The adoption of cloud services, SaaS platforms, and artificial intelligence models has allowed companies of all sizes to access capabilities that, just a few years ago, required enormous investments. An organization can deploy infrastructure in minutes, integrate a language model via an API, and automate entire processes without building each component from scratch. This speed has driven innovation, but it is also concentrating an increasing portion of business operations in technologies controlled by third parties.
The risk isn't in using external providers. No modern company can develop its entire infrastructure, models, and applications in-house. The problem arises when critical data, integrations, knowledge, and processes become trapped within an ecosystem that the organization cannot replace without incurring disproportionate costs.
This concern extends beyond the technical sphere. In June 2026, the European Commission presented a technology sovereignty package aimed at strengthening autonomy and resilience in semiconductors, cloud computing, artificial intelligence, and open-source software. The initiative acknowledges that structural dependence on essential technologies can limit security, competitiveness, and decision-making capacity.
Digital sovereignty doesn't mean disconnecting from global providers or building all technology in-house. Nor does it mean rejecting the cloud, business models, or SaaS platforms. It means retaining enough control over critical data, identities, architectural decisions, and processes so that the organization can act in accordance with its own objectives.
A company with digital sovereignty knows where its information is stored, under what jurisdiction it is processed, who can access it, and how to recover it if it decides to change providers. It also understands its application dependencies, has clear contracts, and can maintain operations in the event of an external disruption or modification.
The European Commission has structured its approach to cloud sovereignty around objectives that include strategic control, data protection, security, compliance, choice, and reduced dependencies. In 2026, it also launched a European sovereignty assessment framework for cloud and AI services, reflecting that the concept is now part of concrete procurement and infrastructure decisions.
A company rarely decides to consciously hand over control of its entire operation to a single vendor. Dependence develops gradually. First, a managed service is used because it speeds up a project. Then, proprietary features are added to simplify development. Later, other applications begin consuming those services, and data is stored in platform-specific formats.
Over time, switching providers ceases to be a migration and becomes a complete overhaul. The organization must modify applications, transform data, replace integrations, train teams, and redesign processes. Even without a contractual prohibition against leaving, the technical and operational costs act as a barrier.
Google Cloud defines prevention of vendor lock-in such as reducing the risk of over-reliance on a specific technology or vendor. It also clarifies that dependency doesn't exist solely in cloud services: it can appear in databases, tools, frameworks, and any component that is difficult to replace.
Sovereignty begins by identifying these dependencies before they become invisible.
There's a tendency to portray any dependency as an architectural flaw. However, using proprietary services can be a reasonable decision when it provides a significant advantage in speed, security, scalability, or customer experience. A company may consciously accept a certain dependency because the value gained outweighs the potential migration cost.
The problem arises when the decision is made without evaluation or when no one understands its consequences. Adopting a managed database can save months of operational work. Using a commercial model can allow a solution to be launched without training proprietary infrastructure. These advantages are real and should not be sacrificed simply to achieve theoretical portability.
The architecture must analyze the level of dependency, the criticality of the component, and the available alternatives. Google Cloud recommends evaluating business objectives, interoperability, refactoring costs, team capabilities, and operational complexity before adopting a hybrid or multicloud strategy. Complete independence can also be costly and difficult to manage.
Traditional applications rely on external infrastructure, databases, and services. AI systems add new layers: models, embeddings, evaluation tools, prompt formats, agent systems, vector databases, and moderation services. Each component can create a different dependency.
An application may be connected to a model through a seemingly simple API. However, over time it begins to use vendor-specific features, particular structures for tool calls, proprietary cache formats, or unique document retrieval mechanisms. Assessments may also be tailored to the behavior of that model.
Switching providers is no longer just about replacing a URL. Responses can have a different style, vary in accuracy, and use tools differently. The team needs to review prompts, tests, security policies, and complete user experiences.
The NIST AI Risk Management Framework recommends that organizations identify and manage risks associated with third-party models, data, software, and services throughout their lifecycle. It also suggests having contingency plans in place to address failures or incidents in critical external components.
