Approve an AI coding assistant only after you can answer six questions for the exact product, plan, and configuration: what data it receives; what the contract permits; which compliance evidence supports your obligations; which identity, audit, and content-exclusion controls are available; whether it fits your network and development toolchain; and how generated code will be reviewed and tested. Run a limited, measured pilot with named owners and stop criteria before expanding access.
Start with an approval decision, not a product demo
An AI coding assistant can affect source code, prompts, developer telemetry, dependencies, credentials, and regulated or personal information. The approval question is therefore broader than whether suggestions look useful. Your organization must decide what uses are allowed, who is accountable, what evidence is required, and how the service will be operated after purchase.
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Form a review group with engineering, information security, IT, legal, privacy, compliance, and procurement. Give the group authority to approve the standard configuration and to decide when an exception needs a separate review. Record the decision for a named product and plan rather than for an abstract category called “AI coding tools.”
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Define permitted use and accountable owners
Classify the work the assistant may support
Write a policy that distinguishes allowed, restricted, and prohibited uses. For example, you might permit generation of boilerplate in low-risk repositories, test scaffolding, documentation drafts, and explanations of code that is already approved for the service. You might require additional approval for production authentication, cryptography, payment processing, safety-critical logic, infrastructure-as-code, or code handling regulated information. You might prohibit pasting secrets, credentials, personal data, customer content, unreleased source code, or material subject to a legal hold.
The policy should cover every enabled feature, not only inline completion. Chat, repository indexing, pull-request summaries, issue analysis, agentic editing, code transformation, and model-selection options can expose different context. State which repositories, teams, data classes, and environments are in scope, and identify who can approve an exception.
Assign responsibilities
- Engineering: owns coding standards, review requirements, test coverage, and repository eligibility.
- Security: assesses threat scenarios, secret handling, software supply-chain risk, and monitoring.
- IT and identity: manages accounts, single sign-on, provisioning, endpoint configuration, and network access.
- Legal and privacy: interprets data-processing terms, confidentiality obligations, intellectual-property issues, and jurisdictional requirements.
- Compliance and procurement: checks attestations, retention commitments, purchasing authority, renewal terms, and evidence needed for audits.
Document a service owner who remains responsible after launch. A one-time approval without operational ownership will not keep pace with changing models, features, clients, or terms.
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Verify the exact product, plan, purchaser, and contract
Do not rely on a vendor’s general marketing page or on a consumer plan’s settings when your organization will buy an enterprise plan. Record the product name, edition, purchaser, enabled features, model-access policy, regions involved, and the account structure used for employees and contractors.
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Map the purchase route to the governing terms
GitHub’s approval materials illustrate why the purchase route matters. A direct GitHub purchase is governed by GitHub Generative AI Services Terms. A Microsoft purchase is governed by Microsoft Product Terms, including Microsoft Generative AI Service terms and GitHub Offering terms. GitHub also states that its Data Protection Agreement applies to generally available Copilot features and to specifically listed previews. Those terms are a vendor-specific example, not a rule for every service. Obtain and archive the agreement that actually governs your transaction.
Check whether previews, beta models, extensions, or separately enabled features have different terms or data practices. Confirm the order form, data-processing agreement, subprocessors, confidentiality language, intellectual-property provisions, retention and deletion commitments, security obligations, breach-notification process, service levels, and termination assistance. Ask how changes to terms are announced and whether continued use constitutes acceptance.
Resolve ownership and permitted-use questions
Legal and engineering should determine how the contract treats prompts, supplied code, generated output, feedback, and telemetry. Establish whether the provider may use organizational content to train or improve models, whether that setting is controlled by the administrator, and what happens to data after an account is disabled. Verify these points for the selected plan and region; they cannot be inferred from a different edition.
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Trace every data flow
Ask the provider to describe what is sent when a developer accepts a completion, asks a chat question, indexes a repository, or invokes an agent. Include prompt text, surrounding code, file names, repository metadata, account identifiers, diagnostic logs, feedback, and generated output. Map each flow to your data-classification policy and retention requirements. Separate data needed to deliver the feature from optional telemetry and product-improvement data.
