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Freedom251 › Blog › AI coding assistants

AI Coding Assistant Privacy FAQ: What Happens to Your Source Code and Prompts?

By Freedom251 Team · · 10 min read

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Short answer: An AI coding assistant is a hosted data-processing service unless you have verified a genuinely local setup. The prompt, surrounding code snippets, repository metadata, and conversation history selected by the tool can leave your editor, pass through the assistant and one or more model providers, and be logged, cached, monitored, or used for training. The outcome depends on the exact product, plan, access route, model, privacy setting, and contract.

“Not used to train models” is only one part of the answer. You must also check operational retention, telemetry, abuse monitoring, temporary caches, subprocessors, geographic processing, administrator controls, and deletion terms before sending proprietary or regulated data.

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What data can an AI coding assistant send from my editor?

Typing a question into a chat box is only the visible part of a request. To generate a useful answer, an assistant may attach the open file, nearby lines, selected code, imported symbols, repository metadata, diagnostics, terminal output, prior conversation turns, or instructions from project files. An agent-style feature can gather additional files as it plans and executes a task.

The exact context is product- and feature-specific. A completion request may contain a narrow window around the cursor; a chat request can include files you explicitly attach; a codebase question may involve indexed snippets and repository structure. The request can then travel from the editor to the assistant’s service and onward to a model host or other subprocessors.

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  • Prompt content: the words you type, including pasted requirements and error messages.
  • Code context: snippets, open files, selected ranges, and material retrieved from the repository.
  • Conversation state: earlier turns or agent plans needed to maintain context.
  • Metadata: repository, workspace, account, model, extension, and request information.
  • Outputs and feedback: generated code, ratings, accepted-suggestion events, and other engagement signals.

Read the product’s context and indexing documentation as well as its privacy policy. A setting that excludes one file pattern does not necessarily exclude terminal output, chat history, or metadata.

Why are training, retention, telemetry, and caching different privacy questions?

They describe separate uses of the same request. A service can promise not to train a model on customer content while still retaining prompts briefly for abuse detection, storing logs selected by an administrator, collecting usage telemetry, or caching file contents long enough to complete a request.

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Training and model improvement

Training means using inputs, outputs, or derived signals to improve a model or service. Opting out of training prevents that stated use; it does not automatically erase operational logs or provider records.

Operational processing and retention

Processing is what happens while a request is routed, filtered, generated, and returned. Retention is how long copies remain afterward. A “zero-retention” commitment generally concerns ordinary request data, but the scope and exceptions must be read in the applicable policy.

Telemetry and engagement data

Providers may collect request events, response events, reactions, accepted-suggestion counts, performance data, or security signals without using the underlying code to train a model. Those records can still reveal how a team works and may have their own retention period.

Caching and abuse monitoring

Temporary caches can hold file contents while a request is serviced. Abuse classifiers and security investigations can create exceptions to ordinary deletion promises, so ask what triggers an exception, who can access the material, and when it is removed.

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Does GitHub Copilot store or train on my code?

The answer changes with the Copilot plan and access path. GitHub’s March 25, 2026 policy update says interaction data from individual Copilot Free, Pro, and Pro+ users will be used to train and improve models unless the user opts out, beginning April 24, 2026.

“From April 24 onward, interaction data—specifically inputs, outputs, code snippets, and associated context—from Copilot Free, Pro, and Pro+ users will be used to train and improve our AI models unless they opt out.” — Mario Rodriguez, GitHub, March 25, 2026

That interaction-data definition is broader than the text entered in chat: it includes inputs, outputs, code snippets, and associated context. GitHub says the update does not affect Copilot Business and Enterprise. It also says private-repository content stored at rest is not used for training, while interaction data generated when you actively use a private repository can be covered by the individual-plan policy unless you opt out.

What does GitHub retain?

GitHub’s product FAQ identifies prompts, suggestions, feedback, and engagement data as processed data. For Business and Enterprise IDE chat and code-completion, prompts and suggestions are not retained by default. Other access paths retain prompts and suggestions for 28 days, while engagement data is kept for two years. Those periods apply to the described access paths, not to every Copilot feature or geography.

