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Capabilities differ by product plan, client, operating system, and release channel. Validate the effective configuration on each supported combination rather than assuming a setting in one IDE or cloud agent applies everywhere.
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1. Establish ownership and policy authority
Give one person or function responsibility for the policy. This may be an engineering-platform leader, security architect, or risk owner, but the role must have authority to change settings and stop an unsafe deployment. Keep the administrator group small and use named accounts with strong authentication.
Define the hierarchy
Document which level controls each decision. In GitHub Copilot, enterprise policies can constrain organization settings, while organization administrators control options that the enterprise has left available. A team can otherwise believe it has disabled a feature while an inherited setting still enables it, or two administrators can make conflicting changes.
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- Record the enterprise owner, organization owners, security reviewers, and operational administrators.
- State which settings are mandatory at enterprise level and which organizations may choose locally.
- Require a ticket or change record for policy changes, with an approver and effective date.
- Review administrator membership at a defined interval and immediately after role changes.
- Export or retain audit events for setting changes, seat assignments, exceptions, and policy overrides.
GitHub’s organization and enterprise policy guidance warns that unclear access and governance can produce configuration drift. Treat the policy repository and the administrative console as controlled systems, not informal documentation.
2. Inventory every assistant and data path
Before writing allow or deny rules, list what is actually in use. Ask developers, procurement, identity, and security teams for evidence from device-management records, source-control integrations, and expense reports. Include sanctioned and unsanctioned use.
Inventory checklist
- Product name, edition or SKU, contract owner, and renewal date.
- IDE extensions, desktop applications, web interfaces, command-line clients, and cloud-hosted agents.
- Models exposed by each client, including models enabled by default and preview models.
- Repository integrations, pull-request review features, issue tools, ticket systems, and deployment systems.
- Plugins, extensions, MCP servers, local services, and scripts that an agent can invoke.
- Identity method, group or seat assignment, personal-account access to company repositories, and service accounts.
- Data sent in prompts, retrieved as surrounding code, uploaded as files, returned in logs, or retained in conversation history.
- Supported operating systems and the versions of the IDE, CLI, agent runtime, and endpoint-management software.
- Exceptions, pilot users, contractors, and repositories that handle regulated, customer, security, or export-controlled information.
Make the inventory a living register. A new model or client can create a new data flow even when the product name has not changed.
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3. Turn risk into permission tiers
Use risk tiers to connect a developer’s task to a technical permission and a human decision. The following is a policy design example, not a vendor default.
| Tier | Typical work | Suggested assistant permissions | Required human control |
|---|---|---|---|
| Low | Explain public or low-sensitivity code, draft tests, produce documentation, or suggest refactoring without writing files. | Approved model and client; read-only repository context; no secrets; restricted network. | Developer checks the output and runs normal tests before use. |
| Moderate | Generate or edit code in a development branch, update tests, or prepare a pull request. | Write access only to declared workspace paths; approved tools; no production credentials; network limited to an allowlist. | Human review, automated tests, static analysis, dependency scanning, and branch protection. |
| High | Modify authentication, authorization, cryptography, infrastructure, billing, personally identifiable information handling, or security controls. | Separate approval, restricted model and agent set, explicit file and network boundaries, and session logging. | Named subject-matter reviewer and security or platform approval before merge. |
| Critical | Production changes, privileged operations, secrets access, external side effects, or actions that can delete or expose data. | Deny by default. Permit only through a controlled workflow with just-in-time credentials and explicit confirmation. | Two-person or equivalent approval, change record, rollback plan, and post-change verification. |
Write the tier decision in terms a developer can apply: what data is involved, what the assistant may read or change, and which action always requires a person to confirm. Do not rely on labels such as “safe mode” without defining their effect.
4. Define data-use rules for each plan
Separate the question “Can the assistant see this?” from “What does the provider retain or use it for?” Your policy should classify repositories, prompts, surrounding code, tool output, feedback, telemetry, and conversation history.
