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Short answer: An AI coding assistant should be treated as a hosted data-processing service, not as a feature that runs entirely inside your editor. A request can include your prompt plus surrounding code, file excerpts, repository metadata, and conversation history. That material may pass through the assistant and one or more model providers, then be logged, cached, inspected for abuse, or used to improve models depending on the product, plan, access route, and settings.
“Not used for training” is only one privacy question. You must also establish what is sent, how long it is retained, who processes it, where processing occurs, what administrators can control, and whether a zero-retention promise has exceptions. The sections below explain the differences and give a practical way to approve an assistant for personal or business code.
What data can an AI coding assistant receive?
The text you type is only part of a typical request. To produce a useful completion or answer, an assistant may collect context automatically from the editor, project, or conversation.
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- Code context: the open file, selected lines, nearby functions, related files, and snippets retrieved from the repository.
- Project information: file paths, language, symbols, repository identifiers, branch or workspace metadata, and tool results.
- Conversation history: earlier messages and generated answers included to preserve context.
- Interaction and telemetry data: accepted-suggestion counts, request and response events, feedback, editor actions, latency, and diagnostic information.
Context selection is usually controlled by the extension or agent, not by the words in your prompt alone. An instruction such as “fix this authentication bug” can therefore send the authentication file, imported types, test failures, and nearby configuration. Review the extension’s context indicators, ignore-file rules, and agent permissions before assuming that only highlighted text leaves the machine.
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Does sending a prompt mean the vendor stores my code?
No single retention rule applies to every assistant. A vendor can process data transiently, retain prompts or outputs for a defined period, cache files for a request, collect telemetry without storing the code itself, or use interaction data for model training. These are separate decisions.
Training and model improvement
“Used for training” concerns whether your inputs, outputs, or related data become examples for improving a model. An opt-out may stop that use while still allowing operational logging, abuse detection, billing records, or short-lived caching. Conversely, a service can advertise no training while retaining requests for security monitoring or troubleshooting.
Operational processing and retention
Operational processing is what the service must do to answer a request: routing it to a model, generating a completion, filtering abuse, or measuring reliability. Retention is how long copies remain in logs, caches, backups, or provider systems. Ask for a duration and a deletion mechanism rather than accepting the word “private” as a technical specification.
Telemetry and abuse monitoring
Telemetry may include request events, response events, reactions, accepted-suggestion character counts, latency, and account information. Abuse systems can inspect content or preserve a sample when an investigation is triggered. These controls can exist even when ordinary prompts are not retained or used for training.
How private is GitHub Copilot?
GitHub Copilot privacy depends heavily on whether you use an individual plan or a Business or Enterprise account, and on the access path. GitHub’s March 25, 2026 policy update says individual Copilot Free, Pro, and Pro+ interaction data will be used to train and improve models unless the user opts out, beginning April 24, 2026.
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GitHub defines that interaction data as “inputs, outputs, code snippets, and associated context.” Private repository content stored at rest is not used for training, but interaction data generated while you actively use a private repository can fall within the individual-plan policy unless you opt out. The distinction is between files sitting in a repository and material sent during an interaction.
What Business and Enterprise users should know
The March 2026 individual-plan change does not affect Copilot Business or Enterprise. GitHub’s product FAQ says 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 categories and surfaces described by GitHub; they are not a universal Copilot rule.
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How model hosting changes the data path
Copilot can route requests to different model hosts. GitHub’s model-hosting documentation says Amazon Bedrock does not store or log prompts and completions or use them to train AWS models, and Google Cloud says it does not train on GitHub data. Other providers have their own zero-retention or caching commitments, so the selected model and route matter as much as the Copilot plan.
How to reduce exposure in Copilot
- Identify whether the account is Free, Pro, Pro+, Business, or Enterprise and whether you are using the IDE, chat, command-line, or another surface.
- Open GitHub’s current Copilot privacy or data-use settings and enable the individual-plan training opt-out where it is available.