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If your organization uses an individual plan for company code, do not assume the repository’s private status makes live interactions private. Confirm the training opt-out, the client surface being used, and the account type actually signed in.

Does Cursor train on my code?

Cursor says requests send prompts and code context to providers such as OpenAI, Anthropic, and Google. Its Privacy Mode is designed to prevent Cursor and those providers from using code for training, and team or enterprise administrators can enforce the setting.

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Cursor’s September 3, 2026 data-use overview says Privacy Mode uses zero-data-retention agreements with providers. It also says Cursor temporarily caches file contents to service requests and describes that cache as encrypted and temporary when Privacy Mode is enabled. An abuse classifier can create an exception when content triggers an investigation, so zero retention is not an unconditional promise that no copy can ever exist.

What happens when Privacy Mode is off?

With Privacy Mode disabled, Cursor says it may use and store codebase data, prompts, editor actions, snippets, and other code data to improve features and train models. Treat the toggle as a data-use control, not a cosmetic preference: verify its state for every workspace and make sure an administrator policy cannot be overridden by a personal setting.

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What does Gemini Code Assist store?

Google Cloud describes Gemini Code Assist Standard and Enterprise as stateless Google Cloud services: prompts and responses are not stored in Google Cloud by default. Customers can configure Cloud Logging, which changes what their own environment records. Google says it does not use customer data to train models without permission.

Stateless service handling does not mean no telemetry. Google separately collects service signals such as request events, response events, reactions, and accepted-suggestion character counts. The applicable notice depends on whether access is through Standard, Enterprise, GitHub, or Cloud Code, so identify the edition and route before relying on a general statement about Gemini Code Assist.

Why grounding features need a separate check

Google’s zero-data-retention guidance for its Gemini API illustrates an important qualification: abuse monitoring and grounding features can require limited storage. Search or Maps grounding, for example, can involve retention for 30 days. That guidance is not a universal retention period for every Code Assist request; it shows why a “no training” statement must be read alongside feature-specific storage terms.

Can model providers and subprocessors see my prompts?

Often, yes. The assistant may be an orchestration layer that forwards your request to a selected model host. Review both documents: the coding assistant’s privacy policy and the provider commitment for the model actually selected.

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GitHub’s model-hosting documentation gives different commitments by provider. Amazon Bedrock does not store or log prompts and completions or use them to train AWS models. Google Cloud says it does not train on GitHub data. Other providers can have separate zero-retention or caching commitments. A model switch, fallback route, or newly enabled feature can therefore change the data path without changing the editor extension.

For procurement and security review, record the provider name, processing region, subprocessors, support-access rules, breach obligations, deletion process, and whether the commitment is contractual or merely a product setting.

How do the major assistants differ?

The following comparison reflects the described policies and their stated conditions. It is not a permanent guarantee: plans, access routes, models, and settings can change.

Assistant and route Context and data sent Training treatment Retention and monitoring Controls to verify
GitHub Copilot Free, Pro, Pro+ Inputs, outputs, code snippets, and associated context; private-repository interactions can be included while actively using the repository Used to train and improve models from April 24, 2026 unless the user opts out Prompts and suggestions on other access paths: 28 days; engagement data: two years Individual-plan training opt-out; confirm the client and access path
GitHub Copilot Business and Enterprise IDE chat and code-completion prompts and suggestions are processed Not affected by the March 25, 2026 individual-plan update IDE chat and code-completion prompts and suggestions are not retained by default Organization policy, account type, and any non-IDE access path
Cursor with Privacy Mode Prompts and code context are sent to Cursor and selected providers Privacy Mode is intended to prevent Cursor and providers from using code for training Zero-data-retention provider agreements; encrypted temporary file cache; abuse-investigation exception Privacy Mode state and administrator enforcement
Cursor with Privacy Mode off Prompts, codebase data, editor actions, snippets, and other code data may be used May be used to improve features and train models Cursor says it may use and store the listed data Workspace setting, team policy, and selected model provider
Gemini Code Assist Standard and Enterprise Prompts, responses, and service telemetry; exact notice depends on access route Google says customer data is not used to train models without permission Prompts and responses not stored in Google Cloud by default; Cloud Logging and telemetry are configurable or collected separately Edition, GitHub or Cloud Code route, logging configuration, and grounding features
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How should a team configure an AI coding assistant safely?

