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Yes, you can use an AI coding assistant with private code—but only after you verify the exact product, account, feature, contract and data path your organization will use. “No training” is not a complete privacy answer. You also need to establish what is retained, who can access it, where it is processed, which subprocessors and model host receive it, whether connected tools can read more data, and what legal or compliance approval is required.
Use an approved business account, send the minimum context needed for the task, exclude secrets, and subject every generated change to normal review, testing and security controls. If you cannot determine where covered code goes or which terms govern it, do not submit that code until the designated security, privacy or legal owner approves a suitable configuration.
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What “safe” use means
An assistant is not simply safe or unsafe. Risk depends on a combination of product design and operating choices:
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- Feature: Inline completion, chat, repository indexing, agent mode, terminal access, retrieval and connected applications may process different data.
- Model host: A service may route a request to its own model or to a selected third-party provider.
- Data lifecycle: Training use, transient processing, service logs, abuse monitoring, backups, deletion and legal holds are separate questions.
- Permissions: An agent that can read a repository, execute shell commands or call external services has a larger blast radius than a text-only completion tool.
- Governance: Your organization’s confidentiality, export-control, privacy, regulatory and intellectual-property rules may be stricter than a provider’s default settings.
Document these dimensions for each approved configuration. A policy that covers one assistant or plan should not automatically be applied to another.
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A local client does not necessarily keep code local
“Runs locally” often describes the editor, command-line client or orchestration process—not the model inference. Anthropic’s Claude Code documentation says the tool runs locally while sending user prompts and model outputs over the network to interact with the language model. Read the data-flow description for the exact feature you intend to use, including automatic context collection and telemetry: Anthropic’s Claude Code data-usage documentation.
The same principle applies to desktop extensions, self-hosted gateways and “private” network deployments. A local process can still transmit source files, file names, prompts, diagnostics, embeddings, tool results or generated patches. Confirm the destination, encryption in transit, logging points and deletion behavior rather than inferring them from the word “local.”
Pre-use approval checklist
1. Identify the exact account, plan and contract
Record the vendor, product edition, workspace or organization, model, region and enabled features. Capture the agreement that governs the account, not only a marketing page. OpenAI states that Codex data is governed by the applicable ChatGPT terms for an individual account or the relevant online service agreement for business, enterprise, education or API users: OpenAI’s Codex plan guidance. GitHub likewise distinguishes individual use from business and enterprise arrangements in its model-hosting documentation: GitHub’s model-hosting documentation.
2. Verify training and improvement settings
Ask whether prompts, source context, outputs, feedback and telemetry may be used to train or improve models; whether that differs by plan; and whether an administrator must opt out or opt in. OpenAI says business product and API inputs and outputs are not used for training by default, while individual content may be used unless the applicable control is changed. Its help page also describes a distinct environment-training setting for Codex: OpenAI’s data-use controls. Treat the setting as one control, not as proof that the service has no retention or access.
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3. Establish retention, deletion and backup rules
Obtain feature-level retention periods, deletion triggers, backup treatment, abuse-monitoring exceptions and legal-hold procedures. Do not assume that a chat transcript, indexed repository, prompt log and API request share one policy. For example, OpenAI describes API inputs and outputs as potentially retained for up to 30 days for service and abuse monitoring, with endpoint and feature exceptions; eligible customers may request zero data retention. That statement applies to the described API circumstances, not automatically to every OpenAI product or endpoint. Check the current endpoint documentation and agreement at OpenAI Enterprise Privacy.
4. Locate processing and list subprocessors
Identify the model host, cloud region, support access, subprocessors and any transfer mechanism used for your jurisdiction. Ask whether a model selection, “bring your own key” (BYOK) option or fallback model changes the recipient. Keep the provider’s subprocessor list and your approved model list with the service record so a later model change does not silently change the data path.
5. Check access, encryption and audit controls
Confirm encryption in transit and at rest, administrator roles, single sign-on, multifactor authentication, SCIM or lifecycle management, audit logs, export controls and support-personnel access. Determine whether administrators can disable public sharing, personal accounts, unapproved models, repository indexing, terminal execution and external connectors. Test that offboarding removes tokens and repository access promptly.
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6. Inventory integrations and automatic context
List every source that can be attached to a request: open files, neighboring files, repository history, issues, pull requests, tickets, documentation, terminal output, environment variables, cloud drives and third-party applications. Disable integrations that are not required. A prompt that appears to contain one function may include additional context selected by an extension or agent.
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7. Define permission boundaries
Decide whether the assistant may only suggest text or may edit files, run tests, execute shell commands, install packages, access networks, create pull requests or modify production systems. Use separate low-privilege credentials, read-only tokens and isolated worktrees where possible. Require confirmation for destructive, networked or externally visible actions.
8. Obtain the right organizational approval
Route covered use cases to the owner named in your policy. GitHub’s approval resources describe common involvement from legal, privacy, compliance and cybersecurity teams and point to product terms and governance controls: GitHub’s resources for approval. Approval should name the permitted plans, repositories, data classes, models, regions, integrations and review obligations.
