Articles

Vendor Risk Checklist for AI Commerce Infrastructure

A checklist for evaluating agentic commerce vendors before they enter security, procurement, and legal review.

Vendor risk teams are used to reviewing SaaS systems that store customer data or process operational workflows. AI commerce infrastructure is slightly different: the system may not be the seller, but it can still touch buyer identity, shipping details, payment tokens, order status, and customer support paths.

The Short Checklist

Confirm whether the vendor is merchant of record, payment processor, checkout orchestrator, or data processor.

Request a data flow diagram for product lookup, landed cost calculation, payment token handling, order placement, and webhook delivery.

Verify whether payment data is tokenized before the vendor receives it.

Ask how failed, partial, duplicate, or abandoned orders are detected and reconciled.

Confirm retention periods for buyer PII, order metadata, logs, and support artifacts.

Review webhook authentication, event replay protection, and incident notification commitments.

Questions CISOs Should Ask

The strongest review questions are concrete:

Can the vendor place an order without a fresh user consent signal?
What fields are required to calculate tax and shipping?
What fields are stored after the order is placed?
Can a customer revoke a saved payment or shipping profile?
What happens when the merchant changes price or availability between recommendation and checkout?

These questions reveal whether the vendor has modeled agentic checkout as a security-sensitive workflow or as a simple API wrapper.

Evidence To Request

Data flow

Core document

Audit log

Core artifact

Bounded action

Core promise

Ask for evidence that maps directly to the transaction lifecycle. Useful artifacts include architecture diagrams, SOC 2 reports when available, subprocessors, sample webhook payloads, API authentication docs, incident response policies, and example audit records.

A Clean Review Outcome

The best vendor risk outcome is not a giant questionnaire. It is a concise operating model:

The AI app owns recommendation.
The user owns authorization.
Rye owns checkout execution infrastructure.
The merchant owns the sale.

When those responsibilities are clear, legal, security, and product teams can move faster without pretending the risk is smaller than it is.