A general vendor security questionnaire, covering encryption, access control, incident history, certifications, is still necessary for any AI vendor, but it misses a whole category of AI-specific risk that a security-focused questionnaire built for conventional software vendors was never designed to surface. Assessing an AI vendor well means adding specific questions about how the vendor's AI capabilities actually handle data and behave, layered on top of the general assessment already in use.
What happens to submitted content, specifically
The most important AI-specific question is what the vendor actually does with content submitted to their AI features: does it train or improve the vendor's own models, does it get retained beyond the immediate request, and if so, for how long and with what access controls. Vendor answers vary a lot here, some explicitly exclude customer content from any training use by default, others require an explicit opt-out, some give limited clarity without direct questioning. Get this answer specifically and in writing, since it directly decides whether your organization's data could end up shaping a model used by other customers.
What happens during a subprocessor chain
Plenty of AI vendors build on top of other AI providers, wrapping a foundation model from one company inside their own application layer. Ask specifically about this chain: which underlying model providers are involved, what data handling commitments exist at each layer, whether your organization's data flows through providers beyond the vendor you're directly contracting with. A data handling commitment from your direct vendor doesn't automatically bind an underlying subprocessor, not unless the contractual chain specifically addresses it.
How the vendor handles model updates and versioning
Ask how the vendor manages model updates: are customers notified before a significant model change that could affect behavior, do prior model versions stay available for a transition period, how does the vendor validate that an update hasn't introduced new safety or behavior issues before rolling it out broadly. A vendor with no clear answer here might deploy model updates that silently change your integration's behavior with zero advance warning, which complicates your own testing and change management process considerably.
What security testing the vendor has performed on their own AI features
Ask whether the vendor has conducted or commissioned adversarial testing specific to their AI features, prompt injection resistance testing, output validation review, testing for their own excessive agency risks if their product includes any agentic capability. A vendor whose security testing program predates their AI feature additions, and never got extended to cover them specifically, might have strong general security practices without having applied the same rigor to their newer AI-specific capabilities.
What incident history exists specific to AI features
Ask about the vendor's incident history specific to AI-related issues, not just general security incidents, since an AI-specific incident, a data leakage through model output, a successful prompt injection affecting customers, is directly relevant risk information a general incident history question might miss entirely if it's phrased only around conventional breach categories.
What contractual commitments exist around these specific answers
Verbal or documentation-based answers to these questions carry less weight than contractual commitments do. Where an AI-specific answer matters a lot to your risk tolerance, data training exclusion, retention limits, subprocessor disclosure, push to get that commitment reflected in the contract itself, not just a security questionnaire response that might not carry the same enforceability if the vendor's practice later drifts from what got represented.
Reassess periodically, since AI vendor capabilities change quickly
AI vendors add new features and capabilities faster than typical enterprise software vendors tend to, which means a security assessment completed at initial vendor selection can go stale as the vendor adds new AI capabilities you might not even know about. Build periodic reassessment into vendor management specifically for AI vendors, rather than trusting the assessment from initial onboarding to stay accurate forever.
Scale assessment depth to what data and capability the vendor actually touches
Not every AI vendor relationship warrants the same assessment depth. A vendor providing a narrow, non-sensitive capability with no access to customer data deserves a lighter assessment than a vendor whose AI feature processes sensitive customer or business data directly. Use the same risk-based proportionality principle that matters elsewhere in AI governance, so assessment effort doesn't go disproportionately toward low-risk vendor relationships.
Where TELEON fits
TELEON's audit trail can help track what data actually flows to specific third-party AI vendors through agent tool calls, applied through the gateway or supported middleware, supporting ongoing verification that vendor data flows match what was represented during the assessment.
The assessment questions themselves, and the contractual negotiation to secure specific commitments, are vendor management and legal work that has to be conducted directly with each vendor relationship.
A short checklist
- Ask specifically what happens to submitted content, including training
use and retention.
- Ask about the full subprocessor chain, not only the direct vendor
relationship.
- Ask how the vendor manages and communicates model updates.
- Ask what AI-specific security testing the vendor has performed.
- Ask about AI-specific incident history, not only general security
incidents.
- Secure important commitments contractually, not only in questionnaire
responses.
- Reassess periodically, since AI vendor capabilities evolve quickly.
Assessing an AI vendor well requires adding AI-specific questions about data use, model updates, and subprocessor chains to whatever general security assessment process already exists, since these questions surface risk that a conventional vendor questionnaire was never designed to catch.