Create a data matrix
| Data class | Default treatment | Exception path |
|---|---|---|
| Public code and documentation | Allowed with an approved client and model. | None unless license or contract restrictions apply. |
| Internal source code and tests | Allowed only on a business plan whose contractual data terms have been reviewed. | Security or privacy approval for new providers or models. |
| Secrets, private keys, tokens, and credentials | Never place in prompts or agent-readable paths; block through secret scanning and endpoint controls. | Only a dedicated, audited workflow with short-lived credentials. |
| Customer, regulated, or production data | Prohibited unless the exact plan, location, retention, deletion, and contractual terms are approved. | Documented privacy and legal approval with minimization and masking. |
| Vulnerability details and incident material | Restricted to approved environments and personnel. | Incident commander or security owner authorizes the specific session. |
| Third-party code | Use only when licensing, confidentiality, and contract obligations permit the transfer and output use. | Legal or open-source review for unusual licenses or confidential code. |
Review the actual terms for the exact plan and surface. GitHub states that Copilot Business and Enterprise customer data is not used to train models, while its product information also describes contextual information used to generate suggestions. Those statements do not automatically cover an individual plan, a different client, or a connected service.
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OpenAI says data from specified business products and its API is not used for training by default and that qualifying organizations can configure retention. Confirm that your purchased product and configuration qualify; do not extend the statement to consumer accounts or unrelated integrations.
Retention is product-specific. Anthropic’s commercial API documentation dated July 1, 2026 says inputs and outputs are automatically deleted within 30 days, subject to exceptions such as longer-retention services, agreements, usage-policy enforcement, or law. Its products that save conversations and coding sessions retain those sessions in product history. Anthropic’s March 16, 2026 Team and Enterprise guidance says Enterprise customers can set custom retention timelines. These are different services and settings, not a universal Anthropic rule.
Ask the provider these questions
- Is prompt, code-context, tool-output, feedback, or telemetry data used to train or improve a model?
- What is the retention period for requests, responses, logs, conversation history, and backups?
- Can an administrator configure retention or request deletion, and what exceptions apply?
- Where is data processed and stored, and which subprocessors receive it?
- What contract, data-processing addendum, audit report, or regional option governs the exact SKU?
- Do web, IDE, CLI, desktop, cloud-agent, and code-review surfaces follow the same terms?
5. Decide what is enabled, for whom, and where
Write an explicit feature and model baseline. Do not assume that a future feature inherits today’s restrictions. GitHub documentation describes policies that can apply to IDE, website, and CLI surfaces, while the Copilot app and CLI have independent client policies. Test each surface separately.
Set defaults deliberately
- Choose the approved models and define whether users may switch models.
- Disable preview features and autonomous agents until they pass a review.
- Specify which organizations, repositories, groups, and contractors receive seats.
- Block personal accounts from accessing company repositories where your identity controls support it.
- Set a process for requesting an additional model, extension, MCP server, or client.
- Record the default for new features and revisit it when the vendor changes its policy behavior.
GitHub’s current defaults documentation states that its new-feature default policy is scheduled to begin applying on October 22, 2026. Because that date is after the documented information used here, verify the live behavior and set explicit values rather than relying on the scheduled default.
6. Constrain agents with technical boundaries
An agent that can edit files, run commands, call networks, or use credentials needs controls stronger than a written prohibition. Configure the narrowest workspace and tool permissions that still support the task.
Filesystem controls
- Declare read-only paths for source that the agent may inspect but never modify.
- Declare writable paths for a temporary workspace or feature branch.
- Deny paths containing secrets, production configuration, signing material, identity stores, and unrelated repositories.
- Prevent path traversal and symlink escapes where the client or sandbox supports those controls.
- Test both allowed and denied paths with the exact client and operating-system account used by developers.
Network and service controls
- Deny outbound network access by default for local coding tasks.
- Allow only documented package registries, source-control endpoints, or internal services required for the task.
- Block access to cloud metadata services, internal administrative panels, databases, and production APIs.
- Maintain an allowlist for MCP servers and connected tools; deny unknown servers and extensions.
- Log tool calls and network attempts where the platform provides that telemetry.
