You can evaluate AI coding tools without uploading proprietary production code: use synthetic, public code you are permitted to use, or a sanitized repository explicitly cleared for external processing. Then verify what the exact plan, model, IDE feature, and agent configuration transmit and retain before testing with that material.
The prompt box is only one possible part of the data flow. An extension may include open or adjacent files, repository context, IDE state, or conversation history. And a promise that prompts are not used for training does not, by itself, tell you whether they are transmitted, temporarily processed, logged, or retained.
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What you can—and cannot—learn without production code
A safe pilot can tell you whether a tool fits your team’s languages, workflows, and review practices without exposing code that must remain inside your organization. It can assess whether suggestions are useful, compile, satisfy a task, and come with tests. It can also reveal how the tool behaves when given realistic but cleared context.
It cannot prove that a vendor’s service is safe for every repository, plan, or configuration. Data handling differs by plan, model provider, and feature. A completion feature, chat, code indexing, private-code customization, bring-your-own-key (BYOK) routing, and an autonomous agent may each involve different data and permissions. Evaluate the precise configuration you intend to deploy, not a product name in the abstract.
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Keep two decisions separate throughout the evaluation:
- Privacy and access: What leaves your environment, who processes it, what is retained, and what the tool can access or change.
- Quality and security: Whether generated code is correct, maintainable, licensed appropriately, and safe for your use case.
Passing one test does not pass the other. A privacy control does not establish that generated code is secure, and a useful suggestion does not establish that its data path is acceptable.
Start by drawing a firm boundary around sensitive data
Before enabling a tool, identify what your organization cannot send to the proposed service. Make the boundary specific enough that a developer can apply it during a pilot. Depending on your classification scheme, it may cover source code, secrets, credentials, customer data, internal architecture, or regulated information. State whether data outside that boundary may be processed externally and under what conditions.
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Include more than committed source files in the boundary. Secrets can appear in local configuration, environment files, logs, test fixtures, comments, terminal output, or copied error messages. Internal architecture can be revealed by file names, paths, service identifiers, and the shape of a repository even when a snippet looks harmless. Decide which of those categories need to be excluded or sanitized.
If the tool offers private-code indexing or customization, assess it as a separate data-processing feature. Do not assume it has the same handling as a stateless chat request. Google says Gemini Code Assist code customization accesses and stores private code to provide the service; an organization considering that feature should examine its storage, access, and deletion arrangements separately from ordinary prompts.
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Build a pilot with cleared code
Choose representative, low-risk material
Use synthetic code, public code that your organization is permitted to use, or a sanitized test repository that has been explicitly cleared for external processing. A synthetic repository can reproduce the shape of a real task—such as a service boundary, a failing test, or a dependency update—without copying confidential implementation details. Sanitization should remove secrets and sensitive identifiers, not merely rename a few variables.
Prefer a small, purpose-built repository over a developer’s normal working checkout. Keep production source, credentials, customer records, and unrelated internal files out of the pilot environment. Record who approved the test material and what categories it contains, so that the pilot’s boundary is auditable.
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Observe the actual context sent
Do not assume that an IDE extension sends only highlighted text. Depending on the feature and request, context can include open or nearby files, repository information, the conversation history, and IDE state. During testing, inspect the tool’s documentation, configuration, and available request or audit views to establish what context is included. If you cannot determine what the exact feature sends, do not test it with data that requires protection.
Use controlled prompts that make the context question observable. For example, put a harmless synthetic identifier in an adjacent file and ask a task that might need it. Check whether the tool uses that information and whether its documentation or administration controls describe the context transfer. This is a way to examine the feature’s behavior on cleared material, not proof that every request has identical context.
Keep a record of the configuration
For every pilot run, record the product plan, enabled feature, selected model or provider, relevant settings, and whether the request used chat, completion, indexing, customization, BYOK, or an agent. These details matter because controls that apply to one plan or route may not apply to another. A result from one configuration should not be generalized to the entire product.
