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You can evaluate AI coding tools without uploading proprietary production code: run a controlled pilot using synthetic, public-but-cleared, or sanitized code in an isolated test repository. Before enabling the tool, establish what data its exact plan, model, and features can access or transmit—including IDE and repository context—and verify the applicable retention, logging, training, processing-location, and permission controls. A promise that prompts are not used for training does not, by itself, mean they are never transmitted or retained.
Start with the data boundary, not the prompt box
An AI coding tool may receive more than the text you deliberately paste into a chat. Depending on the feature and configuration, context can include open or nearby files, conversation history, repository information, and IDE state. Completion, chat, code indexing or customization, bring-your-own-key routing, and agent modes can also have different data paths and controls. Treat each enabled feature as a separate path to assess rather than assuming one privacy setting covers the whole product.
First decide what information may not leave your organization under any circumstances, and what may be processed by an external service only under approved controls. That boundary should be based on your organization’s data classification, contractual commitments, jurisdictional obligations, and security requirements. There is no universally safest tool for every team: the answer depends on the exact plan, model or provider, features, settings, and applicable contract.
Classify what the tool might encounter
List the information present in the repositories and development environment you might use for evaluation. Consider at least:
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- Proprietary source code, unpublished product logic, and internal architecture.
- Secrets and credentials, including values embedded in configuration files, test fixtures, or environment files.
- Customer, employee, or other personal data in examples, logs, or test data.
- Regulated or contractually restricted information.
- Metadata and context that might reveal project names, system structure, or operational details, even when a prompt contains no obvious secret.
Mark each category as prohibited from external processing, allowed only in a specifically approved service, or cleared for a controlled pilot. A sanitized repository is not automatically safe: check that sanitization removes sensitive information from the code, commit history, filenames, comments, fixtures, and other context the feature may use.
Separate features that store or index code
Some capabilities require a different assessment from sending a prompt for a response. For example, Google says its Gemini Code Assist code-customization feature accesses and stores private code to provide that service. Assess the storage, access, and deletion requirements for such a feature separately from chat or completion. Do not enable private-code indexing or customization as an incidental step in a pilot whose purpose is only to test suggestions.
Choose test material that is safe to process
Use one of three sources: synthetic code written for the evaluation, public code the organization is permitted to use, or a sanitized test repository explicitly cleared for external processing. The aim is to make tasks representative without making the underlying code sensitive. A test repository can reproduce languages, frameworks, conventions, and common engineering work while omitting production data and confidential implementation details.
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Build a useful but cleared task set
Choose tasks that reflect the team’s real work without using production code. Examples include asking for a small synthetic function, a test for a deliberately specified behavior, an explanation of a toy code sample, or a change in a cleared repository. Include ordinary tasks as well as cases that should make the tool refuse to act on unavailable information or expose inappropriate context. Record the task and expected outcome before comparing tools; otherwise, reviewers may favor polished-looking answers without checking whether they meet the same requirements.
Use the same cleared tasks when comparing configurations. Note the plan, enabled feature, selected model or provider, and relevant settings for each run. This makes it possible to distinguish a difference caused by the tool from one caused by different routing or permissions.
Verify the precise data path and applicable terms
Ask the vendor and check the live tenant settings and contract documentation for the configuration you intend to test. Save the answers and the date checked. Product documentation is a starting point, not a substitute for confirming the terms that apply to your organization and the controls actually enabled in your tenant.
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- Data sent: Which prompts, code snippets, repository context, metadata, telemetry, feedback, and outputs are collected or transmitted for each feature?
- Plan and feature: Which subscription, feature, and model are in use? Do chat, completion, indexing, code customization, and agent modes follow different rules?
- Training and product improvement: Is customer content used to train models or improve products? What settings, exceptions, or account-type distinctions apply?
- Retention and logging: How long may prompts, responses, feedback, or logs be retained? Can an administrator configure logging or deletion? Who can access retained data?
- Processing location: Where is content processed, and is processing in a specified region guaranteed or merely described as a possibility?
- Routing and subprocessors: Which model provider or subprocessors receive a request? What contractual commitments apply to them?
- Controls and evidence: Which administrative policies, access controls, and audit records can you verify, and do they cover every enabled feature?
