Vendor security reviews · SaaS & technology
Vendor Security Review & Questionnaire Support for AI Startups & LLM App Builders
This service works in two directions: answering the AI-specific section an enterprise buyer's questionnaire now includes, and vetting the model providers, vector stores and orchestration vendors your own product depends on before they become someone else's finding. The trigger is usually a SIG or CAIQ landing with a new rider on training-data use and prompt retention, or a founder realizing nobody has actually compared what OpenAI, Azure OpenAI and Bedrock each commit to in writing.
Reviewed by the Privacy Horizon team · Last reviewed
What you're protecting
What has to be right on both sides of this review
An answer or a vendor assessment that doesn't match reality is a liability the moment anyone checks it.
Accurate sub-processor disclosure
Your published sub-processor list needs to name every model provider, vector store and orchestration tool that actually touches personal information, matched to what your DPA and privacy policy tell customers.
Verified zero-data-retention and no-training terms
A claim that a model provider won't train on your data is only as good as the specific contract language behind it, and the review has to confirm what each provider's terms actually say, not what their marketing implies.
Consistency with your own AI-PIA and policies
Answers about how prompts and RAG data are handled need to match your acceptable-use policy and any AI-PIA you've completed, since a mismatch is exactly what a sharp reviewer is trained to catch.
Realistic technical claims about the model chain
Answers about encryption, tenant isolation and access control across your vector store and orchestration layer need sign-off from the engineers who built them, not a generic security paragraph.
A version record across deals and vendors
A record of what was sent to which customer and what each sub-processor evaluation concluded, so contradictions don't surface when a renewal review compares this year's answers to last year's.
Regulatory map
Why this review sits on top of both statute and buyer standards
No Canadian law requires a specific questionnaire format, but the underlying due-diligence duty and the buyer standards that now include AI riders both apply directly.
PIPEDA's accountability for third parties
An organization stays accountable for personal information it transfers to a third party for processing, which is exactly the relationship your product has with every model provider a prompt reaches.
OPC guidance on cross-border processing
Guidance on transfers for processing sets out the due diligence and contractual protection expected before personal information moves to a provider, directly relevant when the provider is a US-hosted LLM API.
SIG as the questionnaire buyers default to
The Shared Assessments SIG questionnaire is the format most enterprise procurement teams use, and its newer versions increasingly carry sections asking specifically about AI model use and training-data handling.
CAIQ for cloud-focused reviews
The Cloud Security Alliance's CAIQ overlaps heavily with SIG content and is common among buyers focused specifically on cloud and AI service risk, so one well-maintained answer set serves both formats.
What goes wrong
What a weak review actually exposes
An inaccurate answer or an unvetted sub-processor is not just an audit risk, it's a liability the moment something goes wrong on either side.
A sub-processor's own infrastructure gets exposed
A publicly reachable database leaking chat histories and API keys, the pattern behind one AI vendor's well-documented exposure, is exactly the kind of risk a sub-processor evaluation is meant to catch before you build on top of it.
Claiming zero data retention without checking
Answering that a model provider never retains or trains on your data, when the actual contract carries exceptions or an opt-in default, becomes a warranty the moment a deal closes and reality diverges from the answer.
An undisclosed vector store or orchestration vendor
Leaving a vector database or an observability tool off the sub-processor list because it predates the current questionnaire creates a gap that surfaces at the worst time, during an incident involving that exact vendor.
Credential compromise at a vendor feeding your evals
The campaign against a major data warehouse's customers relied on stolen credentials and missing multi-factor authentication, a reminder that any vendor holding your evaluation or fine-tuning data carries the same exposure your own infrastructure does.
Our vendor security reviews for ai startups & llm app builders
What our vendor security review covers for an AI company
Support answering what comes in, and a framework for evaluating what your product depends on.
Gap review of incoming AI questionnaire sections
We compare what a SIG, CAIQ or custom AI rider asks against your actual controls and contracts, flagging where an honest answer needs a caveat or where a real gap needs closing first.