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Verify the data path before running the pilot
Review the live tenant settings and contract documentation for the configuration you plan to test. Ask the vendor or administrator to answer the following questions in concrete terms, and keep the answers with the pilot record:
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- Routing: Which plan and feature are enabled, and which model or provider handles each request? Does a user-selected model route all activity or only some of it?
- Training and improvement: Is content used to train models or improve products? Which settings, exceptions, account types, or terms change that answer?
- Retention and logs: How long are prompts, responses, feedback, and logs retained? Can an administrator configure logging or deletion? What is stored by the service versus by the customer?
- Location and subprocessors: Where is processing performed, is regional processing guaranteed, and which subprocessors handle the request? What contractual commitments apply to them?
- Control and audit: Which administrative policies, access controls, and audit records are available, and can they be applied consistently to the pilot’s users and features?
Treat these as separate questions rather than accepting a single “private” or “not used for training” description. Training use, temporary processing, retention, logging, processing location, and deletion describe different parts of handling.
Why plan and routing details matter
Google Cloud’s documentation for Gemini Code Assist Standard and Enterprise says prompts and responses are not stored in Google Cloud because the service is stateless. It also says customers can configure Cloud Logging to store inputs and responses, and that processing may occur near the request’s origin without guaranteeing regional processing. Those statements address different controls: service storage, customer-configured logging, and processing location should each be considered on their own.
GitHub’s model-hosting documentation says customer data for Copilot Business and Enterprise is not used to train AI models, while interaction data from individual subscribers may be used according to their settings. GitHub also explains that BYOK routes prompts and responses to the selected provider, whose terms may apply; other agent actions may still use Copilot-integrated models. That makes the selected plan and actual route essential parts of the evaluation.
These are examples of why a vendor-level label is not enough, not a universal ranking or a substitute for checking your own contract and current tenant settings. Published documentation is a useful starting point; confirm that the relevant terms apply to your plan, geography, and enabled features.
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Review permissions before enabling an agent
Completion and chat features can expose context. An autonomous agent can add the ability to take actions, so inspect permissions and approval gates before allowing it into a repository or workflow.
Check access and actions
- Repository scope: Which repositories, branches, and files can the feature read? Can access be limited to a dedicated pilot repository?
- Network access: Can it reach external services or internal systems? Which destinations are allowed?
- Tools and execution: Can it use a shell, run tools, install dependencies, or trigger workflows? What actions can those tools perform?
- Credentials: Can it see tokens, environment variables, secrets, or credentials available to the workspace or workflow?
- Write scope: Can it modify files, push branches, open changes, or alter configuration? Can write access be limited?
- Approval and records: Which actions require human approval, and what is recorded for review?
Begin with the smallest practical scope. Keep credentials and production access out of the pilot, limit repositories and write permissions, and require review before changes can affect shared branches or workflows. A developer’s normal approval process should remain in place; an agent’s ability to produce a change is not authorization to merge or deploy it.
GitHub’s documentation on Copilot cloud agent acknowledges that the agent can access sensitive information and could leak it. It describes mitigations including security checks, limits on triggering and branch capabilities, and human review before merge. AWS guidance recommends validation and approval workflows for agents that access or modify critical data sources, and suggests broker controls when downstream systems lack adequate safeguards. Apply the same principle to a pilot: restrict what the agent can reach and require a person to approve consequential actions.
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Choose tasks that reflect the team’s actual work but can be completed using cleared material. Cover the languages and workflows that matter, such as understanding a small codebase, writing a test, explaining a function, or proposing a contained change. Use the same task set and review criteria across configurations if you are comparing tools.
For each result, record whether the suggestion:
- Addresses the task as written and fits the surrounding code.
- Compiles or runs and passes the relevant tests.
- Includes useful tests or explains assumptions that need checking.
- Adds unsafe dependencies, weakens validation, or introduces a security issue.
- Contains code or material that needs license review under your organization’s rules.
- Requires a level of correction that changes the time or effort saved.
