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The procedure below is designed for engineering leaders, platform administrators, security and privacy reviewers, and developers. Vendor terms and controls differ by plan, client, and operating system, so verify every setting against the exact product SKU and deployment you operate.
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What an AI coding-assistant policy must decide
A policy is more than a statement that developers may use an assistant. It is a set of accountable decisions and enforceable defaults.
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- Scope: list approved products, plans, models, IDE extensions, command-line clients, desktop or cloud agents, plugins, repository integrations, and connected services.
- Data: classify repositories, source code, secrets, customer information, vulnerability details, and third-party code as allowed, prohibited, or approval-required input and context.
- Actions: define which files, networks, credentials, local services, and external tools an agent can reach, and which operations require a person to approve them.
- Assurance: keep normal code review, testing, branch protection, security scanning, and incident response in charge of acceptance.
- Change control: record exceptions, administrator changes, incidents, and vendor changes, with review dates and expiry dates.
These decisions should be specific enough that a platform administrator can configure them and a developer can tell what to do when a request falls outside the defaults.
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Step 1: Inventory every assistant surface and data flow
Start with an inventory before choosing settings. An enterprise may have centrally purchased seats, individual accounts, IDE extensions, command-line tools, browser or desktop agents, cloud agents, and plugins operating at the same time.
Record products, plans, and identities
- Record the product name, paid plan or edition, tenant, and contract owner for every assistant.
- Separate enterprise-managed identities from personal accounts that can access company repositories. Decide whether personal accounts are prohibited, tolerated only through an exception, or technically blocked.
- List the identity provider, single sign-on path, group or team membership, seat-assignment process, and offboarding trigger.
- Capture repositories and organizations connected to each account, including forks, mirrors, issue trackers, and pull-request systems.
Record clients, models, and agents
List every supported IDE integration, command-line client, desktop application, web surface, autonomous or cloud agent, model selector, and extension. A policy that covers an IDE plug-in may not cover a command-line agent. GitHub documentation, for example, describes policies that can apply to IDE, website, and CLI surfaces while noting that the Copilot application and CLI have independent client policies.
Map connected services
Document Model Context Protocol (MCP) servers, local development services, ticketing and documentation systems, package registries, cloud accounts, source-control apps, and any plugin that can send context outside the repository. For each connection, record the data it can read, the actions it can perform, its authentication method, and its owner.
Include exceptions and unmanaged use
Ask teams where they use assistants outside the approved catalog. Include contractors, temporary repositories, personal devices, bring-your-own-model arrangements, and experiments. Each exception needs a business owner, a defined scope, compensating controls, an expiry date, and a removal process.
Step 2: Put authority at the level that can enforce it
Assign one accountable policy owner, such as a platform or security leader, and keep the administrator set small enough to review. Publish a responsibility map showing which team controls enterprise settings, which organization administrators can change, and which repository owners can customize.
GitHub’s organization-policy guidance says enterprise policies can constrain organization settings. Its enterprise policy guidance also warns that unclear governance and broad policy access can produce drift. Use that hierarchy deliberately:
- Set non-negotiable controls at the enterprise or tenant level, including approved plans, identity requirements, prohibited data classes, and minimum review expectations.
- Allow organization teams to add stricter controls for a product, business unit, or regulatory boundary, but not to weaken enterprise requirements.
- Permit repository-level instructions only for local conventions, such as test commands or style rules, unless they would override a security control.
- Review administrator membership regularly and log every policy change, who made it, and why.
Separate the person who approves a policy from the people who operate the platform when practical. At minimum, require a second reviewer for changes to data handling, network access, credentials, or autonomous actions.
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Risk tiers turn a broad “use responsibly” rule into predictable decisions. The following are policy choices you can adapt; they are not vendor defaults.
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Tier 1: Low-risk assistance
Permit explanation of public or non-sensitive code, documentation drafts, test scaffolding, refactoring suggestions, and local code completion when the repository is approved and no secrets or customer data are in context. Require the developer to inspect the output and run normal tests.
Tier 2: Controlled code changes
Allow edits to approved development repositories with read-only access to unrelated paths. Require a pull request, an accountable human reviewer, automated tests, static analysis, dependency checks, and secret scanning before merge. Keep credentials unavailable to the assistant unless a separate approval exists.
