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The safest AI coding-assistant policy is an enforceable operating model, not a list of vague prohibitions. Assign a small group of accountable administrators, inventory every assistant and client, classify permitted work by risk, verify each provider’s terms for the exact plan, then enforce filesystem, network, credential, model, and approval boundaries on every supported operating system. Keep normal code review, testing, branch protection, and human accountability in charge.
What an AI coding-assistant policy must control
A team policy should answer six operational questions before anyone enables an assistant:
- Who may use it? Define employees, contractors, repositories, organizations, and exceptions. Decide whether personal accounts may access company code.
- What may it do? Separate explanation, test generation, code editing, pull-request review, shell commands, production changes, and access to external services.
- Where may it run? Cover IDE extensions, command-line clients, desktop applications, hosted agents, web interfaces, and repository integrations. The same product can have different controls on each surface.
- What data may it see? Classify source code, secrets, customer information, regulated records, vulnerability reports, credentials, and third-party code.
- What technical boundaries apply? Specify allowed paths, denied paths, network destinations, credentials, model access, connected tools, MCP servers, and approval requirements.
- How is use reviewed? Require testing, security checks, human review, audit logging, exception expiry, and an incident process.
Write each requirement at the level that can enforce it. A repository rule cannot prevent a command-line agent from reading a developer’s home directory; an enterprise setting cannot replace operating-system sandboxing; and a contract statement cannot substitute for checking the client that actually sends prompts.
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Establish governance before enabling features
Assign one policy owner and a small administrator group
Name a policy owner who is responsible for the standard, exceptions, reviews, and communication. Give administrative rights only to the people who need them, normally representatives from engineering, security, privacy, and platform operations. Review that membership on a schedule and after role changes.
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Document where authority sits. In GitHub, enterprise policies can constrain organization-level settings, so an organization administrator cannot simply override an enterprise restriction. Record which settings are enterprise-wide, which are delegated to organizations, and which remain a repository or client responsibility. Ambiguous ownership creates configuration drift: two teams can believe they control the same setting while neither is checking it.
Control administrative change
- Require a ticket or equivalent record for policy changes, including the reason, approver, scope, and effective date.
- Log changes to seats, models, clients, agents, connected tools, retention settings, and sandbox rules.
- Review audit records for unexpected enablement, privilege expansion, and changes made outside the normal process.
- Give every exception an owner, compensating controls, and an expiry date. An exception without an expiry becomes an undocumented permanent policy.
Inventory every assistant, client, and data flow
Start with an inventory rather than a vendor preference. Ask platform and security teams to identify:
- Products and exact paid plans, including trial or individually purchased accounts.
- IDE extensions, command-line tools, desktop clients, web interfaces, cloud agents, and automated pull-request reviewers.
- Available models, model-routing features, experimental features, and extensions or plugins.
- Repositories and organizations connected to each service.
- MCP servers, local services, issue trackers, deployment systems, package registries, and other connected tools.
- Credentials that an assistant can read, inherit, or request, including cloud tokens, SSH keys, package credentials, and environment variables.
- Personal accounts or unmanaged devices that can reach company repositories.
- Prompts, surrounding code context, command output, telemetry, feedback, logs, and generated artifacts that leave the developer’s machine.
For each entry, record the identity source, client version, operating-system support, default settings, administrator scope, retention behavior, and the person responsible for verification. A feature documented for an IDE may not exist in the CLI, and a control available in one plan may be absent from another.