- Ask an administrator to confirm organization policies for Business or Enterprise, including model selection, audit access, and repository exclusions.
- Check the selected model host’s retention and geographic-processing terms.
- Use repository and editor exclusion settings to prevent secrets, generated files, and sensitive directories from becoming context.
Does Cursor train on my code?
Cursor sends prompts and code context to model providers such as OpenAI, Anthropic, and Google. Its Privacy Mode is the setting that changes the treatment of that data: Cursor says the mode is intended to prevent Cursor and its providers from using code for training, and team or enterprise administrators can enforce it.
What Privacy Mode does
Cursor’s September 3, 2026 data-use overview says Privacy Mode uses zero-data-retention agreements with providers. Cursor also temporarily caches file contents needed for a request; when Privacy Mode is enabled, that cache is described as encrypted and temporary. “Zero retention” therefore describes the provider agreement and ordinary handling, not a promise that no system can ever hold content under any circumstance.
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The abuse-investigation exception
The same overview says abuse classifiers can create an exception when content triggers an investigation. In that situation, material may be preserved or reviewed for abuse handling even though normal provider processing is covered by zero-data-retention terms. Treat this as a security exception that should be included in your threat model.
What happens when Privacy Mode is off
With Privacy Mode disabled, Cursor may use and store codebase data, prompts, editor actions, snippets, and other code data to improve features and train models. Teams should enforce the setting centrally rather than relying on every developer to remember it, and should re-check it after changing accounts, models, or agent features.
Is Gemini Code Assist stateless?
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, and Google separately collects service telemetry such as request events, response events, reactions, and accepted-suggestion character counts. Google says customer data is not used to train models without permission.
Why the edition and access route matter
Google’s terms index says the applicable notice depends on whether you use Standard, Enterprise, GitHub access, or Cloud Code access. Do not transfer a privacy conclusion from a Cloud-console deployment to a GitHub or IDE integration without checking that route’s terms.
Grounding and abuse-monitoring limits
Google’s Gemini API zero-data-retention guidance illustrates an important qualification: “not used for training” does not always mean “never retained.” Abuse monitoring can require limited storage, and grounding features can create their own windows; Google documents up to 30 days for Search or Maps grounding. If you enable grounding, logging, or other add-ons, evaluate those features separately from the base Code Assist service.
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How do the major privacy controls compare?
The table summarizes the documented differences. “Not stated” means the cited vendor material does not establish a value for that cell; it is not evidence that no processing occurs.
| Service or mode | Training treatment | Prompt/output retention | Other processing and controls |
|---|---|---|---|
| GitHub Copilot Free, Pro, Pro+ (individual) | Interaction data used to train and improve models from April 24, 2026 unless the user opts out. | Exact universal period not stated on the cited policy page. | Inputs, outputs, code snippets, and associated context are covered; model-host terms still matter. |
| GitHub Copilot Business and Enterprise | Not affected by the March 2026 individual-plan update; training treatment follows organization and current terms. | IDE chat and code-completion prompts and suggestions are not retained by default; other access paths retain them for 28 days. Engagement data: two years. | Administrators can control organization use; private-repository content at rest is not used for training. |
| Cursor with Privacy Mode on | Cursor says Cursor and providers are prevented from using code for training. | Provider zero-data-retention agreements; encrypted temporary file cache. Abuse-investigation exceptions can apply. | Teams and enterprises can enforce the mode; providers can include OpenAI, Anthropic, and Google. |
| Cursor with Privacy Mode off | Cursor may use and store codebase data and interactions to improve features and train models. | Exact period not stated. | Data may include prompts, editor actions, snippets, and other code data. |
| Gemini Code Assist Standard and Enterprise | Google says customer data is not used to train models without permission. | Prompts and responses are not stored in Google Cloud by default; Cloud Logging is configurable. | Service telemetry is collected; edition, access route, grounding, and abuse monitoring can change handling. |
What should an organization check before approving an assistant?
Use a written data-flow review for the exact vendor, plan, editor or web surface, model, and enabled features. The following sequence catches the most common gaps.
