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Approve an AI coding assistant only after you have verified the exact plan and contract, what data the service handles, required privacy and compliance evidence, administrative and audit controls, technical deployment fit, and how generated code will enter your secure-development process. Then run a limited, measured pilot with named owners, cost limits, and stop criteria before expanding access.
The right decision is organization-specific. A vendor’s enterprise controls can support governance, but they do not automatically satisfy your legal, regulatory, security, or architecture requirements.
1. Define the business purpose, permitted use, and accountable owners
Start with a written use-case statement rather than a tool purchase. Specify which problems the assistant may address—such as code completion, test drafting, documentation, refactoring, or troubleshooting—and which activities are out of scope. Different features can send different context to a service, so approval should cover the feature set, not just the product name.
Assign a cross-functional approval group
Include engineering, application security, IT, privacy, legal, compliance, procurement, and the business owner for the teams that will use the assistant. Give one person authority to accept residual risk and another authority to approve exceptions. Record who owns policy, configuration, incident response, contract review, and renewal decisions.
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Classify code and data before users sign in
Map your data-classification policy to assistant use. Explicitly identify whether users may submit public code, internal source, customer data, personal data, credentials, security findings, regulated records, or information covered by nondisclosure agreements. If a class is prohibited, enforce that rule with technical exclusion controls where available and with training and review procedures.
Set an exception process
Define how a team can request an exception, what evidence is required, who approves it, how long it lasts, and how it is logged. Temporary access for an incident or a legacy repository should not silently become a permanent policy.
2. Verify the exact product, plan, purchaser, and contract
Record the product name, edition, purchased features, billing entity, deployment model, and intended users. Terms and data practices can differ by plan, geography, preview status, and purchase route. Do not approve a generic “enterprise AI” description when the contract names a narrower or different service.
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Trace the purchase route to the governing terms
GitHub’s approval materials illustrate why this 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. Treat this as a vendor-specific example, not a rule for every provider, and verify the current agreement attached to your own order.
Read the operational clauses, not only the marketing summary
Ask legal and privacy reviewers to identify:
- Which entity is the service provider and which entities may process data.
- Whether prompts, code context, telemetry, and outputs are retained, for how long, and for what purposes.
- Whether customer content is used to train or improve models, and what opt-out or configuration applies.
- Subprocessor, international-transfer, deletion, breach-notification, and assistance obligations.
- Ownership and license language for inputs, generated outputs, and feedback.
- Service-availability commitments, support response levels, suspension rights, and exit assistance.
- How previews, model changes, and new features are added to the agreement.
Capture the document version and review date in your procurement record. Recheck terms at renewal and whenever the provider changes the plan or feature set.
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3. Establish what data the assistant processes
Ask the provider to describe the complete data flow for each enabled feature. “The model does not train on your code” is not a complete answer: a service may still process prompts, retrieved files, editor context, account identifiers, diagnostics, or usage telemetry to deliver and secure the service.
Build a data-flow inventory
For every workflow, document:
- What the user submits deliberately, such as a prompt or selected code.
- What the client collects automatically, such as open files, repository metadata, language-server context, diagnostics, or conversation history.
- Where processing occurs and which regions or subprocessors are involved.
- What is stored in logs, for how long, and who can access it.
- What the organization can delete, export, or restrict through administration settings.
- What appears in outputs and downstream systems such as pull requests, tickets, or build logs.
Test exclusion and redaction controls
Confirm whether administrators can exclude repositories, paths, file patterns, or data categories. Test the control with a nonproduction repository and inspect logs or policy reports to verify that excluded content is not being sent. If the provider offers sensitive-content exclusion, determine whether it is enabled by default, whether it covers every feature and model, and what happens when a user attempts to override it.
Separate prompts from source-code policy
A policy that allows internal source may still prohibit secrets, personal data, export-controlled material, or customer-provided code. Provide examples of allowed and forbidden prompts, and train users not to paste credentials, tokens, private keys, production records, or unredacted incident data into a chat panel.
4. Check privacy, security, and compliance evidence
Match the provider’s current trust-center documents and attestations to your obligations. A certification or audit report is evidence about a defined scope and period; it is not a blanket finding that your deployment is compliant.
Ask for evidence that maps to your requirements
Review the relevant security reports, privacy terms, subprocessors, transfer mechanisms, vulnerability-management information, and incident-notification commitments. Have compliance staff record which control or obligation each document supports, its coverage dates, exclusions, and any customer configuration required.
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Use recognized risk-management guidance
The National Institute of Standards and Technology (NIST) says that organizations should apply standards, guidelines, tools, and practices to manage cybersecurity and privacy risk in the age of AI, adopt AI responsibly, and adapt defenses to AI-enabled threats. Use that principle to map the assistant to your own risk register, threat model, and privacy impact assessment rather than treating a vendor checklist as sufficient.
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Decide how your team will respond if confidential material is submitted accidentally, an account is compromised, or an output introduces a vulnerability. Procedures should cover access revocation, token rotation, repository review, provider notification, legal assessment, user communication, and evidence preservation.
5. Keep generated code inside a secure-development workflow
An assistant can draft code quickly, but every suggestion remains untrusted until a developer reviews, tests, and approves it. Do not weaken branch protection, code-owner review, dependency checks, or release gates to accommodate generated changes.
Apply NIST’s generative-AI practices
NIST Special Publication 800-218A, published July 26, 2024, adds generative-AI-specific practices to the Secure Software Development Framework. Its guidance calls for careful handling of inputs—including prompts and user data—and outputs. Incorporate those practices into your existing requirements, design, implementation, testing, release, and monitoring activities.