Many companies believe they have control because they can export their data. However, downloading files does not guarantee that another platform can understand, relate, and use them without an extensive reconstruction process.
True portability requires preserving structure, meaning, history, relationships, and business rules. A CRM export might include contacts and opportunities, but lose automations, permissions, segmentations, and dependencies with other systems. A document database can be migrated, but the metadata and access policies might not be properly transferred.
The risk increases with Artificial Intelligence. Models need context, and that context often depends on processes of preparation, fragmentation, classification, and enrichment that are not always stored in a portable format. A company can retain the original documents but lose the system that transformed them into usable knowledge.
Data sovereignty requires knowing not only where information is located, but also how it acquires value. Schemas, catalogs, data contracts, lineage, and semantic rules are all part of the enterprise's assets, even if they don't appear in a basic export.
Data can be encrypted and still remain outside the company's strategic control if the provider fully manages keys, identities, and access policies. Sovereignty requires analyzing who can authorize transactions, revoke permissions, and access information during normal or extraordinary situations.
In an AI architecture, this question extends to agents, service accounts, and other non-human identities. An agent can query systems, execute actions, and use enterprise credentials. If these identities are not managed independently, changing a platform can affect multiple unseen processes.
Identity control should allow for the separation of responsibilities, the application of least privilege, the auditing of actions, and the revocation of access without relying on cumbersome manual procedures. It is also necessary to understand what data the provider can access to operate, monitor, or improve its service.
Sovereignty is not limited to the physical location of a server. It includes effective control over the decisions that determine who can use the information and under what conditions.
A company can design its application correctly and still lose operational capacity when a cloud, authentication, communications, or AI provider fails. The disruption occurs outside its infrastructure, but customers and employees experience it as an internal failure.
ENISA has warned that digital systems and services are deeply interconnected and that disruptions can have ripple effects throughout the supply chain. Its 2025 threat landscape also identified an increase in the abuse of digital dependencies to amplify the impact of attacks.
This means that business continuity must include external components. The company needs to know which processes depend on each vendor, how long they might be down, and what alternatives exist. Not all applications require immediate redundancy, but critical processes need explicit strategies.
Digital sovereignty does not prevent a third party from failing. It allows the organization to understand the impact, activate contingencies, and retain decision-making capacity during the incident.
Using two or more providers can reduce certain dependencies, but it can also duplicate complexity, costs, tools, and skills. A company can end up with systems distributed across multiple clouds and still lack the ability to move them.
Multicloud architecture generates value when it addresses specific objectives: continuity, regulatory requirements, geographic proximity, access to specialized services, or negotiating power. Adopting it solely to claim independence can create an infrastructure that is difficult to maintain.
Google Cloud points out that a multicloud strategy can help reduce vendor lock-in and allow for the selection of technologies based on their value. At the same time, it recommends evaluating interoperability, security, management, and costs, as these factors can outweigh the expected benefits.
Sovereignty is not measured by the number of suppliers. It is measured by the ability to continue operating, change decisions, and control essential assets without complexity destroying value.
Open source technologies can facilitate portability, inspection, and deployment across diverse environments. An organization can run specific models, databases, or platforms without relying exclusively on a proprietary license. This flexibility explains why the European strategy for technological sovereignty includes a specific push for open source software.
However, using open source does not mean operating without dependencies. The company still needs technical expertise, infrastructure, updates, security, and support. It may also depend on a small community, a sponsoring company, or libraries whose evolution it does not control.
Running an open model within your own infrastructure doesn't automatically guarantee lower costs. The operation may require GPUs, observability, specialized personnel, and security mechanisms that a managed service provides as part of its price.
Open source should be considered a tool within a broader strategy. It can expand options and reduce exit barriers, but it needs governance, architecture, and internal capacity to become truly autonomous.
When a company begins preparing for a migration during a crisis, it's probably already too late. Portability requires decisions made during the design phase: clear interfaces, documented formats, separation of layers, and knowledge of each vendor's specific dependencies.