- Map the request: list which files, repository metadata, history, tool output, and conversation messages can be attached automatically. Include agent actions such as reading files, running tests, or opening tickets.
- Separate purposes: document training, operational processing, telemetry, abuse monitoring, caching, backups, and human review as different purposes with different retention rules.
- Identify every processor: record the assistant vendor, model host, cloud platform, logging service, and subprocessors. Confirm whether requests cross regions and which entity signs the data-processing agreement.
- Verify controls in the actual account: capture screenshots or policy exports for training opt-outs, Privacy Mode, Cloud Logging, repository exclusions, model selection, and administrator enforcement.
- Set data boundaries: block credentials, private keys, production secrets, regulated personal data, customer records, and unredacted incident dumps unless the approved architecture explicitly covers them.
- Test deletion and access: ask how a prompt, output, cache entry, log record, or backup is deleted, who can retrieve it, and whether legal holds or abuse investigations suspend deletion.
- Reassess changes: repeat the review after switching models, enabling an agent, adding search or maps grounding, changing from an enterprise account to an individual account, or moving from an IDE extension to a web interface.
What are the most dangerous mistakes?
- Assuming private repository means private interaction: a file can be private at rest while the completion request is governed by a different interaction policy.
- Equating no training with no retention: logs, abuse review, telemetry, and grounding can still create copies.
- Ignoring automatic context: a prompt can expose neighboring code, configuration, and repository metadata that you never pasted deliberately.
- Forgetting model routing: a change in the selected model can change the processor, country, retention promise, and contract.
- Relying on personal settings in a team: one developer can disable a privacy control or use an unapproved account unless policy is enforced centrally.
- Putting secrets in a debugging prompt: access tokens, private keys, connection strings, and customer data can be copied into logs or conversation history before anyone notices.
What should you do if sensitive code was sent accidentally?
First revoke or rotate any exposed credential; deleting the chat does not invalidate a key that was already transmitted. Preserve the request identifier and account details, notify your security or privacy contact, and ask the vendor which systems received the content and whether deletion, suppression, or an abuse-review hold applies.
Then determine the exact account, plan, model, access route, and settings at the time of transmission. Check repository and editor logs for additional files that may have been included, document the jurisdictions and subprocessors involved, and record the incident under your organization’s breach-assessment procedure if regulated data was present.
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Can a local model eliminate privacy risk?
A model running entirely on equipment you control can reduce external transmission, but it does not automatically solve privacy. The editor may still send telemetry, crash reports, prompts to a remote embedding service, or repository data to a hosted tool used for indexing. Verify the complete path, including update checks, logging, and any optional cloud features, before classifying a setup as local-only.
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How often should privacy settings be reviewed?
Review them at onboarding and whenever the account, plan, model, IDE surface, agent capability, or grounding feature changes. Vendor policies change as well: GitHub’s individual-plan update took effect on April 24, 2026, and Cursor published a revised data-use overview on September 3, 2026. Schedule a periodic review so a previously approved configuration does not silently become a different data flow.
Frequently Asked Questions
Can an AI assistant see files I never opened in the editor?
Yes, if its context builder or agent retrieves repository files, symbols, documentation, or tool output to answer the request. Configure ignore rules and repository exclusions, and inspect the context preview when the product provides one.
Does deleting an AI chat delete provider backups too?
Not necessarily. Deletion may cover the visible conversation while logs, encrypted caches, backups, legal holds, or abuse-investigation records follow separate schedules; ask the vendor to describe each layer.
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They may use less visible context, but the extension can still transmit surrounding code and telemetry. Evaluate completion requests under the same training, retention, routing, and administrator-control questions as chat.
What should a data-processing agreement specify for coding assistants?
It should identify processors and regions, permitted purposes, retention and deletion windows, security measures, subprocessors, incident notice, access controls, and exceptions for abuse monitoring or legal holds. It should also state whether customer prompts, outputs, and code snippets may train models.
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