Choose a secure AI coding assistant by assessing the exact plan, model, access path, and configuration your team would use—not the product name alone. Verify what code and prompts are processed, whether data is retained or used for training, what administrators can control, and how agent access is bounded. Then require generated code to pass your normal testing, security checks, and review before it is merged.
What should count as secure for a coding assistant?
Security is not a single feature or certification. For a team, it is the combination of data handling, access limits, administrative oversight, development fit, and a workflow that catches unsafe output. A vendor’s policy may differ by subscription tier, model, client, or feature, so evaluate the configuration you intend to buy and deploy.
Include prompts, code context, suggestions, conversation history, repository content, and agent actions in the review. Establish which of those inputs may reach the service or connected tools, why they are processed, how long they are kept, and who can access them. Separately, assess whether the assistant can read or change files, run commands, connect to external services, or act on repositories.
How should you compare candidate assistants?
Use the same questions for each candidate and record answers for the exact plan and deployment under consideration. Ask vendors for documentation or contractual terms that support the answers; do not treat an undocumented sales statement as a control.
| Area | What to verify | Evidence to request |
|---|---|---|
| Data use and retention | Which inputs are transmitted; their processing purpose, retention period, and training use; and whether these vary across completion, chat, CLI, mobile, agent features, or models. | Product- and tier-specific privacy documentation, configuration details, and applicable contractual terms. |
| Administrative control and audit | Whether admins can assign access, disable or scope features, govern agent modes and external tools, and inspect or export relevant activity records. | Documentation for the proposed subscription, supported clients, and available audit records. |
| Context and permissions | Which files, repositories, conversation history, and connected systems an assistant or agent can access—and whether access can be narrowed by role or repository. | Permission model, integration documentation, and a demonstration using the team’s intended configuration. |
| Security workflow | How suggestions and agent changes will be tested, scanned, reviewed, and approved before merge. | A pilot workflow that uses the team’s real review and security checks. |
| Development fit | Support for the team’s IDEs, languages, identity model, repository platform, and operating requirements. | Compatibility documentation and a trial with representative projects. |
| Contract and deployment | Applicable commitments for subprocessors, geography, retention options, and regulated data in the proposed configuration. | Contract terms and deployment-specific documentation, confirmed by security, legal, or procurement as appropriate. |
Score candidates against these same criteria rather than looking for a universal winner. A strong answer for one access path or business tier does not establish the policy for another.
What data does an AI coding assistant send or retain?
Ask for a data-flow explanation covering the inputs each feature uses. A completion feature may have different handling from chat or an agent that can gather repository context. Clarify whether conversation history is included, whether administrators can change retention, and whether data is used to train models. Read the terms for both the assistant and any model provider involved.
Use vendor examples as configuration-specific evidence
GitHub states that it does not use Copilot Business or Enterprise data to train its models. Its published Copilot information also distinguishes default retention by access mode: prompts and suggestions for IDE chat and code completions are not retained by default, while prompts and suggestions for other Copilot access and use are listed as retained for 28 days. These statements apply to the specified GitHub tiers and documented defaults; confirm the live policy and the customer’s settings for the proposed use. GitHub Copilot data-use information.
Model-provider terms can add another layer. GitHub’s model-hosting documentation describes a zero-data-retention exemption, through the end of 2026, for certain Claude models. That is a time-bounded, model-specific statement—not a guarantee for every model, account, or access path. Check the current terms for the model your team plans to enable. GitHub’s model-hosting documentation.
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Google publishes security, privacy, and compliance information for Gemini Code Assist Standard and Enterprise, including information about IDE context that may be processed. Review the documentation for the edition and configuration under consideration rather than applying it to other Google products or settings. Google Cloud’s Gemini Code Assist security and privacy documentation.
Can a team control what its coding agent can access?
It depends on the product, plan, client, and configuration. Treat agent permissions as a separate review from data-retention policy: an agent may be able to read repository files, use tools, or take actions that a completion-only feature cannot. Map those capabilities before enabling them for a team.
- Identify which agent modes are available in the IDE, CLI, or other clients the team uses.
- Determine whether administrators can enable, disable, or scope agents and their use of external tools or MCP servers.
- Limit repository and connected-system access to what a role needs; check how credentials and permissions are applied.
- Find out what activity administrators can inspect and whether records can be retained or exported for the team’s audit needs.
- Review extensions and integrations for provenance and permissions, not just the core assistant.
GitHub documents enterprise controls relating to agents, IDE agent mode, MCP server use, and activity or audit visibility. Confirm which controls apply to the plan and clients you would deploy; the existence of an enterprise control does not mean it applies to every account. GitHub’s enterprise agent-management documentation.
Extension integrity also belongs in the review. BSI and ANSSI guidance discusses risks including training-data poisoning and the security of extensions, supporting a review of both extension provenance and the permissions granted to external tools. BSI/ANSSI guidance on AI coding assistants.
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How should generated code be reviewed?
Do not treat plausible output as trusted code. GitHub cautions that inline suggestions can be syntactically correct without being secure. Keep the same approval gates you use for human-written changes: tests, security checks, code review, and authorization before merge. GitHub’s inline-suggestion guidance.
Define how the team will handle agent-generated changes before rollout. For example, specify who reviews changes, which existing tests and scanners must pass, and whether an agent may execute commands or modify files without human approval. The exact controls will depend on the assistant and the team’s development process; do not assume a product replaces secure coding practices or review.
How can you make the decision defensible?
- Define the intended deployment. Name the plan, models, clients, IDEs, repositories, identity setup, agent features, and connected tools the team would actually use.
- Document the data boundary. For each feature, record inputs processed, purposes, retention, training use, and any model-provider terms. Resolve differences between access paths instead of relying on one broad privacy statement.
- Test administration and audit. In a pilot, verify that the people responsible can manage access and features, constrain tools where needed, and retrieve the activity records required by policy.
- Exercise the workflow on representative code. Use real project types and the team’s normal review process to check IDE and repository fit, permissions, and how changes move through tests and security review.
- Close contractual and operational gaps. Have the responsible security, legal, and procurement stakeholders confirm the terms and deployment details that matter to the organization, including any regulated-data conditions.
- Set rollout conditions. Record approved plans and configurations, allowed tools and repositories, review requirements, and who will revisit the decision when product terms or capabilities change.
NIST’s AI Security Control Overlays project describes implementation-focused guidance for use cases and components, including training and test data, model weights, and configuration settings. It can inform a team’s control review, but the project page should not be described as a finalized standard. NIST’s AI Security Control Overlays project.
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