Do not treat a plausible explanation or passing compilation as proof of correctness. Review generated changes with the team’s ordinary code review, testing, dependency, and secure-development practices. GitHub’s responsible-use guidance warns that generated code can be inaccurate or insecure and advises careful review and testing, particularly for security-sensitive applications. Google also instructs customers to validate generated output.
Privacy tests belong alongside, not instead of, output tests. NIST’s Generative AI Profile recommends examining and documenting privacy risks and conducting AI red-teaming for risks such as revealing confidential or proprietary information. Adapt those exercises to your organization’s data classification and threat model. A vendor’s general assurance is not evidence that a particular configuration is safe for every category of data.
Compare qualifying tools on the same dimensions
Once a configuration passes your organization’s minimum privacy and access requirements, compare it on the dimensions below. Record evidence for the exact plan and feature rather than relying on broad product claims.
| Dimension | What to establish |
|---|---|
| Data sent | Prompt, code, repository context, IDE context, metadata, telemetry, feedback, and outputs included in each feature. |
| Training and product improvement | Whether content is used, what settings or exceptions apply, and which account or plan terms govern it. |
| Retention and deletion | Retention periods and controls for service data, customer-configured logs, and feedback. |
| Processing and subprocessors | Processing locations, whether regional processing is guaranteed, and relevant subprocessor commitments. |
| Administration and audit | Access policies, administrative controls, and records available for oversight. |
| Model and provider routing | Which provider handles each feature and what changes under BYOK or other routing choices. |
| Agent capabilities | Repository and network access, tools, credentials, write scopes, approval gates, and workflow permissions. |
| Output quality | Task completion, test results, security review, dependency risk, and licensing review on cleared tasks. |
| Contract and assurance | Terms and commitments applicable to your plan, jurisdiction, data classification, and use case. |
Set minimum acceptable conditions before comparing convenience or quality. For example, a configuration that cannot meet a required data-location or access-control condition should not advance simply because its pilot suggestions score well. Where evidence is incomplete, document the specific unanswered point and decide whether the organization can accept that uncertainty for the intended data class.
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- Classify the data. Identify prohibited and conditionally permitted information, including secrets, customer data, regulated information, and sensitive architectural details.
- Select the exact configuration. Name the plan, enabled feature, model or provider, logging settings, and any agent capabilities being considered.
- Confirm the data path. Review settings and applicable documentation or contract terms for context sent, training use, retention, logging, deletion, processing location, and subprocessors.
- Constrain the environment. Use a cleared repository with minimal permissions, no production credentials, and only the network and tools required for the test.
- Run representative tasks. Use a consistent set of cleared tasks and record both output quality and any observed context or permission behavior.
- Review risks and results. Have the appropriate security, privacy, legal, and engineering reviewers examine the evidence against the organization’s requirements.
- Document the decision. Approve only the tested configuration and data categories. Reassess if the plan, provider, model, feature, settings, or agent permissions change.
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 was published on July 26, 2024. NIST describes the AI RMF as developed with participation from more than 240 private-sector, academic, civil-society, and government organizations; that figure describes the framework’s development, not a measure of its effectiveness. Teams can use the framework to structure risk management, but it does not certify a coding tool or replace configuration-specific review.
When the pilot should stop
Pause rather than expand the test if the team cannot establish what context a feature transmits, cannot separate its configuration from production access, or cannot determine which provider receives the request. The same applies if required retention, logging, location, or contractual conditions are unresolved. The next step is to seek a concrete answer or test only a lower-risk configuration with cleared material—not to infer safety from a generic privacy statement.
Also stop or redesign the pilot if an agent has access to credentials or systems beyond its task, if consequential actions lack approval, or if reviewers cannot examine what it changed. For output evaluation, a pattern of failed tests, unsafe changes, or unreviewed dependencies is a quality and security concern even when no sensitive code was sent.
Make the decision specific, not universal
There is no universally safest AI coding tool for every organization. The appropriate choice depends on jurisdiction, data classification, contract, plan, model provider, enabled features, configuration, and security requirements. A pilot can support a decision about one tested combination; it cannot establish that every plan or mode from the same vendor is appropriate.
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