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Assess an AI vendor as a contextual supplier, not as a country label or a model demo. Start with the use case and the harm a confidentiality, integrity, or availability failure could cause. Then document the vendor’s ownership and jurisdiction, map models, data, software, infrastructure, hardware, and subcontractors, verify claims with evidence, compare realistic alternatives, contract for controls, and monitor changes throughout the relationship. NIST’s July 2026 SP 1326 organizes supplier due diligence around Foreign Ownership, Control, or Influence (FOCI), provenance, resilience, foundational cyber practices, and supply-chain tiers. AI-specific guidance adds requirements for data handling, model changes, third parties, and contingency planning.
What is geopolitical and supply-chain risk in an AI vendor?
Geopolitical risk is the possibility that ownership, government influence, jurisdiction, sanctions, export controls, political conflict, or cross-border operations could affect an AI service or expose your information. Supply-chain risk is the possibility that a dependency anywhere in the delivery chain—such as a dataset, model, software library, cloud region, chip supplier, or subcontractor—could be compromised, unavailable, changed without notice, or used in a way that harms your organization.
An AI vendor is therefore more than the company named on an invoice. Its service may depend on foundation-model providers, fine-tuning data, annotation firms, open-source libraries, application programming interfaces, cloud infrastructure, networking, accelerators, firmware, support contractors, and monitoring tools. A weakness or legal demand affecting one of those layers can affect your deployment even when the front-end vendor appears secure.
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“Due diligence research is the investigative process of researching all available, pertinent information about a given supplier or product so that informed decisions can be made on new acquisitions or existing systems.” — NIST SP 1326
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This is a procurement and risk-management method, not a legal conclusion about a particular country or provider. Privacy duties, sanctions, export controls, government-access powers, and sector rules depend on the service, data, deployment location, and current law.
How do I set the scope before investigating a vendor?
Define the business use and the consequence of failure first. The same provider can be acceptable for a low-sensitivity drafting assistant and unacceptable for a system that handles regulated records or controls a critical process.
- Describe the function. Record what the AI does, who relies on it, whether it makes recommendations or takes actions, and which systems it can call.
- Classify information. List prompts, uploaded files, retrieved documents, outputs, logs, telemetry, credentials, and support data. Mark personal, confidential, regulated, export-controlled, or commercially sensitive material.
- Describe failure consequences. Consider loss of confidentiality, manipulated outputs, service outage, unsafe automation, legal non-compliance, financial loss, safety impact, and reputational damage.
- Set a risk tolerance. Decide which evidence, contractual protections, technical isolation, and fallback capability are mandatory for this use case.
- Identify privileged access. Note whether the vendor can administer production systems, access customer tenants, change models, alter prompts or policies, or reach internal networks.
Use the result to determine how much detail to demand. A universal vendor score or country pass/fail rule cannot capture these differences; the reviewed government frameworks support a risk-based assessment tied to the deployment’s sensitivity and consequences.
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Request a current architecture and dependency view, then trace every component that can access your content, influence outputs, or affect availability. Include lower tiers when they materially affect security or continuity.
| Layer | What to identify | Questions to resolve |
|---|---|---|
| Application and service | Product, tenant architecture, administration, support channels, APIs, plug-ins | Who can change configuration or access a customer environment? Which functions are outsourced? |
| Models | Base models, fine-tuned versions, routing models, safety filters, version identifiers | Which model handles each request? How are versions tested, approved, rolled back, and announced? |
| Data | Training, fine-tuning, retrieval, evaluation, telemetry, and support data | Where did data originate? Who can access it? Is customer content retained or used for training? |
| Software | Open-source and commercial libraries, containers, agents, build systems, dependencies | How are vulnerabilities found and remediated? Is there a component inventory or software bill of materials? |
| Infrastructure | Cloud providers, regions, networks, storage, identity, backup, observability | Where is data stored, processed, supported, and remotely accessed? What happens if a region fails? |
| Hardware | Accelerators, servers, firmware, networking equipment, replacement parts | Are critical components single-sourced? Can equivalent capacity be obtained elsewhere? |
| Third parties | Subprocessors, annotators, managed-service firms, support vendors, lower-tier suppliers | Which parties see organizational content or control important functions, and how are they monitored? |
Ask the vendor to identify the parties that can read prompts, outputs, logs, or support tickets, not merely the companies that provide infrastructure. The Australian government guidance warns that each component can introduce vulnerabilities or compromise confidentiality, integrity, or availability. Record unknown dependencies as evidence gaps rather than assuming they are harmless.