Use risk tiers to decide what is allowed
Risk tiers turn a general rule such as “use AI responsibly” into decisions developers can follow. The following is a suggested starting point, not a vendor default.
| Tier | Typical work | Minimum controls | Approval |
|---|---|---|---|
| Low | Explain public code, draft documentation, generate isolated tests, or suggest non-sensitive refactoring. | Approved plan and client; no secrets or restricted repositories in context; developer reviews output and runs tests. | No additional approval, provided the repository is permitted. |
| Moderate | Edit application code, modify dependencies, or create pull requests in non-production branches. | Restricted paths and network; dependency and secret scanning; branch protection; review by an accountable developer. | Human review before merge. |
| High | Change authentication, authorization, cryptography, infrastructure, billing, or code handling regulated or customer data. | Approved repository and model; no unrestricted credentials; isolated environment; complete test and security evidence. | Named code owner or security reviewer before merge. |
| Critical | Production deployment, privileged shell commands, secrets access, destructive data operations, or external network actions with material impact. | Read-only or tightly scoped access by default; explicit command and tool allowlists; recorded approval; rollback plan. | Explicit human approval at the point of action and normal production change control. |
State which tier applies when multiple conditions are present. For example, a documentation task in a repository containing customer data still requires the repository’s higher data classification, even if the requested output is harmless.
Define data rules and verify provider terms
Specify what may enter prompts and context
Create an allow, prohibit, and approval-required list for repositories and data classes. Prohibit secrets, private keys, access tokens, unredacted customer records, regulated data, and confidential vulnerability details unless a separately approved workflow provides equivalent safeguards. Decide whether third-party code, incident records, and internal architecture documents may be used as context.
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Also specify what happens to surrounding code, terminal output, feedback, telemetry, conversation history, and generated patches. A prompt can expose more than the text a developer intentionally selected if the client automatically includes open files, repository metadata, or command output.
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Do not copy a privacy statement from one product or plan into another. Verify training use, retention, deletion, processing location, subprocessors, audit access, and contractual commitments for the actual SKU, client, and configuration.
- GitHub Copilot: GitHub states that Copilot Business and Enterprise customer data is not used to train models. Its product description also explains that contextual information is used to generate suggestions. Treat those statements as plan- and feature-specific; do not extend them automatically to individual plans, other clients, or connected services.
- OpenAI business products and API: OpenAI says data from specified business products and its API is not used for training by default. Qualifying organizations can configure retention. Confirm eligibility, the selected retention setting, and which endpoints or tools are covered.
- Anthropic commercial API: A July 1, 2026 privacy article says inputs and outputs are automatically deleted within 30 days, subject to exceptions such as longer-retention services, agreements, usage-policy enforcement, or legal requirements. This does not describe products that save conversations and coding sessions in a product history.
- Anthropic Team and Enterprise: A March 16, 2026 article says Enterprise customers can set custom retention timelines. Verify the configured timeline and whether it applies to every workspace, session type, and administrative export.
Keep the provider’s date and scope beside each control in your policy register. Retention is a product property, not a universal rule for an entire vendor or the AI industry.
Set feature, model, identity, and client defaults
Make enablement explicit
Choose which assistants, models, agents, extensions, and connected services are enabled for each group. Set an explicit default for new features and models rather than assuming they inherit the current posture. GitHub documents policies that can apply across IDE, website, and CLI surfaces, while the Copilot application and CLI have independent client policies.
GitHub’s current defaults documentation says its new-feature default policy is scheduled to begin applying on October 22, 2026. Because that date is after the October 13, 2026 information date for this guide, administrators should check the setting and effective behavior before rollout rather than waiting for an automatic change.
Use identity and seat controls
- Require company-managed identities and single sign-on where the product supports it.
- Assign seats through groups tied to employment or project status, not informal requests.
- Remove access promptly when a person leaves a team or company.
- Separate experimentation from production repositories with different groups, models, and connected tools.
- Block unmanaged personal accounts from company repositories where the platform permits that control; otherwise use repository protections and network controls to reduce exposure.
Document the effective client matrix
Create a matrix showing each approved client, operating system, supported controls, and owner. Mark controls that are unavailable or only previewed. A policy that depends on an IDE setting is incomplete if developers can perform the same action through a CLI or hosted agent with different defaults.
