Start with an end-to-end dependency inventory, not a country label. For every material AI use case, map the model, runtime, cloud and compute, data and retrieval systems, application, network, software and hardware supply chain, support arrangements, and governance. For each layer, record who controls it, which jurisdictions and legal regimes may affect it, where data is processed or accessed, which subcontractors matter, what evidence supports the answer, and what happens if access is interrupted.
Then prioritize dependencies by business consequence, concentration, legal and operational exposure, replaceability, and recovery time. Test an actual migration or failover path. The practical goal is managed interdependence: remove unacceptable single points of failure and exposure while retaining useful international suppliers and interoperability. Complete self-sufficiency is not a realistic default for most organizations.
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What dependence on foreign AI models and infrastructure actually means
Dependence is broader than the nationality of a model developer or the country containing a data center. An organization can run an apparently local application on a model governed by a foreign parent, proprietary software updated from abroad, overseas accelerators, a foreign support team, or subcontractors that can access telemetry and logs. Conversely, an international provider may offer strong technical controls that reduce a specific risk. Assess the actual control and exposure rather than treating “foreign” or “domestic” as a complete verdict.
Brookings defines AI sovereignty as “a spectrum of strategies to enhance a country’s capacity to make independent decisions about critical AI infrastructure deployment, use, and adoption, rather than literal autarky.” For a business, that means knowing which decisions it can still make if a supplier changes terms, loses access to a region, is compelled by a foreign authority, suffers an outage, or withdraws support.
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A useful assessment answers five questions for every critical dependency:
- Control: Who can change, suspend, update, access, or terminate the capability?
- Exposure: Which laws, jurisdictions, subcontractors, and technical channels could affect data or operations?
- Concentration: How many important workloads, regions, models, and suppliers rely on the same provider or upstream component?
- Substitutability: Can another model, platform, or operator serve the workload with acceptable quality and cost?
- Resilience: How quickly can the organization recover, and has recovery been demonstrated?
Step 1: Create an inventory for each material AI use case
Begin with business outcomes, not vendor catalogs. List production, customer-facing, safety-relevant, regulated, and financially significant uses. Include internal copilots and automation when their outage could stop a process or expose sensitive information. Give each use case an owner and a short description of the decision or work it supports.
- Name the process and application. Record the business owner, users, criticality, service hours, regulatory context, and acceptable outage and degradation limits.
- Identify the model and version. Capture model name, version or snapshot, provider, license, fine-tuning method, evaluation tooling, and whether the provider can silently change the model or safety behavior.
- Record the runtime path. Document inference or training services, orchestration, containers, accelerators, host account, region, availability zones, and any managed endpoint that sits between the application and model.
- Map data and retrieval. Include source systems, vector stores, prompt and response logs, evaluation sets, fine-tuning data, backups, disaster-recovery copies, and human review queues.
- Include shared services. Add identity and access management, encryption-key services, networking, DNS, monitoring, incident response, ticketing, billing, and support channels. A model may remain available while one of these dependencies prevents use.
- Attach evidence and unknowns. Link each assertion to a contract clause, architecture record, provider attestation, audit report, configuration export, or test result. Mark information that is unavailable instead of inferring origin or control.
Keep the inventory versioned. Record the date, service region, model release, subprocessors, and major architecture changes so that a later review can distinguish a new risk from stale documentation.