Use this sequence before approving the tool for proprietary repositories.

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  1. Inventory the exact path. Record the vendor, plan, IDE or web surface, account type, selected model, agent features, and any grounding or repository-indexing option.
  2. Read the edition-specific policy. Check training use, prompt and output retention, telemetry, abuse monitoring, subprocessors, geographic processing, and deletion controls. Do not rely on a policy for a different plan.
  3. Set the strongest available control. Enable GitHub’s training opt-out where available, enforce Cursor Privacy Mode for team members, and review Gemini Cloud Logging and feature settings.
  4. Minimize context. Exclude secret files, production logs, customer exports, and regulated data from indexing and attachments. Restrict agent permissions and repository scope to what the task needs.
  5. Review contracts. For business use, obtain the data-processing agreement, subprocessor list, regional commitments, security terms, incident notice window, and deletion procedure.
  6. Test the configuration. Send a non-sensitive marker through the exact IDE and model route, inspect available logs, and confirm that policy settings hold after sign-out, model changes, and workspace changes.
  7. Recheck on change. Repeat the review when switching from an enterprise account to an individual account, enabling an agent or grounding feature, adding a provider, or changing regions.

What should never be pasted into a coding assistant?

Unless your organization has approved the exact data flow, keep these out of prompts, attachments, indexed repositories, and terminal output:

  • Cloud, database, package-registry, and CI credentials
  • Private keys, signing keys, recovery codes, and session tokens
  • Production connection strings and unredacted logs
  • Personal data covered by privacy or sector regulations
  • Customer source code or contract-restricted material
  • Security incident details that are not cleared for external processing

Use placeholders and sanitized reproductions instead. Secret scanning and repository exclusion rules reduce accidental exposure but do not replace a policy decision about whether the service is approved.

What if a secret or proprietary file was already sent?

  1. Revoke first. Rotate the exposed credential, invalidate tokens, and replace affected keys before investigating retention.
  2. Preserve the facts. Note the account, plan, IDE, model, timestamp, workspace, feature, and exact files or text included.
  3. Request deletion. Use the provider’s documented deletion and privacy channels, and ask which subprocessors received the request and whether abuse-monitoring exceptions apply.
  4. Check downstream systems. Review assistant logs, Cloud Logging, provider dashboards, repository audit logs, and security alerts for continued access.
  5. Document notification duties. Your legal, security, or privacy team should determine whether the material triggers contractual or regulatory reporting.

How often should privacy settings be reviewed?

There is no universal retention period or permanent privacy classification for AI coding assistants. Vendor policies change, and the same product can behave differently by plan, model, feature, geography, and access route. Put the review on a change-management schedule and trigger it whenever any of those variables changes.

Frequently Asked Questions

Does autocomplete send my entire repository?

Not necessarily. Completion commonly uses a context window, while chat, indexing, and agent features can retrieve additional snippets or metadata. The extension’s context, exclusion, and indexing controls determine the practical scope, so verify those settings rather than assuming either “only the current line” or “the whole repository.”

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Is a locally running model automatically private?

No automatic conclusion follows from the model being described as local. The editor may still send telemetry, license checks, crash reports, or selected context to a hosted service, and plugins can add their own data paths. Confirm network behavior and the product’s documented commitments for the exact installation.

What does zero data retention allow during a security investigation?

It can still allow a narrowly defined exception for abuse or security review. Cursor’s description, for example, says an abuse classifier can lead to an investigation even with Privacy Mode enabled. Ask how content is isolated, who can access it, and when the investigation copy is deleted.

Can deleting a chat remove every provider copy?

Not always. Deletion may cover the assistant’s conversation record while a configured customer log, telemetry record, backup, or abuse-review copy follows a different schedule. Request the retention map and deletion scope for the specific plan, provider, and access route.

What should a vendor privacy review request include?

Ask for the current plan-specific policy, data-flow diagram, model and subprocessor list, processing regions, retention periods, training position, abuse-monitoring exceptions, administrator controls, DPA, incident terms, and deletion procedure. Require the commitments to cover the models and features your developers will actually use.

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