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Assess dependence as a control-and-resilience problem, not a map question. For every material AI use case, trace the full chain—model, runtime, cloud, data, application, network, hardware, software updates, support and governance. Record who controls each layer, which jurisdictions and subcontractors are involved, what happens if access stops, and how hard it would be to move. Then prioritize the dependencies with the greatest business consequence, concentration, legal or operational exposure, weak evidence and poor substitutability.
The practical objective is usually managed interdependence: remove unacceptable single points of failure and exposure while retaining useful international suppliers and interoperability. Full-stack self-sufficiency is not a realistic default for most organizations.
What “dependence on foreign AI” actually includes
A model’s country of origin is only one dependency. A model hosted in your country may rely on foreign ownership, proprietary software, overseas support, imported accelerators, remote updates or a parent company subject to another jurisdiction. Conversely, an international provider may offer strong controls for a particular threat. Assess the evidence and the effective control path rather than applying a simple foreign-versus-domestic label.
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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsFor each critical use case, map these layers:
- Business process and application: the decision, customer service, research, coding or internal workflow that uses AI, including its owner and recovery priority.
- Model and runtime: model name and version, training or fine-tuning arrangements, inference engine, safety services, evaluation tools and proprietary APIs.
- Cloud and compute: account, provider, region, availability zone, accelerators, orchestration, identity, key-management and disaster-recovery services.
- Data and retrieval: source systems, vector stores, prompts, fine-tuning sets, logs, backups, retention, deletion and retrieval pipelines.
- Application and network: gateways, DNS, connectivity, private links, rate limits, client libraries and dependencies on a single network or cloud.
- Software and hardware supply chain: operating systems, containers, firmware, chips, drivers, update channels, open-source components, suppliers and sub-suppliers.
- Support and governance: contract entity, administrators, support locations, incident response, audit rights, compliance evidence and people who can change, suspend or terminate service.
Include evaluation, fine-tuning, logging, backup and recovery components. A service that appears independent at the API layer may still fail when a logging provider, identity service, key system or model-update channel is unavailable.
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A repeatable assessment method
1. Select material AI use cases
Start with use cases whose interruption, compromise or forced migration could materially affect safety, revenue, legal obligations, customers or essential operations. Give each one a business owner, criticality description and recovery-time objective. Include pilots that are likely to become production systems; hidden dependencies often enter through experimentation accounts.
2. Build a service-path inventory
Document the path from user or machine request to result. The following minimum record is suitable for a spreadsheet or governance system:
| Record | Questions to answer |
|---|---|
| Use case | What process uses AI, who owns it, and what is the acceptable outage and degraded-service period? |
| Model | Which model and version, license, training or fine-tuning route, safety layer and evaluation service are used? |
| Runtime and host | Where is inference or training executed? Which account, region, cluster, accelerator and orchestration stack are involved? |
| Data path | What data enters prompts, retrieval, fine-tuning, logs and backups? Where is each copy stored and processed? |
| Access and keys | Who can administer systems or read data? Who controls encryption keys, identity, secrets and break-glass access? |
| Interfaces | Which APIs, SDKs, schemas, rate limits and proprietary features would have to be replaced? |
| Supply chain | Which software, hardware, firmware, suppliers and sub-suppliers are essential, and what is known versus unknown? |
| Support and change | Who can update, suspend, deprecate or terminate the service? Where are support staff and operators located? |
| Recovery | What alternative model, host or manual process exists, and has the switch actually been tested? |
Do not fill unknown origin or ownership with assumptions. Mark it “unknown,” identify the evidence owner and set a date for resolution. A data-center address is evidence of a processing location, not proof of legal or operational independence.
3. Identify effective control
Record the contracting entity, service operator, parent or control relationships where known, critical subcontractors, support locations and the parties able to change, suspend, update or terminate the service. A brand name may not identify the entity that operates the environment or makes decisions. Ask for ownership and control disclosures, subcontractor lists, administrator-location information, change-notification terms and continuity commitments.
4. Trace data and access, not just residency
For every data category, map collection, prompt submission, retrieval, inference, logging, backup, support access and deletion. Record the country or region for processing and storage at each step, including replicas and disaster-recovery sites. Establish:
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- who can access plaintext and metadata;
- whether customer-managed keys are available and who can use them;
- how privileged access is logged and independently reviewed;
- retention, deletion and recovery-copy behavior;
- which support or fallback route crosses jurisdictions; and
- what audit evidence proves these controls in practice.
“Stored in the Union” or “hosted locally” does not answer who controls keys, operators, software updates or compelled-access channels. Counsel must assess the actual contract, data categories, jurisdictions and threat model; this method does not determine whether a particular transfer is lawful.
