An AI coding assistant can improve developer productivity, but approving one for a team requires more than checking whether it generates useful code. This checklist helps developers, security reviewers, and IT administrators evaluate code retention, model training, prompts, telemetry, repository access, encryption, data residency, administrative controls, and opt-out options before rollout.
Overview
The question “Is an AI coding assistant safe?” does not have a universal yes-or-no answer. Risk depends on the assistant’s configuration, the subscription or deployment model, the data sent to it, the permissions granted to its IDE extension, and the controls your organization requires.
Start by separating three different data flows:
- Code and repository content: source files, snippets, diffs, file paths, dependency information, and sometimes nearby files used to provide context.
- Prompts and interaction data: natural-language instructions, rejected or accepted suggestions, error messages, and conversation history.
- Operational telemetry: account information, device or IDE details, usage events, diagnostics, security logs, and performance data.
These categories may have different retention periods, processing purposes, and administrative controls. Review each one separately rather than treating “your data” as a single category. For broader AI developer tool decisions, pair this checklist with an LLM application reliability checklist and your organization’s existing vendor-risk process.
A useful review records the answer, the evidence, the owner, and any restriction. If a policy is unclear, mark it as unresolved instead of assuming the most favorable interpretation.
Checklist by scenario
Individual developer or personal project
- Confirm whether the tool receives complete files, selected code, surrounding context, repository names, or only the current prompt.
- Read the applicable privacy and product terms before sending proprietary, customer, credential, or regulated data.
- Exclude secrets and sensitive directories with the assistant’s configuration, repository rules, or IDE settings where available.
- Check whether chat history, prompts, and generated suggestions remain visible in the account or workspace.
- Review generated code manually for secrets, insecure dependencies, licensing concerns, and incorrect assumptions.
Team or company rollout
- Identify whether the plan provides an organization-level privacy setting, training opt-out, retention control, or administrator console.
- Determine who can install the extension, connect repositories, create access tokens, and change data-sharing settings.
- Define approved and prohibited use cases. For example, production debugging may require different controls from a public demonstration project.
- Document how employees should handle customer data, incident details, credentials, private keys, and unreleased source code.
- Set an approval path for new models, plugins, agents, or features that can read or modify files.
- Decide whether security, legal, procurement, or data-protection teams must review the tool before use.
Private or sensitive repository
- Verify whether repository context is sent to an external service and whether the organization can restrict that context.
- Check data residency requirements and whether processing locations are documented for the relevant plan.
- Ask how deletion requests, account closure, repository removal, and retention exceptions are handled.
- Use least-privilege access: separate read-only assistance from features that can write files, open pull requests, run commands, or access issue systems.
- Require normal code review, testing, secret scanning, and dependency checks for assistant-generated changes.
Regulated or audited environment
- Map the assistant’s data flows to internal retention, confidentiality, access, and audit requirements.
- Request current security documentation and clarify which controls apply to the exact product and plan under consideration.
- Record exceptions, compensating controls, and the person responsible for periodic review.
- Test whether logs can show who enabled the tool, which repositories it can access, and when permissions changed.
For practical coding use, establish approved prompt patterns that avoid unnecessary context. The AI coding prompt templates guide can help teams standardize debugging, refactoring, and review requests without pasting an entire repository into a conversation.
What to double-check
Use the following comparison table when evaluating a provider or changing plans. Do not fill a cell from marketing language alone; link each answer to a current privacy notice, security document, contract term, administrator setting, or vendor response.
| Area | Questions to ask | Evidence to record |
|---|---|---|
| Code retention | How long are snippets, files, completions, and chats stored? Are temporary copies or backups covered? | Retention terms, configuration, deletion process |
| Model training | Can submitted code or prompts be used to improve models? Is opt-out available, and at what account level? | Product setting, contract language, policy version |
| Prompts and telemetry | What interaction and diagnostic data is collected? Can administrators limit optional telemetry? | Privacy documentation and admin controls |
| Data residency | Where is content processed and stored? Are locations different by product, region, or plan? | Regional terms or vendor confirmation |
| Encryption | Is data protected in transit and at rest? Who manages encryption keys, if customer-managed keys are offered? | Security documentation and contract scope |
| Access controls | Are single sign-on, multi-factor authentication, role-based access, and centralized provisioning supported? | Feature documentation and test results |
| Repository permissions | Can the assistant read, write, execute commands, or access connected services? Are permissions repository-specific? | OAuth scopes, extension settings, access review |
| Opt-out and deletion | Can the organization disable training, remove stored data, export records, or close the account? | Procedure, service-level detail, responsible owner |
Also inspect the IDE extension itself. A code assistant may have capabilities beyond text completion, including workspace indexing, terminal interaction, file edits, or external tool calls. Treat each capability as a separate permission. If your team is building an internal LLM application or agent, apply the same principle to tool calling and structured outputs; the structured output prompts guide explains why validation should remain outside the model.
Common mistakes
- Assuming paid means private: A paid plan may offer additional controls, but privacy, retention, and training terms still need to be verified for the exact product.
- Reviewing only source code: Prompts, file paths, error logs, repository metadata, and telemetry can also reveal sensitive information.
- Granting broad repository access: Start with the smallest practical scope and review permissions after installation and major feature changes.
- Treating an opt-out as retroactive deletion: Disabling future use may not answer what happened to data already retained. Confirm both controls separately.
- Skipping developer guidance: A strong vendor policy cannot prevent users from pasting credentials or customer data into an unrestricted prompt. Provide examples of safe and unsafe input.
- Relying on generated code without review: Privacy approval does not make output correct or secure. Run tests, review dependencies, scan secrets, and use normal change controls.
- Failing to document uncertainty: If a provider’s answer is ambiguous, record the question and apply a temporary restriction rather than silently approving the tool.
When to revisit
Review this checklist before initial approval, during seasonal planning cycles, and whenever workflows or tools change. A new model, IDE extension, agent mode, plugin, repository integration, or enterprise plan can alter the data and permission profile even when the product name remains the same.
Set a practical review trigger for each of these events:
- the provider changes privacy, retention, training, or regional-processing terms;
- your organization adds a sensitive repository, regulated workload, or new geographic region;
- the assistant gains file-writing, terminal, pull-request, or external-tool capabilities;
- an employee changes teams, leaves the organization, or no longer needs repository access;
- a security incident, suspected data exposure, or unexplained telemetry event occurs;
- the team changes its IDE, source-control platform, authentication method, or deployment workflow.
For each review, compare the current configuration with the original approval, test a low-risk repository, confirm that exclusions work, and update the owner and evidence links. Finish with a rollout decision: approve, approve with restrictions, pause pending clarification, or remove access. This creates a repeatable AI developer tool review rather than a one-time privacy checkbox.
