Data handling is usually the first unresolved question.
Teams need to know what is stored, what is used for training, where data goes, and who the underlying processors are.
AI tool due diligence
Before an AI tool enters the workflow, know what risk enters with it.
EvidenceOps reviews AI vendors across data handling, training use, retention, model providers, controls, workflow dependency, pricing, and unresolved verification questions.
Why AI tools feel different
AI vendor risk is often hidden inside workflow convenience.
The tool may look harmless until customer data, employee notes, confidential files, or operational decisions start flowing through it.
Output risk depends on the workflow.
An AI assistant for drafts has a different risk profile than an AI tool shaping customer, finance, legal, or HR workflows.
Policy fit matters before adoption spreads.
AI tools often enter through small pilots. The risk grows when informal usage becomes operational reliance.
Evidence layer
What gets checked before an AI tool rollout.
The review maps vendor claims to public documents, trust-center evidence, privacy terms, product docs, and verification questions.
Training use
Whether customer inputs, outputs, files, or logs may train models
Retention
How long data is stored and whether deletion is documented
Model chain
Underlying model providers, subprocessors, hosting regions
Controls
SSO, admin permissions, audit logs, workspace settings
Workflow risk
Human-in-the-loop needs, error exposure, reliance level
Rollout conditions
Allowed use cases, blocked use cases, vendor questions
Decision logic
A good AI decision is rarely just Yes or No.
The useful answer is often conditional: which use cases are allowed, which data is excluded, and which controls must be verified first.
Define the allowed use case
The same vendor can be acceptable for low-risk drafts and unacceptable for sensitive customer records.
Separate vendor claims from policy assumptions
If the vendor says data is protected, the review checks where that is stated and what remains ambiguous.
Translate risk into rollout conditions
The brief turns open questions into vendor checks, pilot tests, and internal policy constraints.
Starter checklist
Five questions before approving an AI tool.
These questions create the moment of useful friction before a promising AI tool becomes an uncontrolled process dependency.
EvidenceOps