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Free AI coding agents can be safe enough for some private projects, but “free” does not tell you how the tool handles your code. Safety depends on the exact product, plan, feature, data-use and retention settings, and the agent’s access to files and tools. Before you use one, confirm what information leaves your environment, whether it may be used to improve models, what the service retains, and what the agent can do. A setting that prevents training use is not proof that code is never transmitted or retained. For confidential work, follow your employer’s or client’s rules first; if they do not approve the tool, do not send the repository to it.
What does “safe” mean for a free AI coding agent?
“Safe” is not one setting or a universal property of an agent. For a private project, it means understanding and accepting both the data handling and the actions the agent can take. A service might transmit code context to a model provider while preventing training use; separately, an agent might be able to edit files or execute commands. Those are different risks and need separate checks.
It helps to divide the question into two parts:
- Data exposure: What prompts, code snippets, files, conversations, credentials, or repository copies are sent to a service or provider? Are any retained, and can they be used to improve models?
- Execution risk: What can the agent read, change, run, or access through tools, the terminal, or a network connection? What requires your approval, and can you review changes before they take effect?
A product’s policy may differ by plan and feature. A statement about an organization account, a particular model, or a cloud agent does not automatically describe the free individual plan or a local editor feature. Check the configuration you will actually use rather than relying on a brand-wide summary.
What should I check before sharing private code?
Compare the exact product, plan, model, and feature on five points: training use, data flow, retention, execution boundaries, and review controls. These checks help distinguish a setting that limits one kind of use from a broader claim that the project stays private.
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| Check | Question to answer | Why it matters |
|---|---|---|
| Training use | Can prompts, code context, suggestions, or conversations be used to improve models? Is there an opt-out, and does it cover this free plan and feature? | Opting out may limit training use, but does not by itself establish that information is not transmitted or retained. |
| Data flow | Which service or model provider receives the code context? Does routing depend on the model or feature? | A coding feature may send relevant prompts and code to a provider even when you work in a local editor. |
| Retention and deletion | What is stored, where, for how long, and what can you delete? | Requests, cloud repository copies, snapshots, conversations, and configured secrets may have distinct rules. |
| Execution boundary | Can the agent read outside the project, run terminal commands, access the network, or use external tools? | Data exposure is not the only risk: an agent’s permissions affect what it can change or trigger. |
| Review and permissions | Which actions need approval? Can changes run before review? Can you inspect a diff and revert it? | Approvals and review reduce the chance that an unexpected change is accepted or executed unnoticed. |
If a policy or setting does not answer a question, treat that behavior as unestablished rather than assuming the most protective interpretation. Look for terms for the exact plan and feature, then inspect the actual account settings before using a private repository.
Do free plans use private code to train AI models?
Some may, and the answer can vary by provider and account setting. GitHub’s model-hosting documentation says individual subscribers, including Copilot Free, may have interaction data used to train and improve models. It identifies prompts, suggestions, and code snippets generated during sessions as data that may be included, and says individual subscribers can opt out in settings. That is a provider policy statement about individual plans, not a guarantee about every coding agent or a substitute for checking the current setting.
Anthropic’s consumer policy describes a different set of conditions for consumer products and accounts using Claude Code: chats and coding sessions may be used to improve models if the user opts in, if a conversation is flagged for safety review, or if the user otherwise explicitly opts in. The policy statement is for consumer products; commercial products have separate terms.
These examples show why “free versus paid” is not enough to decide. Check whether the policy applies to the plan you have, whether the opt-out is available to you, and whether it covers the particular coding feature and model. Do not infer that turning off training means the provider receives no code, keeps no service records, or has no separate retention rules.
Does Privacy Mode mean my code stays on my computer?
No. Cursor says its AI features send prompts and code context to model providers such as OpenAI, Anthropic, and Google. It says Privacy Mode prevents code from being used for training. That addresses training use; it does not mean that the feature has no data flow.
The distinction matters even when you are using an editor on your own computer. A local interface can still send context to a remote service to provide an AI feature. Before using private code, identify the feature and model you selected, read the provider’s current description of its data flow, and confirm the relevant setting in your account. Do not treat a privacy label as a complete description of transmission, storage, or deletion.
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What extra risks come with cloud coding agents?
Cloud agents can involve more than a prompt-and-response exchange. Cursor’s cloud-agent documentation says agents need repository access over time and store encrypted repository copies temporarily while working. It describes separate rules for runtime workspaces, VM snapshots, conversation state, and secrets. Those categories should not be collapsed into one general “retention” setting.
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For a cloud feature, find out what happens to each relevant item: the repository copy used during work, any snapshot, the conversation or transcript, and secrets configured for the agent. Check how long each is retained and whether deletion is available. The available policy description does not make these behaviors interchangeable, so an answer about one category does not establish the answer for another.