Keep the questions distinct. “Not used for training” addresses a different issue from whether content is transmitted, temporarily processed, logged, retained, or available to a subprocessor. Likewise, a statement about default handling does not necessarily describe customer-configured logging or every feature in the product.
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Examples of why plan and routing matter
Google’s documentation for Gemini Code Assist Standard and Enterprise describes stateless processing and says prompts and responses are not stored in Google Cloud as part of that service. It also says customers can configure Cloud Logging to store inputs and responses, and that processing may occur near the request origin without guaranteeing regional processing. These are separate facts to assess against the organization’s logging and location requirements; one should not be inferred from another. The documentation was accessed October 9, 2026.
GitHub’s documentation says Copilot Business and Enterprise customer data is not used to train AI models, while interaction data from individual subscribers may be used subject to settings. GitHub also explains that bring-your-own-key (BYOK) sends prompts and responses to the selected provider, whose terms may apply; other agent actions may still use Copilot-integrated models. A BYOK setting therefore does not necessarily route every part of an agent workflow through one provider. The documentation was accessed October 9, 2026.
| Configuration described in official documentation | What the documentation establishes | What the team still needs to verify |
|---|---|---|
| Gemini Code Assist Standard and Enterprise | Google describes stateless processing and no prompt/response storage in Google Cloud for the service. Customers can configure Cloud Logging to store inputs and responses; regional processing is not guaranteed. (Google Cloud documentation, accessed October 9, 2026.) | Whether the team’s tenant has logging enabled, the resulting retention and access rules, the relevant feature’s data path, and whether the location commitments meet its requirements. |
| GitHub Copilot Business and Enterprise | GitHub says customer data for these plans is not used to train AI models. (GitHub documentation, accessed October 9, 2026.) | The precise feature and model in use, other processing and retention terms, tenant settings, and any applicable contractual or regional commitments. |
| GitHub Copilot individual subscribers | GitHub says interaction data may be used subject to settings. (GitHub documentation, accessed October 9, 2026.) | Which settings apply to the specific account, what data is covered, and whether the account is appropriate for the organization’s pilot. |
| GitHub Copilot with BYOK | Prompts and responses are routed to the selected provider, whose terms may apply; other agent actions may still use Copilot-integrated models. (GitHub documentation, accessed October 9, 2026.) | The provider’s terms and location, which actions use which model, and whether routing and logging are documented for the complete workflow. |
These examples are not a ranking or a finding that one service is suitable for a particular organization. They show why a decision must be tied to the specific plan, account, feature, provider, and settings, rather than a product name or a single privacy statement.
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A completion tool that offers text is different from an agent that can inspect a repository or take actions. For every feature, establish what files and repositories it can access. For an autonomous or cloud agent, also inspect its network access, shell or other tool use, credentials, write permissions, branch and workflow capabilities, auditability, and approval requirements.
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Use least privilege: grant only the repository and actions needed for the cleared pilot. Keep production credentials and sensitive repositories out of scope. Determine which operations require a person’s approval and whether the agent can trigger workflows or reach systems beyond the test environment. An agent’s ability to act changes the consequences of a bad instruction, exposed context, or unsafe output—not just the amount of code it can read.
GitHub acknowledges that its Copilot cloud agent can access sensitive information and could leak it. GitHub documents mitigations that include security checks, limits on triggering and branch capabilities, and human review before merge. Those controls are useful to examine, but they do not remove the need to review the permissions and safeguards for your own configuration. AWS guidance recommends validation and approval workflows for agents accessing or modifying critical data sources, and suggests broker controls when downstream systems lack adequate safeguards.
Run the pilot as a controlled evaluation
- Write down the boundary. Identify prohibited data, approved test material, permitted features, and the people authorized to run the evaluation. If a category cannot be processed by the proposed service, exclude it rather than relying on a prompt instruction to keep it private.
- Confirm the configuration. Record the plan, account type, selected model or provider, enabled features, relevant privacy and logging settings, and any agent permissions. Verify the terms for the actual configuration with the vendor’s documentation and applicable contract.
- Prepare an isolated test environment. Put only cleared code and test data in the repository. Check files, history, fixtures, and local configuration for sensitive material. Avoid connecting production credentials or granting unnecessary repository and network access.