5. Trace technical and supply-chain dependencies
Record model provenance and licensing, package registries, model-weight access, API gateways, drivers, accelerators, firmware and update channels. Ask whether interfaces use documented, widely adopted standards or proprietary formats. Identify where a supplier depends on another cloud, chip, network, identity service or support contractor. Require supplier and sub-supplier visibility proportionate to the use case, and distinguish verified facts from provider assertions.
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6. Test continuity and substitutability
For each dependency, describe what fails if access is interrupted and for how long. A credible substitute analysis covers:
- another model, runtime, cloud, region or manual process;
- data, prompts, embeddings, fine-tuning artifacts, code and configuration that must move;
- migration time, engineering effort and one-time and recurring cost;
- quality, latency, safety, language or capacity trade-offs;
- skills and permissions needed to operate the alternative; and
- whether the exit has been rehearsed under realistic conditions.
Contractual termination rights and an API that looks portable are not proof of practical reversibility. A migration may be blocked by proprietary tool calls, undocumented quotas, incompatible embeddings, unavailable model weights, export fees or a support dependency.
7. Prioritize and assign treatment
Rank dependencies using the same evidence threshold for every option. Consider business consequence, duration of loss, concentration, effective control, legal and jurisdictional exposure, security impact, evidence quality, replaceability and recovery time. The cited public frameworks provide dimensions, not an organization-independent numerical formula; do not invent a universal pass mark.
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For each high-priority item, assign an accountable owner, treatment, target date, evidence required and reassessment trigger. Treatments can include a second supplier, a second region, cached or local models, exportable data and configurations, stronger key separation, contractual audit rights, documented runbooks, staff training or a deliberately accepted residual risk.
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The European Commission’s Cloud Sovereignty Framework—Implementation guidance organizes assessment around eight objectives. Its criteria are useful reference points, not universal legal requirements or a certification of your organization or provider.
| Objective | Questions for an organization |
|---|---|
| Strategic | Who has ultimate decision authority? Could ownership or control change? Can the service continue if support is withdrawn? |
| Legal and jurisdictional | Which legal systems govern operations and contracts? Could an authority compel access to systems or data? Has counsel assessed the actual facts? |
| Data and AI | Who controls cryptographic access? Can you see when, where and by whom data and models are accessed? Can deletion be verified? Where are pipelines developed, trained, hosted and governed? |
| Operational | Can your operators run and support the service without the provider? Are documentation, source code or operational know-how available? Is migration feasible? |
| Supply chain | Are hardware origin, firmware and software provenance visible? Can you see suppliers and sub-suppliers and exercise audit rights? |
| Technology | How much depends on proprietary technology? Are interfaces documented and interoperable? Can configurations and artifacts be exported? |
| Security and compliance | What certifications and regulatory evidence exist? Who controls logs and monitoring? Can independent assessors obtain access? Can fixes be developed and applied without an external bottleneck? |
| Environmental sustainability | What measurable energy, water, emissions and hardware-lifecycle practices affect capacity, continuity and sustainability objectives? |
How do we know where our AI data is processed?
- List every data-bearing action: prompt and response traffic, retrieval, embeddings, fine-tuning, evaluation, telemetry, support tickets, backups and disaster recovery.
- Obtain configuration evidence: account and region settings, data-processing terms, subprocessors, replication settings, logging destinations and support-access controls.
- Verify the path technically: inspect network routes, service endpoints, access logs, key-use logs and workload placement; check that “regional” settings apply to all dependent services, not just inference.
- Confirm human access: identify provider operators, support locations, privileged roles, approval controls and emergency-access procedures.
- Reconcile evidence: compare contracts, architecture diagrams, logs, provider attestations and your own tests. Record gaps instead of treating a policy statement as proof.
Recheck after model upgrades, architecture changes, acquisitions, new subprocessors, support-policy changes or a move to a new region. Provider regions, subprocessors, model terms and incident commitments can change frequently.
Could we switch AI providers if one became unavailable?
Answer this with an exercise, not a promise. Choose a representative workload and run a controlled failover or migration to an alternative model or host. Measure the work needed to export data, prompts, embeddings, fine-tuning artifacts, policies, evaluation sets, monitoring and application configuration. Test authentication, rate limits, safety behavior, latency, cost, quality and rollback.
Minimum exit-exercise checklist
- Export data and metadata in documented, machine-readable formats.
- Recreate identity, keys, network controls and audit logging without the incumbent’s administrators.
- Deploy the alternative using version-pinned code and infrastructure definitions.
- Run a fixed evaluation set and compare accuracy, refusal behavior, latency and capacity.
- Exercise a degraded mode or manual process for the stated recovery period.
- Record elapsed time, staff hours, unresolved blockers, quality loss and additional cost.
- Update the runbook, contracts, architecture and owner assignment from the findings.
Repeat after major model, API, data or infrastructure changes. Portability is a capability that must be maintained, not a one-time procurement checkbox.