How should I examine ownership, control, influence, and jurisdiction?
Document the legal entity delivering the service and the practical paths through which another actor could direct it or obtain access. Incorporation country is one clue, not a verdict.
Build an ownership record
- Legal name, registration jurisdiction, headquarters, and central management location.
- Controlling shareholders, voting rights, parent companies, and material ownership changes.
- Board members, government ties, state ownership, and contractual control rights.
- Locations of engineering, operations, support, security, and incident-response teams.
- Entities that can compel, direct, or technically enable access to systems or data.
Multinational ownership can be difficult to characterize. Check official corporate documents and validate them against reliable independent sources. Distinguish legal ownership from operational control: a company may be incorporated in one country, managed in another, host data in several regions, and rely on staff or subprocessors elsewhere.
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For every relevant jurisdiction, ask which laws could compel disclosure, restrict operation, require data localization, limit encryption, or affect the export of models and hardware. Determine what a government or other actor could actually reach: stored content, encryption keys, logs, administrative interfaces, support channels, source code, or physical infrastructure. Then record mitigations such as customer-held keys, regional processing, strict access controls, separation of tenants, or a different deployment model.
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Do not convert a nationality into a simplistic safe or unsafe classification. State the evidence, the unresolved uncertainty, the data and systems exposed, and the compensating controls. Recheck the analysis after ownership changes, acquisitions, new operating locations, or changes in applicable law.
What evidence should an AI vendor provide?
Ask for documentary evidence proportionate to the use case instead of accepting broad assurances. Evidence should be current, attributable to the service you will buy, and detailed enough to test the vendor’s claims.
| Evidence area | Useful records | What the record should show |
|---|---|---|
| Ownership and jurisdiction | Corporate filings, ownership chart, operating-location list, legal analysis | Who controls the provider, where decisions occur, and which laws may apply |
| Provenance | Model cards, dataset lineage, supplier list, version history, dependency inventory | Origins, licensing or usage rights, changes, and parties in the chain |
| Security development | Secure-development procedures, vulnerability-disclosure policy, remediation records | How weaknesses are reported, prioritized, fixed, and communicated |
| Access protection | Identity architecture, privileged-access controls, logging, key-management description | Who can access content or operate production systems, and how actions are recorded |
| Incidents | Incident-response plan, notification procedure, relevant history, lessons learned | Detection, containment, customer notification, recovery, and corrective action |
| Data handling | Retention schedule, deletion procedure, training-use settings, processing locations | What is stored, for how long, who sees it, and how deletion is verified |
| Independent assurance | Audit reports, testing summaries, attestations, penetration-test scope | What was examined, by whom, when, and which exclusions apply |
| Change management | Model-release process, subprocessor register, change-notice policy | How material changes are approved, tested, announced, and reversed |
A certification can support an assessment but does not establish that every model, region, subprocessor, or data flow is covered. Check the scope, period, exceptions, and corrective actions in any assurance report.
How do I verify model, data, and software provenance?
Provenance means being able to explain where a component came from, how it changed, and who handled it. For an AI service, request model and data lineage alongside ordinary software dependency information.
- Obtain model names, versions, release dates, fine-tuning methods, safety-layer versions, and retirement policy.
- Ask for the origin and permitted use of training, retrieval, evaluation, and feedback data, including licensed and third-party material.
- Determine whether prompts, files, outputs, ratings, or support conversations are retained, reviewed by people, or used to train or improve a model.
- Request a component or dependency inventory and the process for identifying vulnerable libraries, containers, build tools, and hosted services.
- Ask how artifacts are authenticated, protected from tampering, tested before release, and linked to a reproducible version record.
- Require notice when a model, dataset, dependency, hosting provider, or subprocessor changes in a way that could affect risk.
For generative AI, insist on both contractual and technical controls for training use. A statement that content is “not used for training” should specify which products and support channels it covers, whether humans can review content, how exceptions are handled, and how the setting is enforced.
How do I evaluate cybersecurity and incident response?
Evaluate the vendor’s ability to prevent, detect, contain, and explain compromise across the whole chain. Focus on controls that protect your tenant and data, not just the provider’s corporate network.
Access and isolation
Ask how administrators authenticate, whether privileged access is time-limited and logged, how customer tenants are isolated, and how support access is approved. Clarify key ownership, encryption boundaries, secrets management, and the vendor’s ability to revoke access quickly.
