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Assess an AI vendor by tracing what happens to your specific data in the exact service you plan to use, then verifying those claims in current evidence and enforceable contract terms. A security report or framework alignment can help organize review, but neither proves how every product, account tier, or configuration handles your data.
Start with the use case and the supplier chain
Before reviewing a vendor’s assurances, write down what you intend to do with the AI service and what could go wrong. The same product may be suitable for public text but not confidential customer records, depending on its data practices, configuration, and the consequences of exposure or failure.
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- Use and people affected: Describe the task, users, and anyone whose information or decisions may be affected.
- Data and impact: List the data classes involved and the impact if information is exposed, altered, retained unexpectedly, or unavailable.
- Supplier role: Identify whether the company is the model provider, application provider, integration layer, or a subprocessor. Record embedded models and other services that may receive data.
NIST’s supplier guidance treats due diligence as an investigation of relevant information about a supplier or product before a new acquisition or when reviewing an existing system. Its ICT supplier considerations include provenance, resilience, foundational cybersecurity practices, supply-chain tiers, and ownership, control, or influence where relevant. NIST SP 1326 provides a useful structure, but it is guidance for supplier assessment—not proof that a particular AI service is safe.
Map every route your data can take
Ask the vendor for a current data-flow diagram covering the exact service and deployment. If the vendor cannot provide one, request a written explanation detailed enough to establish where each data category goes and which parties can access it.
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- Prompts, uploaded files, generated outputs, and user feedback
- Logs, telemetry, analytics, and diagnostic data
- Support access, backups, and disaster-recovery copies
- Fine-tuning, evaluation, product-improvement, or other feedback paths
- Subprocessors, embedded models, and downstream model services
For each category, ask what is collected and why; where it is processed and stored; who can access it; how long it persists; and what deletion covers, including backups and derived artifacts. Ask directly whether it may be used for model training, evaluation, product improvement, or another secondary purpose. Do not treat “not used for training” as a complete answer: it does not, by itself, explain logging, retention, human access, or other uses.
NIST’s Generative AI Profile calls for attention to embedded AI dependencies, privacy, security, and ongoing supplier monitoring. Its guidance also supports keeping an inventory of third parties that can access organizational content. See the NIST Generative AI Profile and NIST’s AI RMF Playbook.
Check the evidence against the service you will use
Request current security and privacy materials for the actual product, deployment, account tier, geography, and configuration under consideration. Useful evidence may include independent audit reports or certifications, a scope statement, security architecture, access-control and encryption descriptions, vulnerability-management practices, incident-response commitments, retention and deletion controls, and subprocessor disclosures.
Read the boundaries, not just the report name. Check the report period, exceptions, systems covered, and whether the relevant AI service and data processing fall within scope. A report can provide evidence about controls assessed within its defined scope; it does not establish training-data handling, privacy compliance, or model behavior unless those matters are specifically addressed. The same caution applies to a framework claim: alignment is not a guarantee about every product or customer configuration.
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NIST’s Generative AI Profile recommends due diligence that includes checking incident and vulnerability information and continuing to monitor third-party risks. Treat documents as one part of verification, alongside direct answers, contract commitments, and your own assessment of the use case.
Assess dependencies, provenance, and resilience
Ask who owns or controls the supplier when that could affect your risk, what major components and model services it relies on, and how it tracks material changes. Establish what happens if a provider becomes unavailable, changes its service, or can no longer meet your requirements. Identify a workable fallback for high-impact uses rather than assuming the service will remain unchanged.
NIST SP 1326 includes supplier provenance, resilience, supply-chain tiers, and ownership, control, or influence among its due-diligence considerations. The Generative AI Profile also recommends identifying over-reliance on third-party data and documenting fallback plans. Apply these questions in proportion to the use case: a dependency that is tolerable for a low-impact assistant may be unacceptable for a critical workflow.
Turn assurances into contract terms you can verify
Statements such as “we do not train on your data” or “we delete your data” need precise definitions. Have qualified counsel review the agreement for your jurisdiction, sector, data types, and use case. Check that the contract, applicable service terms, and any service-level agreement cover the commitments that matter to your deployment.
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- Covered data and services: Define which inputs, files, outputs, logs, and derived data are covered, and name the services and configurations to which the promise applies.
- Permitted use: Specify allowed purposes, including whether data may be used for training, evaluation, product improvement, or other secondary uses, and list any exceptions.