Map the complete service path
A dependency map should be readable by both an engineer and a risk owner. The following layers are a practical minimum; add industry-specific components such as robotics controllers or medical-device software when they affect continuity.
| Layer | What to record | Failure or exposure to test |
|---|---|---|
| Business process and application | Purpose, owner, users, decision rights, integrations, service-level objectives | Process stops, unsafe fallback, incorrect output, or inability to approve a change |
| Model and runtime | Model provenance, versioning, license, inference/training engine, fine-tuning, evaluation and safety controls | Model withdrawal, unannounced update, incompatible API, quality loss, or licensing restriction |
| Cloud and compute | Account, region, zones, virtual machines or managed service, accelerators, quotas and capacity reservations | Regional outage, export restriction, quota denial, hardware scarcity, or account suspension |
| Data and retrieval | Source data, indexes, embeddings, prompts, logs, backups, retention, deletion and recovery copies | Unauthorized access, unavailable data, non-portable indexes, incomplete deletion, or cross-border transfer |
| Network and identity | Connectivity, private links, DNS, identity provider, certificates, keys, secrets and policy engines | Authentication failure, blocked route, key-service outage, or inability to rotate credentials |
| Software and hardware supply chain | Operating systems, libraries, containers, firmware, accelerators, update channels, suppliers and sub-suppliers | Vulnerable or revoked component, opaque update, counterfeit hardware, or unavailable replacement |
| Support and governance | Contracting entity, support locations, escalation, incident notice, audit rights, change control and termination terms | No timely remedy, foreign legal demand, unilateral suspension, or evidence unavailable during an incident |
Trace both the normal path and the exceptional path. A support engineer, backup operator, security-monitoring service, or disaster-recovery account may cross a jurisdictional boundary even when primary inference stays in the organization’s preferred region.
Identify who controls each layer
Use the legal contracting entity as a starting point, not the conclusion. Record the ultimate decision-making control where it is known, parent or control relationships, the service operator, critical subcontractors, support locations, and parties that can alter or stop service. A familiar brand name does not establish legal control, and a local data center does not prove local governance.
Ask providers and internal teams for evidence of:
- Who approves model, software, firmware and infrastructure changes.
- Who can access production systems, logs, prompts, weights, keys and backups, including emergency access.
- Which entities can compel disclosure or operational assistance under applicable law.
- Where administrators and support staff are located and whether access is remote, just-in-time, or persistent.
- Which subcontractors operate storage, networking, security monitoring, data labeling, support or hardware maintenance.
- What happens if ownership, control, licensing, or a key subcontractor changes.
Put unresolved ownership or control questions in the risk register with an evidence owner and deadline. “Not stated” is a useful finding; an unsupported assumption is not.
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Build a data-flow record for prompts, outputs, training and fine-tuning data, embeddings, telemetry, logs, backups and support artifacts. For each flow, distinguish processing location, storage location, backup location and human-access location. A service may process in one region, replicate to another, and expose diagnostic data to a support team elsewhere.
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Document the data category and sensitivity, transfer mechanism, retention period, deletion workflow, and evidence that deletion occurred. Identify whether customer-controlled keys protect data at rest and in use, who operates the key service, and whether the provider can access plaintext. Check whether prompts and outputs are used for provider training, abuse monitoring or product improvement under the applicable service terms.
Verify the architecture with configuration records, access logs, provider reports and a controlled test where possible. Contractual language is important but does not by itself prove runtime location or practical deletion. Counsel must assess the organization’s actual jurisdictions, contracts, data categories and transfer mechanisms; this assessment does not determine whether a particular transfer is lawful.
Assess software, hardware and supply-chain dependence
Model provenance is only one part of technical sovereignty. Record who develops and maintains the model, tokenizer, inference engine, orchestration layer, container images, drivers, firmware and security patches. Note proprietary APIs, closed weights, license restrictions, required accelerators, compiler toolchains and specialized networking.
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- Is the source, build process, vulnerability information or operational documentation available to the organization or an independent assessor?
- Are interfaces documented and based on widely adopted standards, or do they require a provider-specific API and data format?
- Can an alternative accelerator, operating system, model format or runtime execute the workload?
- Can the organization obtain replacement hardware, firmware and updates if a supplier or country becomes unavailable?
- Do contracts provide meaningful audit rights over suppliers and sub-suppliers?
Do not guess at country of origin when the supplier cannot provide traceability. State which parts of the chain are known, partially known or unknown, and treat opacity itself as an exposure when the component is critical.