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Review the disclosure channel, severity criteria, remediation targets, compensating controls, and customer notification rules. Ask whether the process includes models, prompts, agents, open-source packages, infrastructure images, firmware, and subprocessors.
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Incident handling
Request the incident-response workflow, escalation contacts, evidence-preservation practices, and notification triggers. Determine whether the vendor will tell you about incidents at a lower-tier supplier when your data or service could be affected. Review a relevant incident history if available, including root cause and corrective action, rather than treating a clean public record as proof that no incident occurred.
Behavior and integrity monitoring
Ask how unusual model behavior, data exfiltration, prompt injection, unauthorized configuration changes, and unexpected outbound connections are detected. The Australian guidance recommends monitoring for unusual behavior or changes; require the vendor to explain what is monitored, who investigates, and what customers can see.
How do I test resilience, continuity, and substitutability?
Assess what happens when a critical model, cloud region, hardware source, dataset, or subcontractor is unavailable or compromised. Resilience is more than a backup statement: it includes recovery, alternatives, portability, and an executable exit.
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- Recovery: Ask for recovery objectives, backup frequency, restoration testing, dependency on a particular region, and communication during an outage.
- Concentration: Identify single points of failure, such as one model provider, chip family, cloud account, data source, or support contractor.
- Substitution: Determine whether another model, region, infrastructure provider, or supplier can perform the required function without unsafe behavior or unacceptable rework.
- Portability: Verify export formats for prompts, outputs, embeddings, fine-tuning data, configurations, audit logs, and metadata. Test an export instead of relying on a promise.
- Deletion: Establish how production data, backups, replicas, caches, and supplier copies are deleted and how completion is evidenced.
- Transition: Define assistance, documentation, notice periods, temporary continued service, and cooperation with a replacement provider.
NIST SP 1326 includes resilience and supply-chain tiers, while NIST’s generative-AI profile recommends contingency processes for high-risk third-party failures. A provider that cannot explain how you would operate during a prolonged outage or forced exit creates a strategic dependency even if day-to-day performance is excellent.
How should I compare multiple AI vendors?
Compare only providers that can actually satisfy the use case, and weight dimensions according to the consequences of failure. Do not let a single proxy—such as nationality, a logo, or a certification—decide the purchase.
| Dimension | Compare | Higher-risk indicators |
|---|---|---|
| FOCI and jurisdiction | Ownership, control, management locations, legal exposure, access pathways | Opaque control, unexplained government ties, or unresolved access authority |
| Visibility and provenance | Supplier map, model and data lineage, versioning, change disclosures | Unknown lower tiers, no version history, or refusal to identify material dependencies |
| Cybersecurity | Access controls, secure development, vulnerability response, incident evidence | Unscoped attestations, weak notification terms, or no verifiable remediation process |
| Resilience | Recovery, alternatives, portability, deletion, transition support | Single-region or single-supplier dependence with no tested exit |
| Contractual accountability | Data use, retention, subprocessors, audit, notice, remedies | Unilateral changes, no audit path, or vague data-use language |
| Use-case fit | Controls matched to sensitivity, criticality, performance, and access | Controls that look strong generally but do not cover your deployment |
Record the evidence supporting each comparison and distinguish “not provided” from “not applicable.” A missing answer may be acceptable for a low-impact pilot but should block or constrain a critical deployment until resolved.
What should the AI vendor contract require?
Convert the assessment into enforceable duties. Terms should cover the service you will use and the material dependencies that can affect it.
- Permitted uses of prompts, files, outputs, logs, telemetry, and support records; explicit limits on training and product improvement.
- Retention periods, processing and support locations, deletion from active systems and backups, and evidence of deletion.
- Security controls for identity, privileged access, encryption, tenant isolation, logging, vulnerability management, and secure development.
- Incident, vulnerability, and government-request notification, with timelines and required information for investigation.
- Disclosure of subprocessors and material lower-tier suppliers, advance notice of additions or replacements, and an objection or termination mechanism where appropriate.
- Notice and documentation for material model, data, hosting, hardware, policy, or dependency changes; include rollback or mitigation expectations.
- Practical evidence and audit rights, including access to relevant assurance reports, testing summaries, and remediation information.
- Continuity commitments, recovery obligations, portability formats, transition assistance, and cooperation with a substitute provider.
- Remedies for failure to meet security, data-use, notification, or exit commitments, calibrated to the service’s impact.