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Require an administrator-controlled method to prevent sensitive content from being included. GitHub identifies sensitive-content exclusion, feature and model access policies, and audit logs as governance controls. Confirm which repositories, paths, languages, clients, and features those controls cover, how exclusions are tested, and what evidence an auditor can retrieve.
Match evidence to your obligations
Review the provider’s current trust-center documentation, independent audit reports, certifications or attestations, penetration-test summaries where available, incident history, subprocessor list, and data-residency statements. Compliance evidence is useful only when its scope, date, service boundary, and control objectives match your obligations. A certification for one cloud service or region does not automatically cover an assistant feature delivered through another boundary.
Record open issues rather than treating a trust-center badge as a complete risk assessment. Your privacy team may still need a data-protection impact assessment, transfer analysis, records of processing, or a review of employee-monitoring implications. Regulated teams should identify the specific control, retention period, access restriction, or audit artifact they need and verify that the product can produce it.
Make generated code part of the secure-development process
An assistant’s output is untrusted code. Keep it under the same review, testing, dependency validation, and release controls as code written without assistance. NIST SP 800-218A, published July 26, 2024, adds generative-AI-specific secure-development practices to the Secure Software Development Framework. It calls for careful handling of inputs, including prompts and user data, and of outputs.
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OWASP’s DevSecOps guidance highlights several risks to assess: insecure generated code, hallucinated or unsuitable dependencies, and disclosure of proprietary code, personal data, or credentials to third-party models. The guidance identifies these as risks to govern; it does not establish a universal defect rate or productivity figure.
Set mandatory review gates
- Require a human author or reviewer to understand and approve every accepted change.
- Run unit, integration, security, and regression tests appropriate to the component.
- Scan dependencies for known vulnerabilities, licensing concerns, typosquatting, and unexpected transitive packages.
- Use secret scanning before a change enters a repository or build system.
- Apply static analysis and policy checks to generated infrastructure, configuration, and application code.
- Require threat modeling or specialist review for authentication, authorization, cryptography, payments, safety functions, and other high-impact components.
- Keep normal pull-request history and attribution so reviewers can investigate how a change entered the codebase.
Train developers to treat confident explanations as suggestions, not evidence. An assistant can omit edge cases, misunderstand local conventions, reproduce insecure examples, or recommend a package that does not exist. The required response is verification, not a ban on useful assistance.
Validate identity, network, client, and distribution requirements
Before procurement, test the service in the environment where it will run. GitHub’s enterprise approval guidance lists client versions, network configuration, authentication, identity-provider integration, enterprise account controls, and software distribution as IT and cybersecurity concerns. The same categories apply when evaluating another provider, but the supported details must be confirmed for that provider.
Identity and administration
- Confirm support for your identity provider, single sign-on, multifactor authentication, automated provisioning, and timely deprovisioning.
- Define administrator roles and least-privilege permissions for enabling features, selecting models, viewing usage, and exporting logs.
- Check whether contractors, guests, personal accounts, and unmanaged devices can be blocked or separated.
- Verify how organizations, repositories, teams, and billing accounts map to policy boundaries.
Network and endpoint operation
- List required domains, ports, protocols, certificate behavior, proxy support, and inspection exceptions for corporate networks.
- Test restrictive egress, virtual desktops, remote development, and offline or intermittent connections if those are normal in your environment.
- Confirm supported IDEs, editor extensions, command-line clients, operating systems, and minimum client versions.
- Decide how extensions are packaged, updated, signed, and removed through your software-distribution tools.
- Measure the administrative effort for upgrades; an unsupported client can create both security and availability problems.