Also consider the agent’s access while it is working. A cloud environment’s ability to read a repository or use configured credentials is a separate question from whether the service uses code for model training. Limit access to what the task needs, and do not configure a secret merely because the agent requests it.
How do I reduce risk when using an agent on a private repository?
Use a staged workflow: establish the rules first, reduce the data and permissions available, then review anything the agent proposes to change. These steps reduce exposure and execution risk; they cannot make every service appropriate for every confidential project.
- Identify the exact setup. Record the product, plan, model, and feature you intend to use. Do not transfer a promise or setting from an enterprise account to an individual free account.
- Check current data-use and retention controls. Read the applicable terms and settings. Disable training use where an opt-out is offered, and verify that it applies to the feature you plan to use.
- Remove unnecessary sensitive material. Do not put credentials, private keys, production data, or unrelated confidential files in prompts or in an accessible workspace. Give the agent only the context needed for the task.
- Start with narrow permissions. Limit the workspace and tool access. Use restricted or sandboxed modes where available; keep terminal, network, and external-tool access limited to what the work requires.
- Keep changes reviewable. Require approval where available, inspect the diff, and understand generated commands before running them. Keep version control available so unwanted edits can be reverted.
- Check cloud retention separately. If the feature runs in the cloud, look up the rules for repository copies, snapshots, conversation state, and secrets, including retention periods and deletion options.
For an unfamiliar project, begin in the most restricted mode available rather than granting broad access and trying to remove it later. In VS Code, the security guidance recommends opening untrusted projects in restricted mode. Its documentation describes workspace-limited access, configurable tools, approvals, review of changes, and OS-level sandboxing for supported configurations. It also cautions that sandboxing is an added layer, not a virtual-machine or user-account boundary and not a replacement for endpoint security.
After an agent proposes a change, examine what files changed and why before accepting or running anything. A diff makes edits visible, but it does not prove that a command is harmless or that the code is correct. Review commands and changes in the context of your project, and run only what you understand.
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When should I avoid a free agent for a private project?
Avoid sending the repository when the applicable policy or configuration is unclear and the project’s confidentiality requirements do not allow that uncertainty. In particular, do not use an unapproved individual account for work governed by an employer, client contract, or regulatory requirement.
Ask the organization for its approved-tool guidance before using an agent if the project contains restricted source code, production data, or credentials. A personal preference setting cannot override contractual or organizational rules. If the organization has not approved the tool, keep the code out of its prompts, accessible workspace, and connected cloud environment.
Even where use is allowed, a task may not justify granting the agent broad repository, terminal, network, or credential access. Narrow the task, remove unnecessary files, and limit permissions; if the required access still exceeds what you are comfortable exposing, use a workflow that does not send the private material to that agent.
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Provider documentation is useful for understanding a provider’s stated behavior, but the examples below do not establish one rule for every free coding agent. Policies, defaults, model routing, plan names, and retention windows can change. Recheck the live documentation and the settings on the account you will use.
| Example | What the provider documentation says | What not to assume |
|---|---|---|
| GitHub Copilot individual plans | GitHub says individual subscribers, including Copilot Free, may have interaction data used to train and improve models; it identifies prompts, suggestions, and code snippets, and says individual subscribers can opt out in settings. | Do not assume the opt-out is already enabled, or that a setting described for one account or feature covers another. |
| Cursor AI features | Cursor says prompts and code context are sent to model providers, and that Privacy Mode prevents training use. | Do not equate no training use with no transmission or retention. |
| Cursor cloud agents | Cursor describes encrypted repository copies during work and separate rules for runtime workspaces, VM snapshots, conversation state, and secrets. | Do not infer that one retention statement answers what happens to all of these data types. |
| VS Code agent safeguards | VS Code recommends restricted mode for untrusted projects and documents workspace limits, configurable tools, approvals, review, and OS-level sandboxing for supported configurations. | Do not treat sandboxing as a VM, user-account boundary, or substitute for endpoint security. |
| Anthropic consumer accounts | Anthropic says consumer chats and coding sessions, including Claude Code, may be used to improve models under specified opt-in or safety-review conditions. | Do not apply consumer terms to commercial products, which have separate terms. |
Frequently Asked Questions
If I turn off training, can I delete code the agent already received?
A training opt-out and a deletion right are different controls. The cited policy examples do not establish a universal deletion process for previously transmitted prompts or code; check the exact service’s retention and deletion terms for your account.
Can I use an AI coding agent without an internet connection?
The product examples here describe features that send prompts or code context to providers, but they do not establish whether a particular product offers an offline mode. Verify the behavior of the specific feature rather than assuming that installing an editor locally makes its AI functions offline.
Are enterprise privacy promises valid for my personal free account?
Not automatically. Product terms and controls can differ between individual and commercial offerings, so rely on the terms and settings tied to the account you will actually use.
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