. In 2026, enterprises also need to understand who controls their data, who operates the systems that process it, how AI tools use it, what evidence exists for audit and compliance, and whether critical workflows can continue if a vendor, cloud provider, model, or jurisdictional rule changes. **The stronger case for digital sovereignty is not that every enterprise should build everything itself. That would be unrealistic and, in many cases, wasteful.** The more serious case is that enterprises need to know which parts of their digital estate are too important to leave entirely outside their control. ## Why the issue has become harder to ignore The pressure behind digital sovereignty has been building for years. Cloud concentration, vendor lock-in, data localization rules, cybersecurity risk, and cross-border compliance have all made platform control more complicated. What changed the urgency is AI. AI systems depend on access to enterprise data. They also introduce new questions about how that data is interpreted, transformed, reused, and acted on. A traditional software system may store a record or move a request through a workflow. An AI-enabled system may summarize a record, classify a case, recommend a decision, generate a response, or trigger the next step in a business process. That makes the governance problem more difficult. The relevant question is whether the organization understands the data well enough to use it safely. **If data ownership is unclear, if permissions are inconsistent, if information is duplicated across systems, or if teams cannot prove how an AI system reached an output, then the enterprise does not have meaningful control. It may have tools, but it does not yet have the foundations needed to trust those tools at scale.** This is why privacy, data governance, cybersecurity, AI governance, and platform architecture are starting to converge. They were once treated as separate functions. In AI-enabled enterprises, they increasingly describe the same operating problem. ## Data residency is not the same as digital sovereignty Many organizations still begin with data residency. That is understandable. Regulated industries need to know where data is stored, which laws apply, and what obligations a vendor can meet. But residency is a narrower requirement than sovereignty. **An enterprise can store data in the correct region and still lack control over how that data moves, who can access it, how long it is retained, whether it is used for AI training, and how usage is evidenced.** It can meet a hosting requirement while still relying on a vendor-controlled workflow that cannot be inspected or changed. It can have local infrastructure and still lack clear ownership over identity, encryption, logging, access controls, model use, and audit evidence. This distinction matters because many enterprises are discovering that the practical difficulty is not simply keeping data in one place. The difficulty is operating across many jurisdictions, vendors, business units, and systems while maintaining consistent control. Data localization can satisfy one requirement and create others: duplicated infrastructure, more vendors, slower rollouts, inconsistent controls, and higher operating complexity. **The same distinction is now showing up in regulation.** The EU's proposed Cloud and AI Development Act introduces tiered sovereignty criteria that reach well beyond storage location, covering ownership structures, immunity from extraterritorial laws, operational control, and supply chain transparency. In other words, the regulatory definition of sovereignty is already moving past residency toward control. A mature sovereignty strategy has to do the same. It needs to separate the regulatory question from the operating question. Where data resides matters. But who can govern, use, move, explain, and recover it matters just as much. ## What large organizations are doing differently The most visible examples in 2026 are coming from Europe, where technological sovereignty has become a policy, infrastructure, and business concern. The European Commission's 2026 technological sovereignty package, presented in June, covers semiconductors, AI, cloud, and open source, delivered through the Chips Act 2.0, the Cloud and AI Development Act, an Open Source Strategy, and a strategic roadmap for digitalization and AI in energy. That is important because it frames sovereignty as a stack-level issue, not a narrow privacy issue. Enterprises are responding in a similar way. The concern is not only where software comes from. It is whether the organization has enough choice and control across the parts of the stack that matter. This is visible in the way large European companies are spreading AI risk across providers. Executives from Siemens, Renault Group, Orange, and ChapsVision have said they already use a mix of US, Chinese, and European models rather than depending on a single provider. That does not mean they are rejecting global technology. It means they are treating dependency as a risk that needs to be managed. As Siemens Digital Industries chief Cedrik Neike put it, sovereignty is often confused with self-sufficiency, and self-sufficiency is the wrong goal. The aim is flexibility, not isolation. This is the important point: **sovereignty is not the same as isolation.