Provider and plan differences you must verify
The following comparison captures documented distinctions, not a universal safety ranking. Policies and model routing can change, so recheck the linked pages before approving a deployment.
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|---|---|---|---|
| OpenAI business products and API | OpenAI says business product and API inputs and outputs are not used to train models by default; explicit opt-in possibilities can apply. | Review encryption, role and access features, retention controls, endpoint-specific logs, model host and subprocessors. OpenAI’s enterprise page describes API retention of up to 30 days for service and abuse monitoring in stated circumstances, with exceptions and eligibility-based zero-data-retention options. | Do not transfer the business/API default to an individual ChatGPT or Codex account, another endpoint or another feature. The governing agreement depends on the account type. |
| OpenAI individual ChatGPT or Codex | Individual content may be used to improve models unless the applicable control is changed. Codex has a distinct environment-training setting. | Check the current consumer terms, account controls, feature logs and any connected tools separately. | An individual setting is not a substitute for an organization-approved business agreement. |
| GitHub Copilot Business or Enterprise | GitHub says it does not use Copilot Business or Enterprise customer data to train models. | Verify the selected model host, provider-specific handling, organization policy, retention, audit and repository permissions. Hosting and handling may vary by model. | “No training” does not answer retention, access, residency, subprocessors or connected-tool questions. |
| GitHub Copilot individual plans | Individual-plan data can be treated differently and may be eligible for training use depending on settings and current policy. | Review the current individual settings, model-hosting details and feature-specific terms. | Do not infer business or enterprise protections from an individual subscription. |
| GitHub Copilot with BYOK | The selected provider’s policy governs the prompt and response handling. | Prompts and responses go to the chosen provider, so inspect that provider’s retention, training, residency, subprocessors and access terms. | GitHub’s default handling does not replace the selected provider’s obligations. |
| Anthropic Claude Code | Anthropic describes training treatment as subject to applicable terms, explicit programs and choices for the product. | The client runs locally but sends prompts and model outputs over the network to interact with the model. Confirm current model-hosting, retention, telemetry and subprocessor terms. | Review the current product documentation for your region and edition; a local client is not local inference. |
For GitHub Copilot, prompts may be processed with source code and other context; suggestions can be inaccurate or resemble public code. GitHub also warns that, in a BYOK configuration, prompts and responses are sent to the selected provider and may follow that provider’s retention and privacy policies. See GitHub’s responsible-use guidance.
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A least-exposure workflow for developers
- Classify the task before opening the assistant. Mark the repository and specific files as public, internal, confidential, regulated or trade secret. Check whether the approved configuration permits that class. If classification is unknown, stop and ask the data owner.
- Use the approved identity. Sign in through the organization’s managed workspace, not a personal account. Verify the organization, model and region shown by the editor or command-line client before starting a session.
- Prepare a narrow working context. Open only the files needed to explain the behavior. Prefer a small, sanitized reproduction, interface definition or pseudocode over an entire repository. Remove proprietary comments, customer identifiers, unique algorithms and unnecessary history.
- Block secrets before prompting. Never paste passwords, API keys, private certificates, session tokens, database connection strings, signing material or production data. Add secret-scanning hooks and repository ignore rules, but do not treat an ignore file as proof that an extension cannot read a file. Rotate a credential immediately if it appears in a prompt, response, terminal transcript or log.
- State constraints in the prompt. Specify the language version, permitted libraries, security requirements, license policy and files the assistant may change. Ask for a plan or patch rather than unrestricted repository changes. Avoid including a real customer record when a synthetic example answers the same question.
- Constrain tools and execution. Run agent work in a disposable branch or isolated worktree with least-privilege credentials. Require confirmation before package installation, network access, file deletion, credential use, deployment or pull-request creation. Review every command before it runs.
- Inspect the complete diff. Look for hard-coded secrets, weakened authentication, unsafe deserialization, shell injection, SQL injection, path traversal, insecure defaults, new network calls, dependency changes and license notices. Check generated tests as critically as production code.
- Run ordinary engineering controls. Execute unit, integration and regression tests; linters; type checks; dependency and secret scanners; static analysis; and, for material changes, dynamic testing or a penetration review. Preserve the assistant’s prompt and output only where your retention policy permits it, and record the human reviewer and tool version in the change record.
- Sanitize feedback. When reporting an error to the provider, send the smallest reproducible excerpt. Remove credentials, customer data and unrelated proprietary code from diagnostics and screenshots.
- Close the session deliberately. Revoke temporary tokens, delete disposable worktrees according to policy, remove downloaded artifacts and confirm that repository indexing or connector access is disabled when the project leaves the approved scope.