Use eight assessment dimensions
The European Commission’s Cloud Sovereignty Framework implementation guidance groups assessment into eight objectives. They are reference points, not universal legal requirements or a certification for an organization or provider.
| Objective | Questions for an organization | Evidence to request |
|---|---|---|
| Strategic | Can the service continue if support is withdrawn? Could ownership or control change? Who has final decision authority? | Ownership and control disclosures, continuity plan, change-notice terms and internal operating capability |
| Legal and jurisdictional | Which laws govern operations and contracts? Could a foreign authority compel access to data or systems? Which international regimes constrain transfer or use? | Contracting entities, processing locations, legal opinions, transfer assessments and government-request process |
| Data and AI | Who controls keys? Can the organization see when, where and by whom data and models are accessed? Can it verify deletion and understand model governance? | Key architecture, access logs, deletion evidence, model cards, training-use terms and audit reports |
| Operational | Can in-scope staff run and support the service without the foreign provider? Are documentation, source code or know-how available? | Runbooks, administrator access, staffing plan, escrow or export arrangements and recovery exercise results |
| Supply chain | Are hardware origin, firmware and software provenance visible? Can suppliers and sub-suppliers be audited? | Bill of materials, provenance records, update process, supplier map and audit rights |
| Technology | How proprietary are interfaces and formats? Can data, prompts, indexes and model configurations move to alternatives? | API specifications, export tools, format documentation, compatibility tests and portability scripts |
| Security and compliance | What certifications and regulatory evidence apply? Who controls monitoring and logs? Can fixes be developed and applied without a vendor bottleneck? | Independent assessments, vulnerability process, incident history, log access and patch commitments |
| Environmental sustainability | What measurable energy, water, emissions and hardware-lifecycle practices affect capacity and resilience? | Environmental reporting, site and capacity data, lifecycle policies and relevant assurance |
Environmental information belongs in the assessment when sustainability, capacity or continuity objectives make resource constraints material. It should not be treated as a substitute for security or legal analysis.
Rank dependencies without pretending there is a universal score
The cited frameworks provide dimensions and criteria, not an organization-independent numerical threshold. Build a transparent prioritization method that your risk committee can explain and adjust. A practical register records:
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- Duration: the effect of an outage lasting minutes, days or an extended period.
- Concentration: how many use cases, regions or critical suppliers share the same provider, account, model family, accelerator or network.
- Evidence strength: verified architecture and test results versus contractual assertion or unknown ownership.
- Replaceability: available alternatives, data and code portability, quality difference, staff skills and migration effort.
- Exposure: jurisdictional reach, privileged access, transfer routes, subcontractor opacity and update control.
Assign an accountable owner, treatment, target date, evidence required and reassessment trigger. Triggers can include a model or region change, ownership transaction, new subprocessors, a material incident, a legal change, a capacity warning, or a failed recovery test.
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| Priority pattern | Typical treatment |
|---|---|
| High consequence, high concentration, weak evidence and difficult replacement | Reduce concentration, obtain independent evidence, establish a second path and schedule an exit exercise |
| High consequence, strong controls but long migration time | Maintain tested exports, reserve capacity, train operators and rehearse a time-bounded migration |
| Moderate consequence with easy substitution | Document alternatives, monitor provider changes and test compatibility during normal releases |
| Low consequence or non-critical experiment | Accept with an owner, data restrictions and a review date rather than imposing costly architecture |
Could we switch AI providers if one became unavailable?
Answer this with an exercise, not a promise. An API that looks portable may conceal provider-specific prompt templates, safety settings, embeddings, tool calls, rate limits, evaluation data and billing controls. A contract termination clause proves a legal right to leave; it does not prove that data, indexes, model configuration, skills and capacity can move in time.
- Define the scenario. Choose a regional outage, account suspension, legal restriction, model retirement or supplier withdrawal. Set a recovery-time and recovery-point target.