- Retention and deletion: Set retention periods and deletion timing and scope, including treatment of backups and derived artifacts.
- Subprocessors and changes: Require appropriate downstream obligations and define notice, approval, or objection rights for material changes to subprocessors, model providers, or data practices.
- Security and incidents: Set security requirements, incident notification and cooperation duties, and expectations for addressing material vulnerabilities or control failures.
- Evaluation and exit: Address audit or evaluation rights, content ownership and usage rights, quality and provenance expectations, and how data can be exported or deleted at termination.
NIST recommends that contracts and SLAs address content ownership, usage rights, quality, security requirements, and provenance, and allow evaluation of third-party GAI processes and standards. It also flags risks from non-standard terms, unexpected liability effects, and unauthorized secondary data use. A promise is more useful when its scope, exceptions, verification method, and remedy are clear.
Compare vendors on the same decision criteria
Use one set of questions for every candidate, and score each answer against your actual data and impact requirements. A side-by-side record helps separate a genuine difference from a gap in disclosure.
| Assessment area | What to compare |
|---|---|
| Data use and minimization | Data collected, permitted purposes, training and secondary-use terms, and whether unnecessary data can be excluded. |
| Retention and location | Retention periods, deletion scope and timing, storage and processing locations, and backup treatment. |
| Dependencies | Subprocessor visibility, embedded models, downstream services, and notice of material changes. |
| Assurance evidence | Scope, recency, exceptions, and whether the specific AI service and processing are covered. |
| Security operations | Access controls, encryption, vulnerability handling, incident response, and relevant incident history. |
| Contract rights | Audit or evaluation, incident notification, change notice, data export, and termination rights. |
| Fit and resilience | Suitability for the intended use, provider concentration, fallback options, and impact if service fails. |
There is no single universally correct trade-off among security, privacy, reliability, explainability, and other trustworthiness characteristics. NIST’s AI Risk Management Framework says deployment decisions should weigh these characteristics, risks, impacts, costs, and benefits in context and with input from interested parties. The NIST AI RMF 1.0 is voluntary guidance, not a certification. NIST says the framework is being revised, so check its current status before relying on it operationally.
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Vendor risk can change after procurement. Keep an inventory of approved AI services and the third parties with access to organizational content, then set a review cadence appropriate to the use and sensitivity of the data.
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- Dual USB-A and USB-C Security Key – Features both USB-A and USB-C connectors for seamless compatibility across desktops, laptops, and tablets. Supports plug-and-stay use or keychain carry.
- NFC-Enabled for Mobile Access – Built-in NFC allows fast, wireless authentication with Android and iPhone devices. Ideal for mobile logins and on-the-go security.
- FIDO Certified for Strong Authentication – [CHECK COMPATIBILITY before purchase] Fully compliant with FIDO2 and FIDO U2F standards. Works with major platforms like Google, Microsoft, GitHub, and Dropbox.
- Passwordless Login with PinPlex – Supports secure passkey login via WebAuthn and CTAP2 with added protection from PinPlex, a complex PIN system that enhances physical security.
- Multi-Layer Authentication Support – Includes PIV certificates and supports both TOTP and HOTP for strong 2FA/MFA coverage across enterprise and consumer apps.
Trigger a reassessment when a vendor adds a model provider or subprocessor, changes retention or data-use terms, expands the data collected, reports a security incident, receives a material audit exception, undergoes an acquisition, or is used for a materially different purpose. Review incident and vulnerability information, confirm that contract commitments still match actual service terms, and test contingency arrangements for critical workflows. NIST’s Generative AI Profile recommends ongoing monitoring, incident-response planning, and documenting third-party GAI incidents.
Frequently Asked Questions
How can I tell whether an AI vendor uses my data to train its models?
Ask for a written answer that defines covered data, services, and configurations, and distinguishes training from evaluation, product improvement, logging, and other secondary uses. Confirm the answer in contract terms, including exceptions and downstream-provider obligations.
Is a SOC 2 report enough to trust an AI vendor?
No single report name is enough. Check the report period, scope, exceptions, and whether the AI service and relevant data processing are covered; a security report alone does not establish training-data handling, privacy compliance, or model behavior.
What should an AI vendor contract say about my data?
It should define the covered data and services, permitted purposes, retention and deletion, subprocessor obligations, material-change notices, security and incident duties, evaluation rights, and data export or deletion at termination.
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