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Yes—but only if you manage the provider as a critical supplier. Before putting an important workflow on one private AI provider, classify the dependency, contract for security and data rights, verify controls with evidence, monitor regulatory duties, and rehearse a workable exit. If the provider cannot show those capabilities, keep the use case low-impact or maintain a second provider, a local model, or a manual fallback.
What dependence actually means
Dependence is not determined by whether a provider calls its service private, hosted, dedicated, or enterprise. It is determined by what stops when the service is unavailable, what information the provider holds, how quickly you could replace it, and what legal duties attach to the use case.
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A useful test is to examine three outage windows:
- One hour: Which people, customer interactions, approvals, or automated jobs fail immediately?
- One day: Can staff continue with a documented manual process, or do backlogs and contractual commitments accumulate?
- One week: Could you move to another provider or operate locally, and would customers, regulators, or auditors accept the degraded process?
Treat the service as mission-critical only when you can answer those questions with named owners, documented procedures, and tested alternatives. A provider’s assurances do not establish resilience by themselves.
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Classify the workload before you approve a provider
Record the classification in your normal supplier-risk or architecture register. The same provider may be acceptable for a low-impact drafting assistant and unsuitable for a system that makes decisions about people or handles sensitive records.
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| Dimension | Questions to answer | What the answer changes |
|---|---|---|
| Data sensitivity | Will the service receive personal data, confidential business information, regulated records, source code, customer prompts, embeddings, logs, or evaluation sets? | Higher sensitivity requires tighter permitted-use language, location controls, retention limits, access controls, deletion evidence, and stronger assurance. |
| Operational criticality | Which process, product, or customer promise depends on the output? | A critical process needs defined availability, recovery commitments, support escalation, observability, and a tested fallback. |
| Replacement time | How long would it take to export data and configuration, adapt interfaces, validate output quality, and retrain users? | A long migration window increases concentration risk and justifies portability work before production. |
| Regulatory impact | Could the use fall under privacy, sector, employment, consumer-protection, copyright, or AI-specific rules? | Regulated use may require documented risk management, transparency, human oversight, impact assessments, or records that the provider must support. |
Do not hide uncertainty in a single “approved” label. Record the workload, data classes, business owner, technical owner, maximum tolerable outage, migration estimate, and fallback owner separately.
Ask for evidence on security and privacy
Your due diligence should test both the provider’s controls and its ability to keep those controls current as threats, models, and subprocessors change. The Federal Trade Commission’s vendor guidance is blunt: “Put it in writing.” It also warns, “Don’t just take their word for it.”
Controls to verify
- Identity and access management, including administrative access, least privilege, authentication, and customer-tenant separation.
- Encryption in transit and at rest, with clear responsibility for key management.
- Tenant isolation and safeguards against one customer’s prompts, outputs, files, or logs becoming visible to another.
- Vulnerability management, security testing, patching, and a process for handling newly discovered threats.
- Incident response, notification, containment, investigation, and recovery procedures.
- Backups, recovery objectives, and restoration testing for the service components your business relies on.
- Subprocessor governance, including a current list, notification of changes, and equivalent obligations down the chain.
Questions that expose hidden data use
- Are customer inputs or outputs used to train, tune, evaluate, or improve any model? Is that use disabled by default, contractually prohibited, or merely configurable?
- Where are prompts, files, embeddings, telemetry, and support records processed and stored?
- How long do application logs, safety logs, backups, and deleted records remain?
- Who can access content for abuse review, support, debugging, or legal compliance?
- What deletion operation is available, what systems does it cover, and what evidence will you receive?
Request independent assurance or assessment evidence rather than a marketing summary. Check its scope, date, exclusions, and whether it covers the exact service, region, and processing path you will use. Ask how the provider notifies customers when controls, infrastructure, models, or subprocessors change.
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Put the critical controls in the contract
A security page cannot give your business a remedy when terms change or an incident occurs. Your agreement, data-processing addendum, order form, and service-level schedule should work together and identify which document controls if they conflict.
| Contract area | Minimum point to define |
|---|---|
| Permitted processing | Purpose, data categories, approved regions, prohibited secondary uses, and whether inputs or outputs may be used for training or human review. |
| Rights and licensing | Ownership or license rights for inputs, outputs, prompts, fine-tunes, retrieval indexes, evaluation sets, and provider-generated telemetry. |
| Retention and deletion | Retention periods by data type, deletion timelines, backup treatment, legal-hold process, and verifiable deletion evidence at termination. |
| Security and incidents | Required standards, control maintenance, cooperation duties, breach and incident notice, investigation access, and recovery communication. |
| Subprocessors | Advance notice, objection or termination rights, location disclosure, and flow-down obligations. |
| Service levels | Availability measurement, latency or quota commitments where material, support response, maintenance notice, recovery objectives, and service credits or other remedies. |
| Change control | Notice periods for model retirement, API or policy changes, processing-location changes, safety-control changes, and material feature removals. |
| Audit and evidence | Access to relevant independent reports, questionnaires, remediation summaries, and a proportionate right to investigate serious issues. |
| Exit and transition | Export formats, API access during transition, assistance with migration, continued read access where necessary, fee limits, and deletion certification. |
| Suspension and termination | Notice, emergency suspension rules, data retrieval windows, termination for security or regulatory concerns, and treatment of prepaid capacity. |
Require practical exports, not a promise that data is “available.” A usable export may need prompts, system instructions, tool definitions, retrieval documents, embeddings or source records, model and safety settings, workflow configuration, logs, feedback, and evaluation cases. If a fine-tune or evaluation set cannot be recovered in a documented format, treat it as a lock-in cost.