- Prepare exports. Export source data, prompts, responses needed for evaluation, indexes or rebuild inputs, model configuration, policies, secrets in a controlled process, logs and application code.
- Select an alternative. Use the same workload and evidence threshold. Record quality, latency, safety, cost, capacity, jurisdiction, key control and operational requirements.
- Rebuild the path. Deploy the alternative runtime, identity, networking, monitoring and data stores. Measure what must be rewritten rather than assuming API compatibility.
- Run acceptance tests. Compare accuracy, refusal behavior, latency, throughput, failure handling, data leakage controls and human-review requirements against the original service.
- Measure elapsed time and effort. Record staff hours, external help, degraded service, licensing costs, hardware needs and unresolved defects.
- Fix and repeat. Turn findings into portability work, reserve capacity, documentation and a scheduled retest.
Where a full second provider is uneconomic, create a smaller “minimum viable recovery” path for the most critical functions. It may use lower performance or manual processing, but its limits must be explicit and tested.
Compare alternatives on the same evidence
Evaluate a foreign provider, locally hosted deployment, regional operator and self-managed option against the same use case. A locally hosted service can still rely on foreign ownership, software, hardware, updates or support. An international provider may offer better resilience or security for a particular workload. The comparison should expose those trade-offs.
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|---|---|
| Jurisdiction and effective control | Who controls decisions, contracts, support and changes? Which legal regimes can reach the service? |
| Data and key control | Where are processing, storage, backups and access located? Who operates encryption keys and verifies deletion? |
| Security and compliance | Which independent evidence, certifications and incident commitments apply? Can assessors obtain access? |
| Supply-chain visibility | Can hardware, firmware, software and sub-suppliers be traced and audited? |
| Capability and resilience | Does the option meet quality, latency, capacity, availability and recovery requirements? |
| Interoperability and portability | Are interfaces, formats and export tools documented? Has a real migration or failover worked? |
| Local operating capacity | Can the organization staff, secure, patch and support the option without a single external bottleneck? |
| Transition cost and risk | What code, data, hardware, training, contracts and downtime are required, and what quality trade-offs result? |
Use the same test data, acceptance criteria and evidence standard for every option. Do not award a better sovereignty conclusion merely because a supplier uses a preferred geographic label.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Apply public frameworks carefully
European Commission Cloud Sovereignty Framework
The Commission’s implementation guidance provides criteria covering access control, auditability of AI use, data location, migration, supply-chain transparency, open interfaces, security controls and environmental reporting. Its eight objectives are useful for organizing an assessment, but applying the checklist does not certify an organization or provider, and the scope and scoring method belong to that Commission framework.
Cloud and AI Development Act policy proposal
The Commission’s current CADA policy page describes four assurance levels for public-sector use, based on risk assessment:
- Data processed and stored on infrastructure in the Union.
- Demonstrated independence from third countries and software supply-chain transparency.
- EU ownership and control with additional criteria.
- Full software supply-chain transparency and control with no third-country interference.
The page presents CADA as a proposal and says provider recognition follows audit. These levels are not automatically operative requirements or available certifications. Check the current legislative text, implementation status and any recognition process before relying on them in procurement or compliance claims.
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Commission procurement framework described in 2026
A Commission news explanation dated 1 June 2026 says a recent cloud procurement framework used 48 specific criteria and produced two complementary outputs: a Sovereignty Effectiveness Assurance Level and an overall sovereignty score. Those figures describe that Commission framework, not a universal benchmark or a score for your organization. Use the underlying guidance and current official materials for context rather than presenting the outputs as a generally recognized rating.
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Use the Brookings “stack” to find hidden dependencies
The Brookings Institution’s February 2026 report, Is AI Sovereignty Possible? Balancing Autonomy and Interdependence, describes dependencies across physical inputs, compute hardware, networks, digital infrastructure, data assets, models, applications, talent and governance. Map your inventory across all of those layers. A model swap will not solve a shortage of accelerators, a proprietary network fabric, a missing operations team or a legal restriction on data.