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How to compare mitigation options
Compare alternatives on the same use case and evidence standard. A simple decision table keeps trade-offs visible:
| Comparison axis | Evidence to collect |
|---|---|
| Jurisdiction and effective control | Contract entity, ownership and control, operating locations, change and suspension authority, applicable legal regimes. |
| Data and key control | Processing and storage locations, replication, access logs, customer key control, deletion verification and support paths. |
| Security and compliance | Independent assessments, certifications, vulnerability response, audit access, monitoring ownership and fix authority. |
| Supply-chain visibility | Hardware, firmware, software provenance, subcontractors, update channels and audit rights. |
| Capability and resilience | Quality, latency, capacity, availability, regional redundancy, safety behavior and recovery performance. |
| Interoperability and portability | Open interfaces, export formats, model and embedding portability, infrastructure-as-code and tested migration time. |
| Local operating capacity | Internal skills, documentation, source or operational know-how, support independence and training needs. |
| Transition cost and risk | Engineering effort, contracts, licensing, data movement, performance loss, dual-running cost and residual exposure. |
A second provider may reduce concentration but add operational complexity. A locally hosted model may improve control while increasing hardware, staffing, patching and capability burdens. A foreign service may be the best fit for a workload if its controls and resilience are demonstrably stronger. State the trade-off and residual risk rather than claiming that any option is inherently sovereign.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What public frameworks can—and cannot—tell you
European Commission Cloud Sovereignty Framework
The implementation guidance groups criteria into the eight objectives above, including access control, auditability of AI use, data location, migration, supply-chain transparency, open interfaces, security controls and environmental reporting. Applying its checklist does not certify an organization or provider, and its scope and scoring method belong to that Commission framework.
Cloud and AI Development Act (CADA) policy proposal
The Commission’s 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. Treat these levels as EU policy context, not currently universal requirements or automatically available certifications. Verify legislative status and implementation details before using them in procurement or compliance claims.
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Commission procurement example
A Commission news explanation dated 1 June 2026 describes a recent cloud procurement framework using 48 specific criteria and two complementary outputs: a Sovereignty Effectiveness Assurance Level and an overall sovereignty score. Those figures describe that Commission framework, not a general benchmark or a score for your organization.
Brookings strategic analysis
The Brookings Institution’s February 2026 report, Is AI Sovereignty Possible? Balancing Autonomy and Interdependence, maps the stack across physical inputs, compute hardware, networks, digital infrastructure, data assets, models, applications, talent and governance. It 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.” Its recommendations—map dependencies by layer, prioritize feasible interventions, diversify suppliers and partners, and embed interoperability and portability—are strategic analysis, not a guarantee that diversification eliminates risk.
What does AI sovereignty mean for a business?
For a business, sovereignty is the practical ability to make critical decisions and keep essential AI services running without an unacceptable external veto, legal exposure or technical lock-in. It does not require owning every chip, model or data center. It means knowing which dependencies matter, maintaining credible alternatives where the consequences justify them, controlling sensitive access, and being able to recover within the business’s required time.
Use the assessment to distinguish three decisions: reduce a dependency that creates unacceptable concentration or exposure; contain one that is useful but needs segmentation, key separation or a fallback; or accept a documented residual risk because the capability and cost trade-off is favorable. Reassess when ownership, law, regions, subprocessors, model terms, architecture or threat conditions change.
Common assessment mistakes
- Equating location with control: a local region does not establish independent ownership, operations, software or legal exposure.
- Scoring nationality instead of dependency: country of origin is one input among control, concentration, substitutability and resilience.
- Ignoring non-production systems: evaluation, observability, backups and support can carry sensitive data and become outage bottlenecks.
- Trusting a contract as an exit plan: rights without export formats, staff, time and tested procedures are not reversibility.
- Accepting opaque supply chains: unknown hardware, firmware, updates or sub-suppliers should be recorded as uncertainty and treated according to impact.
- Using framework labels out of context: EU levels and Commission scores are not universal law, certification or a substitute for organization-specific analysis.
- Promising total autonomy: attempt targeted risk reduction and diversified interdependence, then document the residual dependence that remains.
A practical operating cycle
- Inventory: refresh the use-case and dependency register at least when architecture or supplier changes occur.
- Evidence: attach contracts, diagrams, logs, attestations, test results and unresolved questions to each material dependency.
- Prioritization: review consequence, concentration, control, exposure, evidence quality, replaceability and recovery time with business and technical owners.
- Treatment: fund the selected mitigation, assign an owner and set a measurable target date.
- Exercise: perform migration, failover or degraded-mode tests and record actual effort and performance.
- Reassessment: trigger a new review after provider ownership changes, legal developments, incidents, major model releases, new subprocessors or material data-flow changes.
This cycle turns “foreign AI dependence” from a political label into a managed risk register with evidence, owners and recovery capability.
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