Have legal and privacy specialists review provisions that depend on local law. Contract language cannot eliminate a government’s authority, but it can require disclosure, limit voluntary access, preserve evidence, and give you a workable exit.
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How do I keep the assessment current?
Supplier due diligence is an ongoing control. Maintain an inventory of every third party that can access organizational content or control an important system function, assign an accountable owner, and review each relationship according to criticality.
Trigger-based reviews
- Ownership, merger, acquisition, board-control, or operating-location change.
- New model version, training source, hosting region, infrastructure provider, or subprocessor.
- Material law, sanctions, export-control, or government-access change.
- Security incident, vulnerability, unusual model behavior, or unexplained service degradation.
- Change in the data processed, user population, privileged access, or business consequence.
- Failure of a recovery test, portability exercise, deletion verification, or contractual obligation.
Ongoing records
Keep the architecture and supplier map versioned. Link each decision to evidence, record accepted uncertainties and compensating controls, track open remediation items, and document who approved residual risk. Test an export and an outage procedure periodically; an untested plan is an assumption, not a capability.
The UK Code of Practice for the cybersecurity of AI is voluntary and addresses security through design, development, deployment, maintenance, and end of life. Its lifecycle approach supports reviewing supply-chain security and communicating intended model updates rather than treating procurement as a one-time event.
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Use these questions in a written request and require answers that identify the service, region, version, and responsible entity.
- Which legal entity provides the service, who ultimately controls it, and where are management and operations located?
- Which governments’ laws may compel access to the service or data, and what data, systems, or support channels could be reached?
- Which models, datasets, libraries, infrastructure providers, hardware sources, and subprocessors are material to delivery?
- Where are prompts, outputs, logs, and other customer data stored, processed, accessed, and retained?
- Is customer content used for training or product improvement? Can that use be disabled contractually and technically?
- How are model versions, data sources, dependencies, and material supplier changes recorded and communicated?
- What independent evidence supports security, vulnerability handling, and incident-response claims?
- What are the recovery, portability, deletion, transition, and substitute-provider arrangements?
- What notice, audit, remediation, and termination rights will the contract provide?
What are the most common assessment mistakes?
Relying on incorporation country
Why it fails: It ignores ownership, management, data flows, subcontractors, and technical access. Fix: map control and jurisdiction pathways and document the actual exposure.
Reviewing only the named vendor
Why it fails: Models, data, cloud services, hardware, and lower-tier suppliers can introduce independent failure modes. Fix: request a dependency view and include material tiers.
Accepting marketing assurances
Why it fails: General statements may exclude support systems, backups, training use, or a different product edition. Fix: demand scoped records, dates, responsible entities, and testable contract terms.
Ignoring change
Why it fails: A model, subprocessor, region, law, or threat can change after approval. Fix: establish notice duties, inventory updates, and trigger-based reassessment.
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Why it fails: Proprietary formats, embeddings, workflows, or supplier concentration can make replacement impractical during a crisis. Fix: define export, deletion, transition assistance, alternatives, and test them.
Treating a certification as the decision
Why it fails: An assurance may cover only selected systems and a past period. Fix: read scope and exceptions, then add use-case-specific evidence and controls.
How should I make the final decision?
Approve the vendor only when the evidence, controls, and residual risk fit the defined use case. A practical decision record should state the deployment, data classes, dependencies, ownership and jurisdiction findings, unresolved gaps, required contract terms, contingency plan, accountable approver, and date for the next review.
Possible outcomes are approval, approval with technical or contractual conditions, a restricted pilot using non-sensitive data, or rejection until material gaps are closed. Explain the choice in terms of exposure and consequence—not a country stereotype—and preserve the evidence so a later reviewer can understand what changed.
Frequently Asked Questions
Can a security certification replace geopolitical due diligence?
No. A certification or attestation has a defined scope, period, and set of exclusions; it may not cover ownership, government-access exposure, model provenance, lower-tier suppliers, or your chosen deployment. Use it as one evidence item and verify the gaps separately.
What should we do if an AI vendor refuses to name subprocessors?
Treat the refusal as a visibility and concentration risk. Ask for at least the categories, locations, access levels, and change-notice process; restrict the data or deployment until the missing information is resolved, or select a provider that offers sufficient transparency.
Does keeping data in our country eliminate geopolitical risk?
No. Data residency addresses one storage or processing location, but control may still sit elsewhere and administrators, support teams, models, software, hardware, or legal obligations may cross borders. Trace both the physical data path and the people and entities that can reach it.
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