The report argues that full-stack autonomy is structurally infeasible for almost any country and recommends mapping dependencies by layer, prioritizing feasible interventions, diversifying suppliers and partners, and embedding interoperability and portability. Treat that as strategic analysis, not as a guarantee that diversification eliminates risk. Two suppliers can share the same upstream accelerator, software component, cloud region or legal exposure.
Turn findings into a control plan
For each high-priority dependency, choose one or more treatments:
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- Reduce: redesign the application, minimize sensitive data, separate critical functions, or remove an unnecessary proprietary feature.
- Substitute: adopt a compatible model, runtime, cloud, hardware platform or support provider after testing.
- Diversify: spread workloads across providers, regions, model families or supply routes while checking for shared upstream dependencies.
- Retain with safeguards: accept a dependency when its capability is valuable and exposure is understood, with monitoring, contractual protections, backups and a recovery path.
- Transfer or share: use insurance, contractual allocation or a managed operator only where those arrangements provide evidence and do not hide the underlying single point of failure.
Write a control statement that names the owner, deadline, evidence and trigger. For example, “The payments-assistance service will have a tested alternate inference path and an exportable evaluation set by 30 September; retest after any model-version or region change.” Avoid vague commitments such as “improve sovereignty.”
Operate the assessment as a continuing process
Provider regions, ownership, subprocessors, model terms, incident commitments and hardware availability change frequently. Set review intervals according to criticality, and review immediately after material changes. Procurement, security, privacy, legal, infrastructure, data science and business owners should share the register; no single team can see every dependency.
Monitor practical indicators: provider change notices, new subprocessors, region or support-location changes, model deprecations, quota and capacity warnings, unresolved incidents, failed portability tests, expiring licenses, key-management changes and supplier mergers. Keep an evidence package that an incident commander can use without waiting for a vendor response.
Reassess the threat model when the organization enters a new jurisdiction, adds sensitive data, changes the business process, adopts autonomous actions or moves from experimentation to production. Legal conclusions and regulatory requirements depend on the organization’s facts; obtain qualified advice for those questions.
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“The data center is in our country, so the service is independent.”
Fix: trace ownership, administrator access, keys, software updates, hardware, support and legal control. Residency addresses one location question, not operational or jurisdictional independence.
“The model is open, so switching is easy.”
Fix: test the complete runtime, drivers, accelerators, data formats, safety settings, evaluation suite, skills and capacity. Open weights do not remove every proprietary dependency.
“The contract guarantees portability.”
Fix: export and rebuild the workload, then measure time, cost, quality and data loss. Keep the exercise evidence and remediate failures.
“We will eliminate all foreign suppliers.”
Fix: prioritize unacceptable concentration and exposure. Full self-sufficiency can reduce capability and resilience; managed interdependence with alternatives may produce a better result.
“A provider questionnaire is enough.”
Fix: corroborate statements with architecture, logs, audit evidence, configuration, contract terms and controlled tests. Record unknowns explicitly.
“Diversification means two vendor logos.”
Fix: inspect common upstream clouds, accelerators, software, networks, support teams and legal regimes. Apparent diversity can conceal a shared failure mode.
What AI sovereignty means for a business
AI sovereignty is the ability to make and carry out critical decisions about AI use despite supplier, jurisdictional or infrastructure shocks. It is not a promise that every component will be produced locally. A business demonstrates sovereignty where it has clear control of sensitive data and keys, evidence about legal and operational exposure, enough in-house capability to operate or recover, alternatives that have been tested, and governance that detects change.
The most defensible outcome is a documented choice: which dependencies are accepted because they deliver important capability, which are reduced because their consequence or exposure is unacceptable, and how the organization will recover if a critical path fails. That choice should be specific to each use case, supported by evidence and revisited as the stack changes.
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