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Use several boundaries, not a single “no training” promise: approve which models and AI features employees can use, keep sensitive files out of an assistant’s reachable context, withhold production credentials from agent runtimes, isolate execution and network access, and require review before consequential changes. Then verify each control against the specific product, client, model, plan, and mode in use. File exclusions and provider privacy terms can help, but neither is a universal guarantee.
Start by deciding what an AI tool may access
“AI coding assistant” can mean IDE autocomplete, chat, an edit or agent mode, a command-line tool, a cloud-hosted coding agent, a web chat, an MCP-connected tool, or an automated workflow. These surfaces may have different file access, permissions, policy coverage, and data handling. A control enabled for one is not necessarily enforced in another.
Before enabling a tool, classify the material it could encounter and decide which route is acceptable for each class:
- Code and data: Identify sensitive repositories, paths, generated build artifacts, issue or pull-request contents, and any other material assistants might ingest.
- Credentials: Separate production secrets, broadly privileged tokens, and other high-impact credentials from short-lived, task-specific credentials.
- Allowed processing: Decide whether each category may go to an external hosted model, only to an internally hosted model, or to no AI tool.
This boundary should cover more than source files: an issue description, log, build artifact, or project instruction can also contain confidential information or secrets.
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Control which models and surfaces are available
Approve the models
Treat model selection as an administrative security control. Set enterprise defaults deliberately, enable only approved models, and check the actual eligibility and settings for the organization’s plan. Model availability and provider terms can differ by model and product surface. GitHub’s documentation describes model- and provider-specific exceptions, so a broad statement such as “the service never retains prompts” is not a safe policy assumption.
Inventory every entry point
List the ways employees and automation can reach an AI model: IDE completion and chat, edit or agent mode, CLI, cloud agents, web chat, MCP tools, and workflow integrations. Check policy coverage for each one. If the organization approves a model for one surface, do not assume that approval or its restrictions automatically carry over to another.
For every approved route, record the provider, model, feature, hosting route, retention period, training use, abuse monitoring, and any eligibility condition for enhanced data controls. Revisit the record when a model, integration, or product setting changes.
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Keep sensitive files out of the assistant’s context
Remove secrets at the source
Do not store credentials in source trees, prompts, project instructions, issue text, or logs. Use a secret-management system and remove exposed values from files and histories where appropriate. File exclusions are not a substitute for keeping secrets out of repositories.
Use exclusions, but verify their scope
GitHub documents content exclusion for specified paid organization plans. For supported suggestions and responses, excluded files are not used to inform the assistant. However, GitHub also documents gaps: exclusions are unsupported in some IDE Edit and Agent modes, indirect semantic information may remain available, and symlinks and remote filesystems have limitations.
Check the current support matrix for the exact client and mode employees use, then test the behavior rather than assuming an exclusion applies everywhere. For code that must not be sent to an external provider, use an architecture that prevents the assistant from reading or transmitting it. A prompt instruction or exclusion rule alone is not a strong enough boundary for highly sensitive material.
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Keep credentials out of agent runtimes
An agent that can use a credential has operational authority, even if the value is stored in a platform’s “secrets” feature. GitHub documents that configured cloud-agent secrets are available as environment variables during setup and task execution. Treat a secret provisioned to that runtime as accessible to the agent and to code or tools it can execute.
- Do not provide production credentials or broad-scope tokens by default.
- If a task truly needs a credential, scope it to that task and repository, grant the narrowest permissions, and prefer short-lived credentials where supported.
- Restrict which repositories can receive the credential, and revoke it when the task is complete.
- Keep sensitive credentials in a downstream workflow job when the agent does not need them. GitHub’s Agentic Workflows guidance describes this pattern: the agent produces a validated output, while a later job handles the secret-bearing operation.
Do not infer that a value is hidden from an agent simply because it came from a secret store. The important question is whether the value is made available inside the agent’s execution environment.
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Restricting context reduces what an assistant can see; restricting tools and permissions reduces what it can do. Start with the least authority needed for the task, and isolate the runtime from developer home directories and production systems.
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- Execution: Use an isolated environment rather than a developer workstation or production host.
- Permissions: Start with read-only access where practical, and grant narrow write permissions only for tasks that require them.
- Tools and network: Make only necessary tools available and restrict outbound connections to required destinations.
- Consequential actions: Put human review or approval before merges, deployments, workflow execution, and other high-impact changes.
- Outputs: Validate generated changes and other agent-produced outputs before a downstream system acts on them.
GitHub’s cloud-agent documentation describes risks including accidental disclosure and malicious input, alongside protections such as security validation, secret scanning, internet restrictions, and review controls. These are mitigations, not proof that an agent cannot leak information or take an unsafe action.
Compare tools using the same security questions
Use a consistent checklist when evaluating products or deployment patterns. The relevant differences are the controls each one actually enforces in the organization’s intended configuration—not a general claim that one kind of tool is secure.
| Control area | What to verify |
|---|---|
| Repository and file boundaries | Can the tool be prevented from reading selected repositories and paths? Do exclusions work for the exact client, mode, symlink, and filesystem arrangement? |
| Surface coverage | Do the policies apply to IDE, CLI, cloud-agent, and automation use, or only to some of them? |
| Credentials | Which credentials can enter the runtime, which repositories can receive them, and how narrow and short-lived can they be? |
| Isolation and egress | Is execution separated from developer and production environments, and can outbound network access be limited? |
| Actions and review | Can access start read-only, and are writes, deployments, or workflow execution gated and validated? |
| Provider data handling | What are the retention, training, logging, abuse-monitoring, and hosting terms for this model and route? |
Check the data terms for each provider and route
Privacy controls are specific to a service, model, and integration. OpenAI’s API documentation distinguishes abuse-monitoring logs from eligible Modified Abuse Monitoring or Zero Data Retention controls. Eligibility matters: do not treat those controls as automatic for every API account or product route.
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Anthropic’s notice for designated covered models states that prompts and outputs are retained for 30 days from June 9, 2026, for the arrangements covered by that notice. That is a scope-bound policy, not a general retention promise for every Anthropic product or integration. Verify the current terms and applicability for the organization’s actual service arrangement before relying on either provider’s controls.
Do not transfer one provider’s data commitments to a coding product that routes requests through a different provider, model, or hosting arrangement. Record the terms for each approved path and recheck them when those components change.
Put the controls into operation
- Classify data and credentials. List sensitive repositories, paths, artifacts, issue content, and credential classes; assign an allowed AI-processing route to each.
- Approve models and surfaces. Set enterprise defaults, restrict access to approved models, and inventory IDE, CLI, cloud-agent, web, MCP, and workflow entry points.
- Enforce the context boundary. Remove secrets from source trees, apply supported exclusions, and test them in every client and agent mode employees use. For critical code, prevent access architecturally.
- Withhold credentials by default. Keep production secrets outside agent runtimes. When access is necessary, use the narrowest task-scoped credential available and limit which repositories receive it.
- Constrain runtime authority. Isolate execution, limit tools and network paths, begin with read-only permissions, and gate consequential actions.
- Document provider terms. Record the model, feature, route, retention, training use, logging, monitoring, and any eligibility conditions for additional controls.
- Monitor and rehearse. Review session logs where available, scan repositories and generated changes for exposed secrets, and test exclusions, permissions, and egress rules on each supported surface.
GitHub documents session logs and secret scanning for its cloud agent, but logging and implementation details vary among tools. Use the monitoring available in the product, and verify that it gives the organization enough visibility for its own requirements.
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