An application can use an abstraction layer to interact with different AI models. Data can be stored in open formats or replicated to a controlled location. Critical functions can be exposed through custom APIs instead of distributing direct calls to the provider throughout the codebase.
These practices do not eliminate migration work. Their goal is to reduce the area that needs to be modified and prevent dependencies from spreading uncontrollably.
Testing is also important. An exit plan that was never implemented may rely on incorrect assumptions. The organization should periodically verify that it can recover information, recreate configurations, and operate essential processes using a realistic alternative.
Portability is not a contractual document. It is a technical capability that needs to be maintained.
A multi-model strategy uses different models depending on the type of task, cost, sensitivity of the information, or the required quality level. In addition to optimizing results, it can reduce absolute dependence on a single technology.
Simple queries can be handled with small models. Complex processes can utilize larger models. Certain sensitive workloads can run on private infrastructure, while others use managed services. This distribution creates options, but requires a sufficiently mature evaluation and routing layer.
Not all cases require multiple vendors from day one. Maintaining compatibility with multiple models can increase testing, observability, and control efforts. This strategy makes sense when the criticality of the process or the volume of consumption justifies the added complexity.
The company also needs to remember that the models are not completely interchangeable. A migration can change responses, latency, and tool behavior. Therefore, independence should be measured through testing on real-world tasks, not by the theoretical ability to send the same prompt to another API.
Technology alone cannot resolve all dependency risks. Contracts must establish conditions regarding data ownership, portability, availability, incidents, data deletion, subprocessors, and significant service changes.
A company may have a technically flexible architecture but still face contractual limitations on extracting data or continuing to use certain functions. It may also assume that the vendor offers disaster recovery without knowing the specific timelines, regions, or responsibilities.
Conditions related to Artificial Intelligence require additional attention. The organization needs to understand whether its data is used to train models, how long it is retained, what confidentiality guarantees exist, and what happens when the underlying model changes.
The NIST framework recommends that third-party risks be integrated into AI system policies, assessments, and controls. It also suggests regularly monitoring these resources and defining processes for responding to or disabling them when their behavior becomes incompatible with their intended use.
Corporate sovereignty is built through code, contracts, and coordinated processes.
Not all processes require the same level of availability. A content generation tool can be down for a few hours. An agent handling financial operations or critical care needs a different strategy.
The company must classify its use cases and establish acceptable degradation levels. An assistant might temporarily switch to traditional search. An automated process might be relegated to human validation. An application might use an alternative, less accurate model to maintain core functionality.
Designing controlled degradation is usually more realistic than trying to maintain full capacity during any incident. The architecture needs to know what the minimum acceptable service level is and how to communicate its limitations.
The Google Cloud Well-Architected Framework recommends designing systems with consideration for security, resilience, performance, cost, and sustainable operation. These dimensions should also be evaluated when the application relies on AI services or multicloud components.
A sovereign company is not one that never fails. It is one that knows how to continue when a dependency fails.
The assessment should begin by identifying essential assets: data, applications, models, identities, processes, and integrations. For each, the company needs to understand who controls it, where it is located, and how much it would cost to replace it.
Next, you need to analyze concentrations. A single platform can host applications, authentication, data, communications, and AI models. Even if each service is individually reliable, the combined dependencies can create a single point of impact that is too large.
It's also worth reviewing the outbound capabilities. Can the data be recovered in a usable format? Is there sufficient documentation to rebuild integrations? Does the team have the necessary skills to operate an alternative? Do the contracts allow for migration within a reasonable timeframe?
Finally, the organization must prioritize. Not all departments deserve the same investment. Sovereignty should be focused first on processes whose disruption, loss of control, or forced change could seriously affect continuity, compliance, or competitive advantage.
The first step isn't to replace all vendors. It's to document dependencies and make informed decisions. Every new critical technology should include an assessment of portability, data, identity, exit costs, and alternatives.