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Compare model and service performance, not just demonstrations
Run the same task-specific evaluation against each candidate and preserve the test set so results remain comparable after a model update. A general benchmark or sales demonstration cannot establish performance for your workflow.
| Criterion | Evidence to collect | Risk if missing |
|---|---|---|
| Accuracy and quality | Results on representative, permissioned examples; error categories; human-review thresholds; behavior on edge cases. | You may automate an unreliable process and discover quality loss only after switching models. |
| Latency, quotas, and capacity | Observed response ranges, rate limits, batch limits, concurrency behavior, and the provider’s stated commitments. | Peak demand can make a nominally available service unusable. |
| Version transparency | Model identifiers, retirement policy, release notes, deprecation notice, and a way to pin or test versions. | Silent changes can alter decisions, costs, or compliance evidence. |
| Safety and escalation | Abuse controls, refusal behavior, content filtering, human escalation, and incident-handling channels. | Users may route around controls or be left without a response path when the system fails. |
| Integration and observability | Stable interfaces, authentication, usage records, error codes, tracing, and exportable metrics. | Operations teams cannot diagnose incidents or reproduce decisions. |
| Portability | Documented interfaces, standard data formats, replaceable components, and a tested second implementation. | Migration becomes a rewrite rather than a controlled change. |
| Total exposure | Usage charges, engineering and review effort, support, migration, concentration, and regulatory costs. | A low unit price can conceal a high exit bill. |
Use a repeatable governance method
NIST’s voluntary AI Risk Management Framework Playbook organizes work into four functions: Govern, Map, Measure, and Manage. The Playbook states that it is based on AI RMF 1.0. Use those functions to assign owners and preserve evidence rather than treating vendor review as a one-time questionnaire.
Govern
Name the executive risk owner, system owner, procurement contact, security lead, privacy counsel, and operational approver. Set escalation thresholds for incidents, unacceptable output, regulatory change, and provider changes. Keep the contract, architecture, evaluation results, approvals, and exceptions together.
Map
Describe the users, purpose, data flows, model calls, retrieval sources, downstream decisions, affected people, and fallback process. Identify where the provider, subprocessors, and your own systems each act. This map should show which prompts, outputs, logs, and evaluation artifacts leave your environment.
Measure
Measure task quality, harmful or disallowed outputs, privacy events, latency, quota use, incidents, recovery exercises, and migration readiness. Record the model version and test conditions so a later result can be compared fairly.
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Manage
Apply mitigations, approve residual risk, monitor changes, and retire or redesign the use case when controls no longer match the impact. NIST’s trustworthiness dimensions include security, resilience, privacy, accountability, and transparency; use them as prompts for evidence, not as a substitute for concrete requirements.
NIST released the Generative AI Profile, NIST-AI-600-1, on 26 July 2024. It can provide additional generative-AI-specific structure alongside your organization’s existing risk process.
Map your regulatory role and jurisdiction
AI obligations depend on where the provider, deployer, users, and affected people are located, what the system does, and which role your organization occupies. Determine whether you are dealing with a general-purpose AI model provider, a downstream provider integrating a model, or a deployer using a system in operations. Then assess whether the use case is high-risk and whether privacy, sector, employment, consumer-protection, copyright, transparency, or fundamental-rights duties apply.
For organizations exposed to the European Union AI Act, the European Commission states that obligations for providers of general-purpose AI models entered into application on 2 August 2025, while the Commission’s enforcement powers for those obligations enter into application on 2 August 2026. High-risk systems have separate timelines and duties. Confirm the exact classification and applicable documentation with qualified legal advice; do not assume that a private hosting arrangement changes the role assigned by the regulation.
Your provider should be able to supply the technical and documentary information you need for your own duties, including model limitations, usage conditions, incident cooperation, copyright-related information where applicable, and records supporting transparency or human oversight.
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Design portability before production
Portability is an engineering property and a contract right. Build it while you still have bargaining power and a clean reference implementation.
- Inventory the assets. List prompts, system instructions, tools, schemas, source documents, embeddings or source data, fine-tunes, safety rules, workflow code, logs, feedback, and evaluation sets. Mark which assets are yours, which are licensed, and which the provider cannot export.