Credentials and approvals
- Do not expose long-lived tokens to an agent. Use short-lived, scoped credentials when a task genuinely needs authentication.
- Require confirmation before writes outside the workspace, destructive commands, package publication, deployment, or external messages.
- Separate read, write, and administrative identities.
- Use repository branch protection and deployment approvals even when an agent reports success.
GitHub’s managed-settings documentation describes read/write and read-only path controls, denied paths, network restrictions, sandbox requirements, and MCP allow or deny settings. Its local-sandbox guidance also explains that platform limitations matter: when the host cannot enforce a requested policy in the described configuration, the shell fails rather than running unsandboxed. Treat a failed command as a control signal, not a reason to weaken the policy without review.
7. Validate enforcement on every supported client and OS
A policy is effective only if the runtime enforces it. Build a small conformance test for every combination you support, such as a particular IDE extension on macOS, a CLI on Linux, and a cloud agent.
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- Create a disposable repository containing harmless test files, a fake secret pattern, and a command that would make a visible marker.
- Verify that the assistant can read an explicitly allowed file and cannot read a denied file.
- Attempt a write outside the permitted workspace and confirm that it is blocked or requires approval.
- Attempt a denied network destination and an allowed destination; record the observed result.
- Try to invoke an unapproved MCP server or local service and verify the deny behavior.
- Check that credentials are absent from the process environment and filesystem unless the workflow explicitly grants a scoped token.
- Confirm that approval prompts identify the command, path, network destination, and account involved.
- Capture the client version, operating system, policy version, and test result in the control record.
Repeat the test after client upgrades, policy changes, operating-system changes, and vendor announcements. A setting that exists in an administrator console may not be implemented identically in every client.
8. Keep human review and repository controls in charge
Require developers to understand and review generated code. The author remains accountable for correctness, security, licensing, performance, and maintainability.
Minimum pull-request requirements
- Run the project’s unit, integration, and end-to-end tests appropriate to the change.
- Run static analysis, dependency checks, secret scanning, and security tests.
- Require review from code owners for protected directories and high-risk components.
- Use signed commits or equivalent provenance controls where your organization requires them.
- Keep branch protection, required status checks, and deployment approvals independent of the assistant.
- Review assistant-generated code-review comments before treating them as findings or approvals.
GitHub supports enterprise code-review controls and custom instructions, but automatic review can add noise. Set expectations for triage, false positives, and escalation; an automated comment is not a security sign-off.
Consider recording when AI assistance materially contributed to a change if that helps incident response, customer disclosure, or a local regulatory obligation. There is no universal disclosure rule: determine the requirement for your jurisdiction, contract, and industry.
9. Use instructions without treating them as controls
Repository instruction files can tell an assistant about coding conventions, test commands, architecture, and prohibited edits. They improve consistency but are not a security boundary. A malicious prompt, compromised dependency, or client bug can bypass text instructions.
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10. Handle exceptions and personal accounts
Exceptions should be narrow, time-limited, and reversible. Require the requester to state the repository, data class, client, model, task, business reason, compensating controls, approver, start date, and expiry date.
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Personal accounts accessing company repositories are a common blind spot. Decide whether they are prohibited, technically blocked, or permitted only under a written agreement and approved client configuration. If the organization cannot verify retention, training use, identity, and deletion terms for the account, classify the access as unapproved.
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Assign an operating rhythm rather than waiting for an incident.
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|---|---|
| Each change | Record who changed the policy, what changed, why, and which clients or organizations are affected. |
| Monthly | Review new seats, dormant accounts, exceptions nearing expiry, denied-action events, and unusual tool or network calls. |
| Quarterly | Reconcile the assistant inventory, test sandbox enforcement, review provider terms, and sample pull requests for required checks. |
| After an incident or major release | Preserve logs, determine the effective policy at the time, revoke credentials if needed, and update controls and training. |
Revisit the policy whenever a vendor changes feature defaults, retention terms, model availability, client behavior, or operating-system support. The October 22, 2026 GitHub default-policy activation is an example of a date that should trigger a configuration review.