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Constrain agent actions with technical boundaries
Filesystem and repository paths
Use read-only paths for source that an agent may inspect but must not modify. Define write paths for the working tree or temporary build directory, and deny paths containing secrets, credentials, production configuration, or unrelated repositories. GitHub managed settings document read/write and read-only path controls as well as denied paths. Test path matching with nested directories, symlinks, generated files, and case differences on case-insensitive systems.
Network access
Default agents to no network access or an allowlist of required destinations. Decide whether package registries, documentation sites, issue trackers, artifact stores, and deployment endpoints are allowed. Treat an outbound request that uploads source or command output as a data-transfer event, not merely a convenience feature.
Credentials and local services
Do not expose broad developer credentials to an agent. Prefer short-lived, least-privilege tokens scoped to a repository or task. Keep cloud administration, production databases, signing keys, and deployment credentials outside the agent environment unless a separately approved workflow requires them. Specify whether the agent may reach local services such as databases, container engines, or credential helpers.
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Maintain an allowlist of MCP servers and tool methods. Deny unknown servers by default, record the owner and data access for each approved server, and review changes to its schema or permissions. A server that can create tickets is lower risk than one that can deploy code or export customer data; apply different approval thresholds accordingly.
Sandbox and failure behavior
GitHub’s managed-settings documentation covers sandbox requirements, network restrictions, and MCP allow/deny settings. Its local sandbox documentation explains that when the host cannot enforce a requested policy, the shell fails rather than running unsandboxed in the described configuration. Make that failure visible to developers and support it operationally; never instruct users to bypass the sandbox to “make the tool work.” Validate the behavior on every supported operating system because filesystem and process isolation capabilities differ.
Keep human review and normal engineering controls in charge
Generated code is an input to the engineering process, not an approval. Require the developer who owns the change to understand it, explain material generated sections, and run the project’s normal tests. For higher-risk repositories, require static analysis, dependency checks, secret scanning, and security review before merge.
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Use branch protection, required status checks, code owners, and separation of duties exactly as you would for manually written code. Track AI assistance when it helps incident response or satisfies a local contractual or regulatory requirement, but do not claim that a universal disclosure rule exists without checking the applicable jurisdiction and agreement.
AI-generated review comments also need review. GitHub supports enterprise code-review controls and custom instructions, but automatic review can add noise. Configure it to identify issues and apply repository standards; do not let an automated comment replace an accountable maintainer’s acceptance decision.
Implement the policy in a controlled sequence
- Inventory use. Collect the products, plans, accounts, clients, models, agents, MCP servers, plugins, repositories, and data flows identified above. Include personal accounts and exceptions.
- Assign data classifications. Map repositories and information types to allowed, prohibited, or approval-required categories. Write examples developers can recognize.
- Set risk tiers. Define which tasks are low, moderate, high, or critical risk and the review required for each.
- Choose approved configurations. Select identity groups, model access, client versions, retention settings, network destinations, filesystem paths, credentials, and connected tools.
- Configure the highest enforcement layer. Put enterprise restrictions above organization settings where possible, then configure repository and client controls. Avoid relying on a written rule when a technical setting exists.
- Create approval workflows. Define who approves privileged commands, production changes, secrets access, new MCP servers, and exceptions. Require a recorded decision and rollback plan.
- Pilot with representative repositories. Include a low-risk project, a sensitive project, and a project using the CLI or hosted agent. Test normal work and deliberate policy violations.
- Publish developer guidance. Explain approved clients, prohibited data, expected approval prompts, how to report a blocked action, and how to request an exception.
- Review evidence before expansion. Check audit logs, blocked operations, false positives, generated code findings, and support tickets. Adjust the policy without weakening high-risk boundaries.
Test the effective policy, not just the configuration
For each supported client and operating system, use a test repository and verify both allowed and denied behavior:
- Read an approved file, attempt to read a denied path, and attempt to write a read-only path.
- Run a permitted local command, then attempt a privileged or destructive command that should require approval.
- Request access to an unapproved network destination and confirm that the operation is blocked or requires the defined approval.