Choose controls by data classification
| Data class | Typical assistant use | Minimum controls | Escalation trigger |
|---|---|---|---|
| Public code | Documentation, refactoring and test suggestions in an approved account. | Normal review, dependency checks and secret scanning. | Any request to connect private repositories or production systems. |
| Internal code | Assistance inside an organization-managed workspace with approved model and region. | Business or enterprise agreement, SSO, audit logs, restricted connectors and documented retention. | Model or subprocessor change, BYOK routing or cross-border processing. |
| Confidential or trade-secret code | Only narrowly scoped tasks after owner approval. | Minimized context, no secrets, isolated worktree, least-privilege tokens, explicit retention and access review, human security review. | Unclear destination, individual account, unapproved model or inability to delete data. |
| Regulated, export-controlled or customer-provided code | Use only where legal, contractual and regulatory assessments expressly permit it. | Documented data-processing terms, residency and transfer analysis, logging, approved subprocessors and specialized review. | Any missing contractual permission or unresolved residency, deletion or access requirement. |
Agent permissions deserve separate review
Text completion and autonomous agents are not equivalent risk profiles. Before enabling an agent, answer these questions in writing:
- Can it read ignored files, parent directories, repository history or sibling repositories?
- Can it execute arbitrary shell commands, access the network or install dependencies?
- Which environment variables, cloud credentials and SSH agents are visible?
- Can it open or merge a pull request, change CI workflows or publish an artifact?
- Are commands sandboxed, logged and subject to human confirmation?
- Can a prompt injection in a README, issue or dependency instruct the agent to exfiltrate data or bypass a rule?
Use read-only credentials and a sandbox for exploratory work. Keep production credentials and deployment paths outside the agent’s environment. Treat repository text and tool output as untrusted input, not as administrator instructions.
Review generated code as untrusted code
Generated code can compile and still introduce a vulnerability, violate an internal standard or reproduce a license obligation. GitHub explicitly recommends secure coding, code review and testing, and says Copilot should not replace a comprehensive security review or penetration testing. Its guidance includes the instruction: “You should always follow best practices for secure coding, such as avoiding hard-coded passwords or SQL injection vulnerabilities, as well as following code review best practices, to address Copilot Chat’s limitations.” See GitHub’s security-debt guidance.
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- Does the change preserve authentication, authorization, tenant isolation and input validation?
- Does it introduce a new parser, network endpoint, subprocess, cryptographic operation or dependency?
- Are errors, logs and metrics free of secrets and personal data?
- Do tests cover abuse cases, malformed input, concurrency, rollback and failure recovery?
- Does the dependency have a compatible license and a maintained release history?
Licensing and provenance
Keep your normal open-source review process. Check dependency licenses, generated notices and any tool feature that identifies matching public code. Do not assume that an assistant’s output is original, conflict-free or automatically compatible with your project’s license. A human reviewer should decide whether a contribution can be merged.
What to do if code or secrets were exposed
- Contain: Stop the session, disable the connector or token and prevent the affected branch or artifact from spreading.
- Rotate: Revoke and replace any credential, certificate or signing key that appeared in context, terminal output or logs.
- Preserve evidence: Record the account, feature, model, time, repository, files, prompts, recipients and retention setting, subject to your incident policy.
- Notify the owner: Involve security, privacy, legal and the system or customer-data owner according to your escalation plan.
- Assess obligations: Determine whether contractual notice, regulatory reporting, customer communication or deletion requests apply.
- Correct the control: Remove the unapproved account or integration, update prevention rules and retrain users before resuming.
When you should not submit proprietary code
Pause the use case when any of these conditions applies:
- The user is signed into an individual account for a business-controlled repository.
- The provider, model host, region or subprocessor cannot be identified.
- Retention or deletion terms are missing, contradictory or incompatible with the project.
- The feature sends prompts to a BYOK provider that has not been approved.
- The assistant can access credentials, production systems or unrelated repositories without a tested boundary.
- No owner is available to approve confidential, regulated or customer-provided code.
In those cases, use a non-sensitive reproduction, a sanctioned internal service or a conventional development method while the owner evaluates the configuration. Do not try to solve an unresolved contractual or residency question by merely deleting a few lines from the prompt.
A practical approval record
For each assistant configuration, maintain a short record containing:
- Product, plan, account owner, model and enabled features.
- Governing agreement, training setting, retention period and deletion process.
- Model host, region, subprocessors, encryption and support-access terms.
- Repository and connector scope, agent permissions and credential boundaries.
- Permitted data classifications and prohibited examples.
- Required review, testing, scanning, licensing and incident procedures.
- Approval date, reviewer names and a scheduled policy recheck.
Recheck the record whenever the provider changes models, hosting, terms, retention, connectors or administrator controls. Volatile settings should be verified against the current provider documentation, not an old screenshot or a colleague’s recollection.
The decision rule
Use an AI coding assistant with private code only when the organization can explain the complete data path and has approved the exact configuration. Minimize context, keep secrets out, constrain permissions and review the resulting code like any other untrusted contribution. If the destination, retention, access or governing agreement remains uncertain, the safe decision is to withhold the proprietary code until an authorized reviewer resolves the uncertainty.
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