12. Compare products and plans with a control-based scorecard
Do not choose an assistant solely on model quality or a demonstration. Score the exact plan and clients your team will operate.
| Decision area | Evidence to collect |
|---|---|
| Administration and identity | Enterprise and organization scope, group provisioning, single sign-on, seat controls, administrator roles, and audit export. |
| Data protection | Training use, retention, deletion, residency, subprocessors, contractual commitments, and differences among IDE, CLI, web, and agent surfaces. |
| Feature and model control | Allow or deny controls for models, previews, agents, extensions, code review, and new-feature defaults. |
| Runtime enforcement | Filesystem, network, credentials, sandbox, approval, and MCP controls by operating system and client. |
| Investigation | Logs for prompts, tool calls, approvals, policy changes, access, and incidents, with documented retention. |
| Engineering workflow | Repository integration, branch protection compatibility, code-owner review, testing, and provenance support. |
| Terms and operations | Support commitments, incident notification, contract language, upgrade behavior, and total administrative effort. Current pricing is not established by the information available here. |
Mark an item “not stated” when the provider has not documented it for the exact plan. An undocumented control is a risk to resolve, not an assumption to score as compliant.
13. A rollout plan that limits exposure
- Week 1: establish governance. Name the owner, administrators, security reviewer, and escalation path. Freeze untracked exceptions.
- Week 2: inventory and classify. Map clients, models, agents, repositories, connected tools, data classes, and operating systems. Assign risk tiers.
- Week 3: configure a pilot. Apply the narrowest model, seat, repository, filesystem, network, credential, MCP, and approval settings to a small group.
- Week 4: test and observe. Run the conformance tests, review logs, collect failure cases, and verify that normal development remains possible without bypasses.
- Week 5: expand by tier. Add teams only when the pilot passes security and developer-experience checks. Keep high-risk repositories on stricter settings.
- Ongoing: operate the control. Review changes, expire exceptions, test after upgrades, and update the data and provider-term assessment.
14. Common failure modes and fixes
“We published a policy, so the assistant is controlled.”
Written rules do not stop a client from reading a secret or calling a network. Map every rule to an enforced setting and a conformance test.
“The enterprise setting covers every client.”
IDE, website, CLI, desktop, cloud-agent, and review surfaces can have separate policies. Test each one and document gaps.
“Business plan means every data question is answered.”
Plan names do not replace a review of training, retention, deletion, residency, subprocessors, and connected services. Obtain the terms for the exact SKU.
“Read-only mode is enough.”
Read access can still expose credentials, customer data, or proprietary algorithms. Apply repository and path classification before granting context.
“The agent can deploy because tests passed.”
Tests do not authorize a production change. Keep deployment credentials, approvals, branch protection, and rollback procedures outside the assistant’s discretion.
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New-feature defaults can change the effective posture. Set explicit defaults and review scheduled activations, including GitHub’s stated October 22, 2026 change.
15. The policy document: minimum contents
Keep the human-readable policy short enough to use, with links to controlled procedures and technical baselines. It should state:
- Purpose, scope, owner, administrators, and policy hierarchy.
- Approved products, plans, clients, models, repositories, groups, and operating systems.
- Allowed, prohibited, and approval-required data classes and tasks.
- Filesystem, network, credential, MCP, sandbox, and human-approval requirements.
- Testing, code review, branch protection, scanning, provenance, and incident procedures.
- Exception requirements, expiry rules, audit records, and enforcement consequences.
- Review cadence and triggers for vendor, client, model, retention, or operating-system changes.
Pair this document with administrator configuration exports, a data-flow register, a test script, an exception log, and an incident playbook. That package lets a reviewer determine not only what the team intended, but what the assistant could actually do.
Conclusion
The safest useful policy gives developers fast access to low-risk assistance while making higher-risk actions explicit. As OpenAI describes the operating goal for Codex, “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: define authority, classify data and work, enforce narrow technical boundaries, preserve normal engineering review, and continuously verify the result on every client and operating system.
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