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Protect client data in an AI coding workflow by controlling what may be submitted, obtaining authorization, minimizing and de-identifying context, checking the exact service plan and settings, and limiting repository permissions. Do not treat a “not used for training” promise as proof that prompts are neither logged nor accessible: a work-account coding chat can exclude training while still retaining interactions that administrators can search. Your process should cover chat, IDE extensions, command-line tools, agents, connected repositories, and each model or subscription tier separately. The practical goal is a documented, least-privilege workflow that lets developers use assistance without sending unnecessary client code, personal information, credentials, or production data.
Can I paste client code into an AI coding assistant?
Only when the specific data is permitted by the client agreement, your organization’s policy, applicable law, and the service configuration you have checked. Publicly accessible tools should not be assumed suitable for confidential material. A safer default is to use public or synthetic examples, remove identifiers and secrets, and submit client-confidential or sensitive information only through an approved configuration with explicit authorization.
“Client code” is not one risk category. A public repository, an internal library covered by a nondisclosure agreement, a customer record embedded in a test fixture, and a production access token require different decisions. Before a developer opens an assistant, classify the material and identify contractual and jurisdictional restrictions.
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- Public code: Code already intentionally published can generally be used for debugging or explanation, provided the prompt does not add confidential context, personal information, or secrets.
- Synthetic examples: Fabricated data and reduced reproductions are preferred when they can demonstrate the issue without exposing a client system.
- De-identified examples: Remove direct identifiers and unnecessary business details before submission. De-identification lowers exposure but does not automatically eliminate re-identification risk.
- Approved internal code: Use only a service and account covered by your organization’s approval, with repository access and retention settings that match the policy.
What should be blocked by default?
- Client-confidential source code when the client contract or internal policy has not approved the service.
- Personal information, especially sensitive information, unless an authorized workflow explicitly requires it.
- Passwords, API keys, private certificates, session tokens, connection strings, and other credentials.
- Production database extracts, logs containing customer records, and unrelated files copied merely to provide context.
The Office of the Australian Information Commissioner (OAIC) advises as a best practice that organizations avoid entering personal information, particularly sensitive information, into publicly available generative-AI tools. That is Australian regulator guidance, not a universal legal rule; apply the law and contract terms for the client and jurisdiction involved.
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Why is “not used for training” not enough?
Training use is only one part of a data-handling assessment. A provider can exclude prompts and responses from foundation-model training while still logging them, retaining them for a period, making them searchable by an administrator, routing them through subprocessors, or exposing them to connected services.
For every feature, establish five separate facts:
- Training and product improvement: Whether prompts, completions, code snippets, and attached context may be used to train or improve models.
- Retention: What is stored, for how long, where it is stored, and whether deletion is available to the user or administrator.
- Human and administrative access: Whether provider personnel, account administrators, security teams, or reviewers can access interactions.
- Third-party processing: Which subprocessors, model hosts, search services, telemetry systems, or connectors receive the data.
- Feature boundaries: Whether the web chat, IDE extension, command-line interface, autonomous agent, and repository connector follow the same rules.
Microsoft’s description of Copilot Chat with a work or school account illustrates the distinction: it says prompts and responses are not used to train foundation models, while also stating that prompts, triggered Bing queries, and responses are logged and may be searched or audited by an IT administrator. A training exclusion therefore does not mean zero retention or zero access.
How should a team classify information before enabling an assistant?
Create a short classification rule that developers can apply before they copy, attach, or allow an agent to read anything. The categories below are a practical starting point; map them to the client’s contract, your organization’s policy, and local privacy requirements.
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| Information category | Default treatment | Conditions for use |
|---|---|---|
| Public code and documentation | May be submitted | Confirm that the prompt adds no confidential context, personal information, or secrets. |
| Synthetic or de-identified examples | Preferred for debugging | Perform the transformation before submission and consider whether the remaining details could identify a person, client, system, or transaction. |
| Approved internal code | Use only in an approved account and workspace | Check the organization’s settings, retention terms, repository permissions, and model or feature scope. |
| Client-confidential code or issue details | Hold unless explicitly authorized | Verify the client agreement, internal approval, applicable law, service terms, and exact deployment before sending. |
| Personal or sensitive information | Do not submit by default | Submit only when an authorized, documented use requires it and the approved service has suitable controls. |
| Credentials and production data | Prohibited in prompts and attachments | Use redacted, synthetic, or narrowly scoped test material instead; revoke a credential if it is exposed. |
OAIC notes that privacy obligations can apply both to personal information entered into an AI system and to outputs that contain personal information. Classification must therefore cover the assistant’s response, not just the input.