Tier 3: Sensitive or high-impact work
Require explicit approval before an assistant handles regulated data, vulnerability information, proprietary algorithms, production configuration, authentication code, infrastructure definitions, or third-party code with restrictive license terms. Limit the model, client, repository, and retention settings to a configuration reviewed by security and privacy teams.
Tier 4: Privileged or external actions
Require a human to approve production changes, destructive commands, secret access, database writes, cloud administration, package publication, external network calls, and actions that affect customers. Prefer a sandbox or a disposable environment, short-lived credentials, and a recorded change ticket.
Write the tier in the task request or workflow so that a developer does not have to infer it from the repository name alone.
Step 4: Write data-handling rules for the exact plan
Classify what may be sent in a prompt and what may be supplied as surrounding context. State separately whether the assistant may process source files, issue text, commit history, logs, stack traces, customer records, secrets, vulnerability reports, and third-party code.
Cover the full data lifecycle
- Collection: identify prompt text, selected files, automatically gathered context, telemetry, feedback, and connected-service responses.
- Use: determine whether the provider uses each category to generate a response, improve a model, detect abuse, or provide support.
- Retention: record how long prompts, outputs, chats, coding sessions, logs, and backups remain available.
- Deletion: document user deletion, administrator deletion, contract termination, legal holds, and backup expiration.
- Location and access: verify processing regions, subprocessors, support access, audit evidence, and contractual commitments.
- Disclosure: define when a developer must report that sensitive material was submitted or that an assistant materially contributed to a change.
Do not generalize a privacy statement from one plan to another. Confirm the terms for the actual SKU, client, region, and configuration.
Document the provider-specific statements you rely on
| Product or documentation scope | Documented behavior | Qualification to carry into your policy |
|---|---|---|
| GitHub Copilot Business and Enterprise | GitHub says customer data is not used to train models. | Copilot also uses contextual information to generate suggestions; confirm which context, logs, and connected services apply to your plan and client. |
| OpenAI specified business products and API | OpenAI says business data is not used for training by default, and qualifying organizations can configure retention. | Verify that your product and account qualify and record the retention configuration you actually select. |
| Anthropic commercial API (July 1, 2026 documentation) | Inputs and outputs are automatically deleted within 30 days. | Exceptions include longer-retention services, agreements, usage-policy enforcement, or legal requirements. |
| Anthropic Team and Enterprise product history (March 16, 2026 documentation) | Product history can save chats and coding sessions; Enterprise customers can set custom retention timelines. | Do not apply the API’s 30-day statement to saved product sessions. |
Have privacy counsel or the contract owner retain the provider terms, data-processing agreement, and configuration evidence with the policy version.
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Step 5: Control models, features, and clients explicitly
Choose an allowlist for models and features rather than assuming that future capabilities inherit today’s posture. Decide who can enable new models, agent modes, code review, web or cloud access, and extensions.
For each approved surface, specify whether it is enabled for all users, selected groups, or named repositories. GitHub’s policy documentation covers enterprise and organization controls, while its client guidance notes that the Copilot application and CLI have independent client policies. Test both; disabling a feature in one client is not evidence that it is disabled in another.
Track default changes as operational events. GitHub’s page on default availability states that its new-feature default policy is scheduled to begin applying on October 22, 2026. The current date for this guide is October 17, 2026, so treat that activation as an upcoming change and verify the resulting settings after the date rather than relying on the pre-change default.
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Step 6: Constrain agent actions with technical controls
Written prohibitions are insufficient when an agent can execute commands. Configure the narrowest boundary that still permits the approved task.
Filesystem and repository paths
- Define read-only paths for reference material and read/write paths for the working tree.
- Deny access to secrets directories, credential stores, production configuration, unrelated repositories, personal files, and build caches that contain sensitive artifacts.
- Use separate workspaces for experiments and production-bound code.
GitHub’s managed-settings documentation describes read/write and read-only path controls and denied paths. Record the effective path list for every client that supports agents.
Network and local services
- Default to no external network access for low-risk coding tasks.
- Allow only named package registries, documentation hosts, or internal services when the task requires them.
- Block access to metadata endpoints, production hosts, and arbitrary outbound destinations.
- Require a human approval for a new destination or a request to upload files.