- Define formats and interfaces. Specify machine-readable exports, metadata, timestamps, identifiers, model settings, and error semantics. Document authentication, rate limits, retries, streaming behavior, and version assumptions.
- Separate provider-specific features. Keep orchestration, retrieval, business rules, and audit logging outside proprietary control planes where practical. Wrap model calls behind an internal interface so a second implementation can be tested.
- Build a reference fallback. Connect a second provider, a locally operated model, or a documented manual procedure. It need not match every feature, but it must cover the business-critical path and its quality limits must be known.
- Test quality after translation. Replay the preserved evaluation set, compare accuracy and safety outcomes, obtain human approval for material differences, and record any degraded capability.
- Prove deletion at exit. Obtain confirmation covering live systems, backups, logs, support copies, subprocessors, and derived artifacts, subject to documented legal holds.
Run an exit and disruption tabletop exercise
Perform the exercise before production dependence, then repeat it when the architecture, provider, or regulatory exposure changes. Give participants a clock, named decisions, and a requirement to produce evidence.
| Scenario | Exercise actions | Pass condition |
|---|---|---|
| Provider outage | Declare the incident, switch traffic or activate the manual process, communicate with users, and monitor backlog. | Critical work continues within the documented outage tolerance and operators can see what is failing. |
| Sudden price increase | Recalculate volume, identify approval thresholds, throttle noncritical use, and compare the fallback’s cost and quality. | Management can make a documented keep, reduce, or switch decision without losing data. |
| Model retirement or material change | Pin or select an alternative version, replay evaluations, review safety and regulatory effects, and update user notices. | No untested model silently reaches production. |
| Data incident | Invoke notice and investigation clauses, restrict access, preserve evidence, assess affected records, and coordinate communications. | Owners know whom to contact and can demonstrate containment and decision-making. |
| Regulatory restriction | Pause the affected workflow, identify geographic or role changes, and consult legal and compliance owners. | The business can stop or redesign the use without destroying records needed for compliance. |
| Provider acquisition or insolvency | Review assignment and termination rights, export assets, secure continued access during transition, and activate the alternate path. | Critical data and configuration are recoverable and customers receive a controlled service change. |
Make the approval decision explicit
Approve a single-provider dependency only when the record shows all of the following:
- The business owner has accepted the impact of each outage window.
- Data use, retention, location, deletion, and subprocessors are contractually bounded.
- Security and resilience controls have evidence appropriate to the workload.
- Model quality, safety, latency, quotas, and version changes are measured for the actual task.
- Exportable assets and a tested fallback exist for the critical path.
- Regulatory roles, jurisdiction, documentation, and escalation owners are identified.
- A tabletop exercise has produced actions, owners, and due dates rather than only discussion.
If one of those conditions is missing, reduce the workload’s impact, add a second option, or delay production dependence. The decision should state what evidence would allow reconsideration and when the risk will be reviewed.
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Vendor risk changes after signature. Monitor provider notices, model and API versions, subprocessors, processing locations, incident disclosures, service-level performance, quota use, evaluation drift, and regulatory guidance. Re-run representative evaluations after material changes. Review access rights and retention settings periodically, and confirm that exports and deletion procedures still work rather than assuming they do.
Maintain a current contact list for security incidents, service escalation, legal notices, and executive decisions. Keep an offline copy of the exit runbook, provider contracts, architecture map, evaluation set, and last successful export so an outage does not remove the information needed to leave.
Common failure modes and fixes
“Private” is treated as a security certification
Failure: The label is accepted without checking tenant isolation, access, encryption, logging, or subprocessors.
Fix: Ask for control evidence that matches the exact service and data path, then write the requirements into the agreement.
The contract covers uptime but not change
Failure: The provider can retire a model, alter a policy, move processing, or change an API with little notice.
Fix: Add defined change notices, testing windows, version information, termination rights, and transition support.
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Failure: The business cannot recreate retrieval, tools, safety settings, evaluations, or audit history.
Fix: Specify the complete asset inventory and perform a sample export and import before relying on the service.
A fallback exists only on paper
Failure: Credentials are expired, interfaces differ, quality is unknown, or nobody is authorized to switch.
Fix: Exercise the fallback, preserve results, assign the switch decision, and record the quality limitations users must understand.
Regulatory analysis is left to the provider
Failure: The buyer assumes the provider’s classification determines the buyer’s duties.
Fix: Analyze your own role, location, users, affected people, and use case; obtain the documentation and cooperation needed to meet those duties.
What a defensible decision record contains
Store one concise record with the workload description, dependency classification, data-flow diagram, outage tolerance, provider and subprocessor list, control evidence, contract exceptions, evaluation results, export sample, fallback test, regulatory analysis, tabletop findings, accepted residual risks, and review date. This record lets procurement, security, engineering, legal, and business leadership make the same decision from the same facts—and gives you a starting point when the provider’s terms or capabilities change.
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