Compare candidates using evidence, not feature counts
When more than one assistant is under consideration, use the same questions and evidence standard for each. The following axes are supported by enterprise approval, secure-development, and governance guidance:
| Evaluation area | Evidence to request | Decision question |
|---|---|---|
| Data handling and contract | Applicable terms, data-processing agreement, retention and deletion rules, training-use controls, subprocessors, regions, and purchase route | Can the organization use the service without violating confidentiality, privacy, or contractual duties? |
| Compliance fit | Current trust-center materials, attestations, audit scope, incident process, and required customer responsibilities | Does the evidence address the organization’s actual jurisdictions and control obligations? |
| Administration and audit | Feature and model policies, sensitive-content exclusion, role controls, provisioning, logs, and export options | Can administrators prevent prohibited use and investigate activity? |
| Secure-development workflow | Integration with review, testing, dependency checks, secret scanning, and policy gates | Can generated changes follow existing release controls without creating bypasses? |
| Technical deployment | Supported clients, identity-provider setup, network requirements, proxy behavior, and distribution method | Can IT deploy and maintain the approved configuration on managed endpoints? |
| Pilot evidence | Adoption by approved cohort, support load, cost records, quality findings, and user feedback | Does observed value justify the residual risk and operating cost for this organization? |
The sources do not establish a current cross-vendor winner or a universal productivity benchmark. A feature checklist cannot substitute for evidence from your own environment.
Design a controlled pilot before broad rollout
Choose a bounded cohort and use cases
Start with a limited group that represents your languages, IDEs, repositories, regions, and security constraints. Exclude high-impact systems until the review group explicitly approves them. Provide training on permitted data, verification of output, dependency review, secret handling, and reporting of suspected exposure or insecure suggestions.
Define measures and stop conditions
Set organization-specific measures before access is granted. Track activation and sustained use, accepted suggestions, review findings, security exceptions, support tickets, latency, spend, and time spent administering the service. Pair usage data with qualitative feedback from developers and reviewers. Do not claim that a tool improves productivity unless your design can measure the outcome against a credible baseline.
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Set stop or rollback conditions for events such as confirmed sensitive-data exposure, an inability to enforce repository exclusions, unacceptable security findings, uncontrolled spending, or failure of deprovisioning. Define who can suspend access and how affected credentials, repositories, logs, and contracts will be reviewed.
Use the pilot to test controls, not just enthusiasm
- Provision only approved accounts and repositories through the enterprise identity path.
- Apply the intended model, feature, network, and content-exclusion policies.
- Have developers perform representative tasks, including ordinary code, tests, documentation, and a deliberately restricted case to verify that controls work.
- Run normal review, testing, dependency, and secret-scanning pipelines on every accepted change.
- Collect cost, adoption, quality, security, and support evidence for the agreed period.
- Review findings with all accountable owners and record remediation, residual risk, or a decision not to expand.
GitHub recommends piloting a feature or model to validate adoption, control costs, and gather evidence for broader rollout. That is a rollout practice, not proof that any assistant increases productivity.
Plan for change after approval
AI services change faster than traditional developer tools. At each renewal, major feature release, model change, client update, or contract revision, recheck terms, data policies, available controls, supported clients, regions, and logging. Require notification to the service owner when teams request a new feature, connect a new repository class, or change the purchase route.
Keep an inventory of enabled features and exceptions, the date of the last legal and security review, the evidence relied upon, and the person who can suspend the service. Reassess the threat model when the assistant gains repository-wide context, autonomous actions, external tool access, or the ability to modify files without an explicit per-change request.
Quick Recap
Approval checklist
- Named product, plan, purchaser, region, features, and governing agreement are recorded.
- Permitted, restricted, and prohibited data and use cases are documented.
- Engineering, security, IT, legal, privacy, compliance, procurement, and a service owner have assigned responsibilities.
- Prompt, context, code, telemetry, output, retention, training-use, and deletion practices are understood for the exact configuration.
- Trust-center evidence and attestations have been matched to organizational obligations.
- Identity, provisioning, administrator roles, audit logs, sensitive-content exclusion, network access, clients, and distribution have been tested.
- Generated code remains subject to human review, tests, dependency validation, secret scanning, and secure-release gates.
- A bounded pilot has approved users, approved repositories, training, cost monitoring, success measures, and stop criteria.
- A review schedule covers product, model, feature, client, and contract changes.
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