Credentials
Do not expose long-lived personal tokens, cloud administrator keys, SSH private keys, or production database passwords to an agent. Use short-lived, least-privilege credentials issued for one task, and make their use visible in logs. A task that needs a secret should pause for an approval step rather than silently discovering it from the filesystem.
MCP servers and extensions
Maintain an allowlist of MCP servers and plugins. For each one, specify tools, data sources, write operations, authentication, owner, and expiry review. GitHub’s managed settings include MCP server allow and deny controls; configure them centrally where available and test that a denied server cannot be invoked through another client.
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Sandbox and operating-system behavior
Require a sandbox for autonomous commands and document the supported operating systems. GitHub’s local-sandbox documentation describes platform limitations and says that, in the described configuration, a shell fails rather than running unsandboxed when the host cannot enforce the requested policy. Treat that failure as a safety signal: do not instruct developers to bypass it. Validate filesystem, network, credential, and approval behavior on every operating-system and client combination you support.
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Step 7: Keep human review and software controls in charge
Require developers to understand generated code, inspect the diff, run the project’s tests, and evaluate security, licensing, reliability, and maintainability. Generated code enters the same branch protections and acceptance criteria as hand-written code.
- Require pull requests and accountable reviewers for material changes.
- Run unit and integration tests, static analysis, dependency scanning, and secret detection in continuous integration.
- Require threat modeling or security review for authentication, authorization, cryptography, infrastructure, and data-processing changes.
- Keep production deployment approvals separate from the assistant interaction.
- Record AI assistance when that information is useful for incident response or a local regulatory obligation, without assuming a universal disclosure rule.
GitHub supports enterprise code-review controls and custom instructions. Automatic review can help detect issues and apply standards, but GitHub cautions that it can also add noise. Set expectations for triaging its comments and never treat an automated approval as the team’s acceptance decision.
OpenAI described the operational goal in “Running Codex safely at OpenAI” on May 8, 2026: “keep the agent inside clear technical boundaries, let developers move quickly on low-risk actions, and make higher-risk actions explicit.” Use that principle to set approval thresholds rather than trying to remove every low-risk interaction.
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Run a pilot with representative repositories and users from each operating system, IDE, CLI, and agent surface. Test both permitted and denied behavior.
- Create a low-risk task and confirm the assistant can read only the intended paths and complete the expected test workflow.
- Attempt to open a denied file, read a credential, reach a blocked network destination, invoke an unapproved MCP server, and execute a destructive command. Confirm each attempt is blocked or pauses for approval.
- Repeat the tests in every supported client and operating system. Capture the effective policy, not just the administrator’s configured value.
- Submit synthetic sensitive strings to verify redaction, logging, retention, and deletion procedures without exposing real customer or production data.
- Open a pull request containing generated changes and verify branch protection, required checks, reviewer assignment, and deployment gates.
- Test a vendor default or model change in a staging organization before enabling it broadly.
Keep test evidence with the policy version. If a host cannot enforce a required control, stop the rollout or change the task design; do not rely on an informal promise that users will behave as if the control existed.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Step 9: Operate the policy as a living control
Set a review cadence and triggers. Review at least when a vendor changes model availability, feature defaults, retention terms, client behavior, or sandbox support; when your organization adds a new data classification; and after an incident or near miss.
- Maintain a register of exceptions with owner, reason, compensating controls, approval date, and expiry date.
- Alert on administrator changes, new seats, personal-account access, new models, new MCP servers, and changes to network or credential settings.
- Collect audit logs and retain the subset needed for incident investigation and contractual obligations.
- Define an incident path for accidental prompt disclosure, unauthorized tool use, suspicious generated code, and policy bypass attempts.
- Revoke seats, tokens, sessions, and connected services during offboarding or an incident.
Measure operational signals such as blocked-action events, exception age, review findings, and recurring policy failures. Avoid claiming that a policy improves productivity or security unless your own controlled measurement supports that conclusion.
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Questions to answer during product and plan selection
When comparing assistants or plan tiers, use the same questions for each candidate. The reviewed documentation establishes concrete controls but not a complete like-for-like vendor comparison or current pricing.