The architecture can isolate proprietary services behind internal interfaces, maintain governed copies of critical information, and avoid distributing credentials or specific logic throughout the system. Open standards, APIs, and interoperable formats can be used where they provide a real advantage.
Selective diversification is also useful. A company can retain a primary supplier while maintaining alternatives for critical functions. In AI, it can periodically evaluate different models, even if it doesn't use them all in production.
Autonomy requires internal capabilities. If no one understands how the system works, the company will remain dependent on third parties even if it uses open technologies. Investing in architecture, documentation, and team knowledge is just as important as selecting vendors.
Sovereignty should not slow us down. It should prevent the current pace from eliminating future options.
In The Cloud Group We help organizations design cloud, data, and artificial intelligence architectures that leverage external services without unnecessarily surrendering control of the business.
Our approach begins by identifying critical applications, dependencies, data, integrations, and continuity risks. Based on this analysis, we design modernization, portability, integration, and resilience strategies tailored to each company's specific context.
We can combine managed services, open technologies, hybrid architectures, APIs, multi-model strategies, and backup mechanisms. The solution isn't always about using multiple clouds or hosting everything in-house. It's about selecting the right level of autonomy for each process.
We also incorporate data governance, observability, identity management, and exit plans by design. Because a company shouldn't discover the cost of its dependency during a downturn, a price increase, or a change in vendor terms.
Technology should enable growth. It shouldn't turn growth into a gradual loss of decision-making power.
It is an organization's ability to maintain sufficient control over its data, systems, identities, and technology decisions. This includes knowing where information is processed, who can access it, and how to maintain operations or switch providers when necessary.
No. A company can maintain sovereignty by using cloud services and external providers. The important thing is to retain choice, data control, appropriate contracts, portability, and business continuity plans.
It is a technological dependency that makes switching providers difficult or costly. This can stem from proprietary services, data formats, integrations, contracts, specialized skills, or functions that are difficult to replicate.
Not automatically. A multicloud strategy can reduce certain dependencies, but it can also increase costs and complexity. It must address specific business objectives, such as continuity or compliance.
It's possible. AI applications rely on models, APIs, tools, data, prompt formats, and evaluation systems. The more proprietary features you use from a single vendor, the higher the migration cost can be.
Not on their own. They offer greater inspection and deployment capabilities, but they require infrastructure, support, security, and technical expertise. The company must evaluate the total cost and its capacity to operate them.
Through documented formats, governed copies, data contracts, catalogs, lineage, and periodic export and restore testing. Downloading files does not always guarantee their usability on another system.
It involves using different AI models depending on the task, cost, risk, or required quality level. This can improve flexibility and reduce dependency, but it also demands more evaluation and observability.
It must include data and dependency inventory, export formats, responsibilities, deadlines, costs, technical alternatives, access revocation, integration migration, and continuity testing.
The cloud, software as a service, and artificial intelligence enable companies to innovate at an extraordinary pace. Rejecting these technologies to avoid any dependence would be unrealistic and, in many cases, detrimental to competitiveness.
The real challenge is to use them without surrendering all future decisions.
An organization loses sovereignty when it doesn't know where its data is located, can't replace a model, is unaware of its integrations, or depends on a platform for processes that have no alternative. This dependency can remain invisible for years because everything seems to be working correctly. It only becomes apparent when the price changes, a service fails, new regulations are introduced, or the provider modifies its terms.
That's why sovereignty must be designed before a crisis occurs. It requires modular architecture, portable data, controlled identities, appropriate contracts, internal knowledge, and proven continuity plans.
Not all dependencies need to be eliminated. Some are valid strategic decisions. But they must be accepted with information, boundaries, and a real understanding of the exit costs.
Companies that build this capability will be able to leverage the best available technology without becoming locked into it. They will be able to change models, suppliers, and platforms when the business needs it, not just when the supplier allows it.
Because in the next stage of digital transformation, the advantage will not only be in adopting Artificial Intelligence faster.
It will be possible to use it without relinquishing control of the business's future.
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