- Try to invoke an unapproved MCP server and an approved server with a disallowed method.
- Check whether environment variables, shell history, SSH agents, and local credential helpers are visible.
- Confirm that a sandbox failure stops execution rather than silently falling back to an unsandboxed shell.
- Verify that model and feature restrictions hold in the IDE, website, CLI, desktop client, and hosted agent separately.
- Inspect logs to confirm that administrator changes, approvals, denials, and connected-tool calls are attributable to an identity.
Record the client version, operating system, policy version, expected result, observed result, and remediation owner. Re-run this suite after client upgrades, operating-system changes, new model availability, or vendor policy changes.
Monitor drift, incidents, and vendor changes
Run recurring reviews
At least quarterly, review administrator membership, seat assignments, enabled models, feature defaults, connected tools, retention settings, exceptions, and audit exports. Review sooner after a security incident, a major client release, or a change in contract terms.
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- Unexpected enablement of a feature or model.
- Repeated attempts to access denied paths, networks, credentials, or tools.
- Policy bypasses through personal accounts or alternate clients.
- Generated-code vulnerabilities, leaked secrets, dependency regressions, or unexplained production changes.
- Retention or deletion behavior that differs from the documented configuration.
Define an incident path that can disable seats, revoke tokens, quarantine repositories, preserve relevant logs, and notify affected owners. Afterward, update both the written rule and the technical test that failed to catch the problem.
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Track changes that can alter the posture
Recheck policy behavior when a provider changes feature defaults, model availability, retention language, client architecture, or operating-system support. Preview and scheduled features deserve an explicit status in your register so that a future activation cannot silently widen access.
Compare assistants and plans using the same control questions
When selecting a product or plan, compare the controls that affect your risk rather than relying on a feature count.
| Decision area | Questions to answer |
|---|---|
| Administration and identity | Can enterprise restrictions constrain organizations? Is single sign-on available? Can seats, groups, and administrator changes be audited? |
| Data use and retention | Is training use excluded for this exact SKU? What inputs, context, logs, feedback, and histories are retained, where, for how long, and under what deletion exceptions? |
| Feature and model control | Can administrators disable models, new features, agents, extensions, and clients independently? Are defaults explicit or inherited? |
| Execution boundaries | Can you enforce read-only and denied paths, network allowlists, credential restrictions, sandboxing, and approval prompts on every operating system you support? |
| Connected tools | Can MCP servers and other integrations be allowlisted by organization, repository, method, or user? Are calls logged? |
| Review workflow | Does the product fit existing branch protection, code owners, required checks, and pull-request review? Can automatic review be limited to useful scopes? |
| Support and terms | Are contractual commitments, incident support, audit exports, regional requirements, and pricing suitable for the exact deployment? |
The available evidence establishes concrete controls and plan differences, but it does not provide a complete like-for-like comparison or current pricing. Obtain current terms for the deployment you intend to operate.
A concise policy standard developers can follow
Your published standard should contain these statements in language that matches your tools:
- Only company-managed identities, approved plans, clients, models, and repositories may be used for company code.
- Secrets, credentials, regulated data, unredacted customer information, and restricted vulnerability details may not be placed in prompts or assistant context unless an approved workflow explicitly permits them.
- Agents start with least privilege: read-only access where possible, limited write paths, no unapproved network, no broad credentials, and no unknown connected tools.
- Privileged commands, production changes, destructive operations, secrets access, and external data transfers require explicit human approval.
- Developers must understand and review generated changes, run tests and security checks, and follow branch and code-owner protections.
- Exceptions require a named owner, documented compensating controls, and an expiry date.
- Administrators test effective behavior by client and operating system and review the policy after vendor or platform changes.
OpenAI describes the operational goal well: “keep the agent inside clear technical boundaries, let developers move quickly on low-risk actions, and make higher-risk actions explicit.” Apply that principle consistently across every assistant surface instead of treating an IDE extension as the whole system.
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