** The serious enterprise version is about resilience and optionality. If a provider changes access, pricing, terms, model availability, or deployment rules, the enterprise should not find that a critical workflow has become impossible to operate. This is no longer hypothetical. In 2026, a major US AI lab abruptly cut access to some of its strongest models after US government orders restricting use by foreign nationals, which renewed concern in Europe about a digital kill switch where access to critical AI could be revoked with little warning. For non-critical experimentation, a single external provider may be acceptable. For systems that affect customers, compliance, regulated data, or business continuity, that level of dependency may be harder to justify. ## Why AI sovereignty is only one part of the issue AI sovereignty is often treated as the headline, but it is only one layer of digital sovereignty. An organization can run an approved model in a controlled environment and still have weak sovereignty if the data feeding that model is poorly governed, if workflows are not auditable, or if teams do not know who owns the decisions made with AI support. This is why the broader platform matters. **Enterprises need control over data at rest, data in use, and data in motion. They need operational control over environments. They need technology choices that avoid unnecessary lock-in. They need AI controls that define where models run, which data they can use, how inference is governed, and when human review is required.** Vendor offerings are beginning to reflect this. IBM's Sovereign Core, which reached general availability in 2026, defines digital sovereignty across operational sovereignty, data sovereignty, technology sovereignty, and AI sovereignty. Whether or not an enterprise uses that specific product, the structure is useful because it shows where the market is moving. Sovereignty is being operationalized as control over systems, evidence, AI execution, and portability, not merely as a location requirement. ## The harder part is workflow sovereignty The least discussed layer is workflow sovereignty. This may become the most important one. **Most enterprises do not experience risk as an abstract data problem. They experience it inside workflows.** A customer request is routed incorrectly. A compliance document is generated from the wrong source. A support answer uses outdated policy. A content team publishes AI-assisted material without adequate review. A regional team uses a tool that does not follow the organization's data rules. A model gives a plausible answer, but no one can show which source it relied on. These are workflow problems. They are also sovereignty problems. **As AI enters more business processes, enterprises need to decide which actions can be automated, which require review, which sources are approved, which outputs need evidence, and who can stop or change the workflow. Without that control, AI governance remains a policy document rather than an operating capability.** This is especially relevant for industries where trust and accountability matter: financial services, healthcare, public sector, education, manufacturing, energy, and large multi-market enterprises. In these sectors, the risk is not only that an AI tool gives a weak answer. The larger risk is that the organization cannot explain, govern, or correct the process that produced it. ## The downsides are real **Digital sovereignty is not free.** More control usually requires more discipline. It may require better data classification, stronger identity and access management, clearer vendor contracts, more careful architecture, and closer coordination between legal, security, data, product, infrastructure, and business teams. It can also increase cost. Running alternative providers, maintaining portability, or choosing sovereign cloud options may be more expensive than relying on the easiest default. Some organizations may overcorrect and add unnecessary complexity in the name of sovereignty. Others may turn sovereignty into a procurement slogan without changing how systems are actually governed. **These are real risks. They are also why digital sovereignty should be approached as a risk-based discipline, not a blanket rule.** Not every system needs the same level of control. A low-risk marketing experiment does not need the same architecture as a national health platform, a banking workflow, or a customer identity system. The work is to identify which systems are critical, which dependencies are acceptable, and which parts of the platform must remain portable, auditable, and under direct governance. ## What enterprises should do next The practical starting point is not to launch a sovereignty program in name. It is to **map where control matters. A** [**digital sovereignty self-assessment**](/digital-sovereignty-assessment) **can show you where that control is already weak.** **Enterprises can begin by identifying their most critical digital workflows:** the systems that support customers, regulated data, revenue, operations, compliance, and AI use cases. For each one, the questions are the same. Who owns the data, where does it live, which vendors process it, which AI tools can access it, what evidence exists, and what happens if access to a provider changes. **The second move is platform architecture.** Lock-in risk hides in cloud dependencies, proprietary data formats, tightly coupled integrations, vendor-controlled workflows, and AI services that cannot be inspected, governed, or replaced. The aim is not to remove every dependency. It is to know which dependencies are strategic risks. **The third is governance.** Access rules, audit evidence, content approval, model selection, data classification, and human review belong inside workflows, not bolted on at the end. That is what makes sovereignty operational. **The serious question for 2026 is not whether an enterprise owns every part of its technology stack. Most will not, and most should not.** It is whether the enterprise still controls the platforms, data, workflows, and AI systems its business now depends on. For many organizations, the honest answer will be mixed. Strong control in some areas, weak visibility in others. Promising AI pilots, fragmented data foundations. Vendor contracts, unclear accountability. **That mix is not a failure. It is a map of where to start.** The work is to know which parts of your stack you cannot afford to lose control of, and to close that gap before someone else decides it for you. Written by Priyanka Jeph Content Design Lead