Address common failure modes
OWASP DevSecOps guidance identifies risks including insecure generated code, hallucinated or malicious dependencies, and disclosure of proprietary code, personal data, or credentials to third-party models. Convert those risks into controls:
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- Require human review by someone who understands the affected component.
- Run unit, integration, security, and regression tests; do not treat a plausible explanation as validation.
- Pin and verify dependencies, check provenance, and review license obligations before adoption.
- Use static analysis, secret scanning, software-composition analysis, and runtime testing in the normal pipeline.
- Record when material code was generated so reviewers can focus on assumptions and edge cases without exempting it from policy.
No reliable universal rate for these failures is established by the cited guidance, so set controls based on your threat model and observed pilot data.
6. Validate identity, network, client, and distribution requirements
Technical incompatibility can create security workarounds. Confirm the deployment path with IT and security before enabling users.
Identity and administration
- Verify support for your identity provider, single sign-on, multifactor authentication, group provisioning, and deprovisioning process.
- Determine whether enterprise accounts are isolated from personal accounts and whether administrators can restrict model or feature access.
- Check role granularity for policy management, billing, audit review, and support.
- Confirm that departing staff lose access promptly and that service tokens or cached credentials are revoked.
Network and endpoint controls
- List required domains, ports, certificate behavior, and proxy or TLS-inspection requirements.
- Test allowlists from developer networks, remote-access paths, build environments, and restricted segments.
- Check whether the IDE extension, command-line client, and web interface have different network or data behavior.
- Verify supported operating systems, IDEs, client versions, and automatic-update controls.
Software distribution and support
Decide how approved extensions and clients reach workstations: managed software deployment, signed packages, or another controlled channel. Define the supported version range, rollback method, help-desk ownership, and a process for removing a vulnerable client quickly.
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When evaluating more than one assistant, use a common scorecard. The sources support these comparison dimensions but do not establish a current cross-vendor winner.
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| Evaluation area | Questions to answer | Evidence to retain |
|---|---|---|
| Data handling and contract | What content and telemetry are processed, retained, or used for improvement? Which terms govern this purchase route? | Signed order, service terms, data-processing agreement, retention and deletion statements |
| Compliance fit | Which attestations and reports cover the service, region, and features you will use? | Trust-center documents, control mapping, privacy and transfer assessment |
| Administration and audit | Can you restrict features or models, exclude sensitive content, manage groups, and review activity? | Configuration records, audit-log samples, exclusion test results |
| Secure-development integration | Will generated changes pass review, testing, dependency, and secret-scanning gates? | Pipeline evidence, review policy, dependency and license checks |
| Deployment fit | Do identity, network, IDE, client, and distribution requirements match your environment? | Architecture review, compatibility matrix, pilot support tickets |
| Pilot evidence and cost | Do intended users adopt approved features at an acceptable, predictable cost? | Usage, spend, feedback, defect findings, and stop/continue decisions |
If a candidate cannot provide a required item, record it as “not stated by the provider” rather than assuming the most favorable interpretation.
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8. Run a controlled pilot before wider rollout
A pilot converts claims into evidence from your repositories, policies, users, and network. GitHub’s rollout guidance recommends piloting a feature or model to validate adoption, control costs, and gather evidence for a broader deployment. That recommendation does not prove that any assistant increases productivity.
- Select a bounded cohort. Include representative languages, repositories, seniority levels, and network conditions. Exclude regulated or business-critical code until controls are proven.
- Approve specific use cases. Start with tasks whose review path is clear, such as test scaffolding or documentation, and list prohibited uses.
- Configure guardrails first. Apply identity groups, content exclusions, logging, network controls, branch protection, and software-distribution rules before inviting users.
- Train participants. Cover prompt hygiene, confidential-data handling, verification, dependency review, attribution or licensing questions, and incident reporting.
- Measure agreed outcomes. Track active use of approved features, spend per user or repository, review and rework effort, defect findings, security-tool results, support load, and user-reported friction. Define collection methods and a baseline before the pilot starts.
- Set stop criteria. Pause access for a material data leak, unacceptable security finding, uncontrolled cost, repeated policy violation, or inability to audit activity. Assign an owner and response time for each trigger.
- Make a documented decision. Continue, change configuration, narrow scope, or stop. Record evidence, unresolved risks, and conditions for the next phase.
There are no universal success or cost thresholds. Set limits that reflect your organization’s budgets, risk tolerance, engineering baseline, and regulatory obligations.
9. Govern changes after approval
Approval is not permanent because plans, models, clients, and policies change. Schedule reviews at renewal and whenever a provider adds a feature, changes retention or training terms, introduces a new model, or alters supported clients.
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Monitor the controls that can drift
- Review administrator settings, group membership, exclusions, audit logs, and spending alerts.
- Sample repositories for prohibited data, unreviewed generated changes, and dependency issues.
- Revalidate network and client versions after updates.
- Refresh the contract, privacy assessment, threat model, and compliance mapping when scope changes.
- Reassess whether pilot outcomes still support the approved use cases.
Keep an exit plan
Document how to disable accounts, remove extensions, revoke tokens, export required records, delete retained content where applicable, and transition teams to an approved workflow. An exit test is especially important when the assistant is integrated with source control, identity, ticketing, or build systems.
10. A practical go/no-go decision
Approve a pilot only when the organization can name the service and governing terms, identify permitted data and users, demonstrate essential exclusion and access controls, integrate generated changes with secure development, and monitor cost and incidents. Delay or reject adoption when a material data-flow question is unanswered, contractual protections do not match the data being processed, required audit or identity controls are unavailable, or the team cannot review and test generated code.
Use the decision record to state residual risks and compensating controls, not merely “approved” or “rejected.” That record gives engineering, security, legal, compliance, IT, and procurement a shared basis for revisiting the decision as evidence and product capabilities change.
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