The Tool Desk
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What an AI coding assistant may see
A prompt is not always limited to text you type or paste. Depending on the product and configuration, a coding assistant may receive conversation history, snippets from open or nearby files, workspace context, terminal output, or information returned by connected tools. An agent may also read files and execute commands locally.
For example, Google’s Gemini Code Assist Standard and Enterprise documentation says prompts may include conversation history and snippets from open or adjacent files. That describes those editions, not every Gemini-branded product. Check the context and exclusion controls for the exact assistant, editor integration, plan, and feature you use.
Before enabling an assistant for a repository, find out whether it can access:
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- Open files, adjacent files, and indexed workspace content
- Conversation history and terminal output
- Repository issues, pull requests, documentation, or fetched web pages
- Connected tools, extensions, local files, or remote services
Also distinguish access from transmission: a tool may read a local file even when that file is not included in every request. Its documentation and settings should explain what context can be sent and how to exclude sensitive paths.
Training and retention are separate questions
Ask separately whether a provider uses your inputs or outputs for model improvement, what data it retains, for how long, and whether an administrator can configure those practices. A statement about training alone does not answer retention, logging, access, or the context sent to a model. Vendor terms also depend on product, plan, interface, and configuration.
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| Product and scope | Training or model improvement | Retention and context details |
|---|---|---|
| GitHub Copilot, individual subscribers | GitHub says it may use interaction data, including prompts, suggestions, and code snippets, to train and improve models. Individual subscribers can opt out. | Do not apply the Business or Enterprise retention statements to individual plans. |
| GitHub Copilot Business and Enterprise | Check the terms for the applicable plan, model host, feature, and access path rather than assuming all Copilot offerings share one rule. | GitHub says prompts and suggestions from IDE chat and code completions are not retained; other access paths may retain them for 28 days. The statement is access-path-specific. |
| OpenAI ChatGPT Enterprise, Business, Edu, Healthcare, Teachers, and API platform | OpenAI says inputs and outputs from these listed business products are not used for training by default. | OpenAI says business data is encrypted in transit and at rest. Qualifying organizations can configure retention, including zero data retention on the API platform. These statements do not cover all consumer services or third-party integrations. |
| Google Gemini Code Assist Standard and Enterprise | Google says it does not use customer data to train models without permission. | Google describes the service as stateless and says prompts and responses are not stored in Google Cloud by default; optional Cloud Logging can store them. Its documentation also describes prompt context that can include conversation history and open or adjacent file snippets. |
| Anthropic Claude Free, Pro, and Max, including Claude Code on those accounts | Anthropic’s notice dated March 16, 2026 says chats and coding sessions may be used for model improvement if the user opts in, if a conversation is flagged for safety review, or under another explicit opt-in. | Anthropic says feedback may cause the related conversation to be retained for up to five years. The notice concerns consumer plans, not Claude for Work or API terms. |
These are examples of published provider statements, not a ranking or a guarantee that one setup is suitable for every organization. GitHub’s Copilot privacy and responsible-use information, OpenAI’s business data information, Google Cloud’s Gemini Code Assist security and privacy documentation, and Anthropic’s Privacy Center describe their respective scopes. The pages were checked October 4, 2026, except Anthropic’s notice, dated March 16, 2026; recheck the applicable terms when products or settings change.
Set a repository policy before connecting an assistant
- Identify the exact service. Record the product, plan, interface, model provider, and feature, including whether you are using chat, autocomplete, an IDE extension, or an autonomous agent.
- Check the applicable terms and controls. Review training, retention, logging, feedback, subprocessors, context handling, and organization-wide administration for that specific setup.
- Classify the repository. Decide which code and data classes are allowed. Apply organizational rules to regulated, classified, customer, and commercially sensitive material; a provider’s general privacy statement does not establish legal or contractual suitability.
- Inspect context and access. Check what files, history, terminal content, workspace indexes, extensions, and connected tools the assistant can read or send. Test exclusion settings with a non-sensitive file before relying on them.
Keep credentials out of prompts and project context
Do not paste live API keys, access tokens, passwords, private keys, or production credentials into prompts or assistant-visible terminal sessions. Keep secrets out of source files and CI/CD configuration; use an approved secrets manager or protected secret store instead. OWASP’s Secure Coding with AI and CI/CD Security cheat sheets discuss secure handling and detection of exposed credentials.
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Configure the assistant’s own exclusion controls for paths such as .env, private keys, and credential files, and verify how they work in the product you use. .gitignore prevents Git from tracking matching files; it does not prevent a local assistant from reading them.
Run secret scanning on repositories and relevant development workflows. If a credential is exposed, treat it as compromised: revoke or rotate it through the issuer’s process promptly. Removing the text from a prompt or deleting a file does not establish that the credential is unusable.
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Constrain agents that can act
Autocomplete and chat primarily suggest text; an agent may also edit files, run commands, install dependencies, use a network connection, or interact with services. Give it only the access needed for the task.
- Limit its filesystem scope, commands, tools, and credentials. Avoid broad cloud, administrative, SSH, or production access.
- Separate read and write permissions where available. Require approval for sensitive actions.
- Use a sandbox, development container, virtual machine, or ephemeral workspace for command-executing agents. Restrict outbound network access unless the task requires it.
- Treat issue text, pull-request comments, README files, logs, fetched pages, and tool output as untrusted input. They can contain instructions that try to steer an agent.
After an agent processes external content, inspect what it did and the resulting diff. GitHub documents specific branch and human-review controls for its cloud agent; those protections should not be assumed to exist in other products.
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Review generated code before it runs or ships
Keep your normal code review, tests, dependency review, secret scanning, and security scanning in place. GitHub advises applying the same safeguards and diligence to Copilot output as to other third-party code, including reviewing suggestions before execution. OWASP’s Secure Coding with AI guidance likewise emphasizes reviewing agent output.
Give particular scrutiny to changes involving dependencies, build scripts, CI/CD workflows, deployment settings, and credential access. These can affect what runs in your development or production pipeline, even when the visible code change looks small.
Choose a setup by matching controls to risk
Compare the controls that matter for your organization rather than relying on a broad label such as “private.” Ask these questions for each candidate:
- Training: Are prompts and outputs used for model improvement by default, by opt-in, or under another stated condition?
- Retention: What is retained, for how long, on which interface, and can your organization configure it?
- Context: Can the assistant access file snippets, history, terminal content, repository sources, or connected tools?
- Administration: Does the plan provide the identity, access, audit, and organization-wide settings you need?
- Agent authority: Can it run commands, reach the network, use credentials, change files, or push changes? What isolation and approval controls are available?
- Independent checks: Can you preserve human review, testing, secret scanning, and code-security scanning?
No cited provider statement establishes a universally safest assistant or configuration. Choose only after matching product-specific controls to your data classification and organizational requirements.
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