AI coding tool procurement checklist for engineering teams

Buying an AI coding tool is simultaneously a product, security, privacy, and operations decision. A feature demo alone is not enough.

1. Data flow and accountability

Request a current data-flow diagram and ask:

• Which data leaves the device or repository?
• Which vendors and subprocessors receive it?
• Which models and regions can be selected or enforced?
• How long are metadata, prompts, and outputs retained?
• Which processing supports abuse monitoring or customer support?

The Security & Trust Center and model overview should connect these facts for Lurus Code.

2. Contractual documents

Review the DPA, subprocessor list, transfer mechanisms, deletion periods, and change notifications. “EU region available” does not fully describe the operator, jurisdiction, or actual routing.

For personal data, controller/processor obligations and international transfers require specific assessment. This checklist is not legal advice; it structures technical and organizational due diligence.

3. Separate training, retention, and product persistence

Ask distinct questions:

• Is customer data used for model training?
• Does zero data retention apply to every offered route?
• Do product features such as history, memory, or logs persist data independently of the model provider?
• Which settings can administrators enforce?

A “no training” statement does not automatically answer retention or product persistence.

4. Permissions and system boundaries

Assess permission modes, secret access, shell commands, external tools, and MCP transports. Every action should be clearly allowed, confirmation-gated, or denied.

5. Quality and security pilot

Define before purchase:

• repositories and tasks in scope,
• existing test and review gates,
• measurable acceptance criteria,
• owners for findings and approvals,
• stop conditions.

Use the rollout checklist and Engineering Teams solution to design the pilot.

6. Exit and evidence

Clarify export, deletion, audit logs, contract termination, and stored sessions. A tool is enterprise-ready only when adoption and offboarding are both controlled.

Decision matrix

Rate each criterion as verified, partially verified, open, or unacceptable. Link the source and review date for every rating. The decision then remains traceable when vendors, models, or contracts change.