A model-provider comparison framework
A structured way to evaluate OpenAI, Anthropic, Azure OpenAI, Bedrock, Vertex and Cohere against retention, training-use and security terms, so the choice of provider is a documented decision, not an assumption.
Zero-data-retention and no-training clause checklist
A list of what to actually ask a model provider for, and how to read what they offer, so a ZDR claim in a sales conversation gets verified against the contract before it becomes a public commitment.
Vector store and orchestration vendor evaluation
The same due diligence extended to Pinecone, Weaviate, Qdrant or a LangChain-class orchestration layer, covering access control, data residency and what happens to embedded data on offboarding.
A reusable answer library
Verified answers to the AI-specific questions that recur across nearly every enterprise questionnaire, so each new request starts from a checked base instead of a blank page.
How the engagement runs
How we handle a review in either direction
Built to move at the speed a deal or a vendor decision actually requires.
Step 1
Triage the request
We identify whether this is an incoming questionnaire under deadline or an outgoing evaluation of a new model or vector-store vendor, and what can be pulled from existing work immediately.
Step 2
Verify against actual contracts and controls
Draft answers or vendor assessments are checked against your real model-provider agreements, sub-processor list and technical controls, with engineering input where sign-off genuinely matters.
Step 3
Document the decision
A completed questionnaire response, or a documented sub-processor evaluation with a clear recommendation, goes back inside your deadline.
Step 4
Fold it into the reusable record
New answers and vendor evaluations update your answer library and vendor register, so the next questionnaire or provider decision moves faster than this one did.
What it costs
What drives the cost of vendor review support
Cost depends on how many model, vector-store and orchestration vendors are in scope, how long and complex the incoming questionnaire is, and how much existing documentation, such as an AI-PIA or SOC 2 report, already exists to draw from. A single-model product answering a routine renewal needs far less support than one comparing several inference vendors for the first time.
This work is frequently delivered alongside SOC 2 or ISO 27001 readiness, since the same gap review and vendor documentation feed both, and can sit inside a Virtual Privacy Office retainer for companies facing recurring questionnaire volume. We quote standalone support after seeing the specific questionnaire or vendor decision in front of you.
AI Startups & LLM App Builders: Vendor security reviews questions, answered
Answer based on what data actually reaches your model provider, what the provider's retention and training terms say in writing, and whether that provider is on your published sub-processor list. A vague answer draws more follow-up questions than a precise one, so verify each claim against the actual contract before it goes out.
Compare their data-retention defaults, whether zero-data-retention or no-training terms are available and under what conditions, their published security certifications, and the region where inference actually runs. The right choice depends on your data sensitivity and customer commitments, not on which is easiest to integrate first.
Ask for the specific contract language, not a marketing statement: whether retention is fully disabled or reduced to a short window, whether it applies to logs as well as training, and whether it's the provider's default or something you have to request. Confirm the answer in writing before repeating the claim to a customer.
Yes, if the provider processes personal information on your behalf, which is the case whenever a prompt containing user or customer data reaches it. Leaving a model provider off your sub-processor list is one of the most common gaps we find, and it's usually simple to fix once identified.
Treat it like any other data processor: check where it hosts data, what access controls and encryption it offers, what happens to embedded data on account closure, and whether it appears in your existing sub-processor and DPA documentation. Embeddings derived from personal information carry the same review requirement as the source documents did.
Yes, though it rarely covers AI-specific sections completely on its own. A current SOC 2 report answers most general security questions in one document, but the AI rider on training-data use and model sub-processors usually still needs its own verified answer, since SOC 2's Trust Services Criteria weren't written with model providers specifically in mind.
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About this service
Answers & guides
- How does a startup pass an enterprise vendor security review?
- How do we prepare for a customer security questionnaire?
- How do you assess the privacy and security risk of an AI vendor?
- How a Startup Passes Its First Enterprise Vendor Security Review
- An AI Vendor Privacy & Security Checklist for Procurement Teams
- Building a Third-Party Vendor Risk Assessment Program That Scales
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