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| Decision axis | Evidence to collect |
|---|---|
| Administration and identity | Enterprise and organization scope, single sign-on, group management, seat controls, administrator roles, and change-audit export. |
| Data use | Training treatment, prompt and context handling, retention, deletion, processing location, subprocessors, support access, and contract language for the exact SKU. |
| Feature and model governance | Allow or deny controls for models, agent modes, IDEs, CLI, web, cloud features, extensions, and new-feature defaults. |
| Technical containment | Filesystem paths, network policy, credential handling, sandbox enforcement, MCP controls, approval gates, and operating-system coverage. |
| Audit and response | Logs, export formats, alerting, incident support, and evidence that can be retained for investigations. |
| Review workflow | Pull-request integration, branch protections, custom instructions, automated review controls, and the ability to separate machine suggestions from human approval. |
| Terms and support | Contract commitments, service limits, support response, regional availability, and documented change-notification practices. |
How to answer the two questions leaders ask most often
“How does Copilot use my company’s data?”
Answer from the configured plan and client, not from a generic product slogan. State what prompt and surrounding code context are collected, which logs and feedback are retained, whether data is used for training, which connected services receive it, where it is processed, who can access it, and how deletion works. GitHub says Copilot Business and Enterprise customer data is not used to train models, while also describing contextual information used to generate suggestions. Your internal answer should identify the exact repositories, clients, and settings covered by that statement.
“Which compliance standards does Copilot meet?”
Do not provide a blanket certification answer without checking the current plan, region, contract, and trust documentation. Ask the provider for the applicable attestations, scope, dates, subprocessor list, data-residency commitments, deletion terms, audit reports, and shared-responsibility description. Map those documents to your own control requirements and record any gaps or compensating controls. A provider’s certification does not make your repository permissions, review process, or secret handling compliant by itself.
A concise policy document your team can implement
Publish one version-controlled policy with these sections:
- Purpose and scope: covered products, repositories, users, clients, operating systems, and exceptions.
- Ownership: policy owner, administrators, approvers, organization responsibilities, and review date.
- Approved use: risk tiers, allowed tasks, prohibited data, and approval-required actions.
- Data handling: provider-specific training, retention, deletion, location, contract, and connected-service rules.
- Technical controls: model and feature allowlists, paths, network, credentials, sandbox, MCP, and client settings.
- Engineering controls: pull requests, reviewers, tests, security checks, branch protection, deployment gates, and provenance expectations.
- Monitoring: logs, alerts, exception register, incident process, and evidence retention.
- Change management: vendor-change review, revalidation tests, communication, and rollback.
Common failure modes and fixes
One broad rule covers every tool
Failure: an IDE extension, CLI, and cloud agent receive the same permission even though their data paths and execution abilities differ.
Fix: maintain a surface-by-surface inventory and configure independent client policies where the product supports them.
“No training” is treated as “no exposure”
Failure: a team assumes that a no-training statement eliminates retention, logging, support access, or connected-service risk.
Fix: document collection, use, retention, deletion, location, and access separately for the exact plan.
Written prohibitions replace technical barriers
Failure: the policy forbids production access but the agent can read a local credential or call an unrestricted network endpoint.
Fix: enforce paths, network, credentials, sandbox, and approvals, then run negative tests on every client and operating system.
Automatic review becomes the approver
Failure: generated review comments or an automated pass are treated as acceptance.
Fix: retain accountable human review, required checks, and branch protections for material changes.
Defaults change unnoticed
Failure: a new model or feature becomes available under a permissive default.
Fix: set explicit defaults, subscribe to vendor change information, and re-test after scheduled activations such as GitHub’s October 22, 2026 new-feature policy change.
Quick Recap
Final rollout checklist
- A named owner and limited administrator group are in place.
- Enterprise, organization, repository, client, and operating-system responsibilities are documented.
- Products, plans, models, agents, extensions, MCP servers, identities, and exceptions are inventoried.
- Risk tiers specify allowed data, tasks, approvals, and prohibited operations.
- Provider terms for training, retention, deletion, location, and contracts are verified for each SKU.
- Filesystem, network, credential, sandbox, MCP, and human-approval controls are configured and tested.
- Pull requests, branch protection, tests, security scans, and deployment approvals remain mandatory where appropriate.
- Negative tests prove that denied actions fail safely on every supported client and operating system.
- Logs, exceptions, incidents, administrator changes, and vendor changes have owners and review dates.
- The policy has a communication, rollback, and revalidation process.
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