- Observe context and routing. For each feature, determine what is included with a request and which provider handles it. Record relevant context, settings, and outcomes without copying sensitive material into the evaluation record.
- Use a fixed set of representative tasks. Give each configuration the same cleared tasks and success criteria. Test ordinary completion or chat separately from indexing and agent capabilities; they need not share the same data path or risk.
- Review output and actions. Have qualified reviewers check whether responses meet the task, whether code compiles and tests pass, and whether suggestions introduce security, dependency, or licensing concerns. For agents, inspect actions as well as final code.
- Document the decision and limits. Record what was verified, what remains unknown, which configuration was assessed, and what must be rechecked if the plan, provider, feature, contract, or tenant settings change.
If the vendor cannot clearly establish a data path or a required control, treat that point as unresolved. Keep sensitive code out of the pilot until the uncertainty is resolved or the organization approves a different, adequately controlled processing arrangement.
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Judge quality and security separately from privacy
A privacy control does not prove that generated code is correct, safe, or appropriate to merge. Assess output using the team’s normal secure software development lifecycle rather than treating a successful demonstration as a substitute for review. GitHub warns that generated code can be inaccurate or insecure and advises careful review and testing, particularly for security-sensitive applications. Its responsible-use guidance says: “You should be careful when using Copilot Chat to generate code for security-sensitive applications and always review and test the generated code thoroughly.” Google likewise instructs customers to validate generated output.
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For each cleared task, evaluate whether the answer satisfies the stated behavior, fits the team’s conventions, and can be maintained. Check whether suggested code compiles and whether tests exercise the intended behavior. Inspect for unsafe patterns, vulnerable or unnecessary dependencies, hidden assumptions, and code that needs license review. A fluent explanation or plausible patch is not evidence that the implementation is correct.
Keep two records rather than collapsing the findings into one score: one for data handling and access risk, another for engineering usefulness and output risk. A tool may perform well on cleared tasks while failing an organization’s data-location requirement; a configuration with acceptable controls may still produce output that needs substantial review. Each is a separate decision gate.
Compare qualifying configurations on the same criteria
Once a configuration meets the organization’s minimum data and access requirements, compare it with other qualifying configurations on evidence gathered for the same features and cleared tasks. Ask for specifics rather than accepting a product-level assurance.
- Data transmitted, including IDE context, repository access, and conversation history.
- Training and product-improvement use, including account or setting exceptions.
- Retention, logging, deletion, processing region, and subprocessor commitments.
- Administrative controls, access restrictions, and auditability.
- Model and provider routing, especially when BYOK or multiple agent actions are involved.
- Agent permissions, network exposure, write scope, and human approval gates.
- Output usefulness on representative cleared work, reviewed with the same criteria.
- Security testing, dependency checks, licensing review, and contractual terms relevant to the organization.
NIST’s AI Risk Management Framework is a voluntary framework for incorporating trustworthiness considerations into AI design, development, use, and evaluation. Its Generative AI Profile, NIST AI 600-1, was published July 26, 2024. NIST says the framework was developed with participation from more than 240 organizations across the private sector, academia, civil society, and government; that figure describes the framework’s development, not a measure of its effectiveness. The profile’s risk-management perspective can help teams document assumptions, assess potential harms, and make evaluation repeatable. NIST also recommends examining and documenting privacy risk and conducting AI red-teaming for risks such as disclosure of confidential or proprietary information. Tailor those activities to the organization’s threat model and data classification rather than treating a general assurance as proof that a configuration is safe.
Make approval conditional on evidence
Approve a pilot or broader use only for the specific configuration that was assessed. The decision record should name its plan, features, model or provider, relevant settings, test environment, and permitted data categories. State the controls the team verified and the issues that remain unresolved. If a vendor changes terms, the organization changes settings, or a new feature or provider is introduced, reassess the affected data path rather than assuming the previous review still applies.
The practical standard is straightforward: sensitive code stays outside the evaluation unless the organization has explicitly approved its processing under verified controls. A controlled pilot can still answer whether a tool is useful, but it does not make an unknown data path acceptable—and privacy approval never removes the need for engineering review of generated code.
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