What authorization do I need before using confidential or sensitive client data?
Obtain authorization before submission, not after a developer has already pasted the material. Check three layers: the client relationship, your organization, and the law that applies to the people and data involved.
Check the client agreement
- Look for confidentiality, subcontracting, data-location, security, deletion, and approved-tool clauses.
- Determine whether client code, logs, tickets, screenshots, or prompts may be processed by an external provider or model host.
- Record any requirement to notify the client, obtain written consent, or use a named environment.
Check internal approval
- Identify the service, subscription tier, model, feature, and account that are approved.
- Assign an owner responsible for settings, access reviews, retention, and incident escalation.
- Define who may approve an exception and what evidence must be retained.
Check applicable privacy obligations
The Office of the Privacy Commissioner of Canada’s generative-AI principles state: “Where personal information (and, in particular, sensitive or confidential information) must be entered into a prompt, only do so where authorised.” Treat that as a clear operational rule, while recognizing that Canadian guidance and other regulators’ guidance have jurisdictional scope.
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If authorization is unclear, stop at a synthetic or de-identified reproduction and ask the client or privacy owner. A developer’s access to a repository does not by itself authorize sending its contents to a separate AI service.
How do I minimize and sanitize prompts, code, and logs?
Send the smallest context that can answer the question. Minimization reduces the amount that could be logged, retained, disclosed, or accidentally included in a generated response.
- Reproduce the issue locally. Identify the smallest file, function, stack trace, or test case that demonstrates the behavior.
- Remove direct identifiers. Replace names, email addresses, customer numbers, addresses, account IDs, and other identifying fields with consistent placeholders or synthetic values.
- Strip secrets. Delete API keys, passwords, bearer tokens, private keys, cookies, signed URLs, database credentials, and environment files. Do not rely on the model to ignore them.
- Remove unrelated context. Exclude neighboring repositories, complete ticket histories, customer exports, and files that are not necessary for the answer.
- Sanitize logs and screenshots. Redact request bodies, headers, URLs with embedded tokens, hostnames, and personal data before attaching them.
- Prefer a synthetic reproduction. Rewrite the failing input and schema so the behavior remains testable without copying the client’s records or business identifiers.
- Inspect the final prompt and attachments. A human should confirm the data class and approved destination immediately before submission.
The UK government’s AI cyber-security code calls for checks and sanitization of data and inputs. Canadian privacy principles likewise recommend anonymized or de-identified prompt information where possible. Neither recommendation makes every transformation irreversible; assess the specific context for re-identification risk.
How do I check whether an AI coding service logs, retains, or trains on data?
Read the current terms, privacy documentation, security documentation, and administrator settings for the exact plan, model, feature, and hosting arrangement. Do not infer behavior from a consumer chat product, a vendor’s marketing summary, or another subscription tier.
Questions to answer in the service documentation
- Are prompts, completions, snippets, repository files, telemetry, and tool outputs used for model training or product improvement?
- What is the retention period for each surface, and can an administrator configure or delete it?
- Who can view or search interactions: the user, organization administrators, provider staff, support personnel, subprocessors, or model hosts?
- Does an IDE extension send different data than web chat, a command-line tool, an agent, or an issue-tracker integration?
- Where is the service hosted, and what data-processing, confidentiality, and cross-border commitments apply?
- Can the organization disable optional sharing, telemetry, indexing, connectors, or human review?
- Are model choices subject to different hosting or retention rules?
What do published service examples show?
| Service and scope | Published training statement | Important qualification |
|---|---|---|
| GitHub Copilot Business and Enterprise | GitHub states that customer data for these plans is not used to train models. | Documentation distinguishes these plans from individual-plan interaction data and notes that model-hosting details can differ. Verify the current terms and the model selected. |
| GitHub Copilot Free, Pro, and Pro+ announcement | GitHub announced that, beginning April 24, 2026, interaction data from these individual plans would be used to train and improve models unless users opted out. | The announcement said Business and Enterprise were not affected by that update. It is a dated policy statement; re-check the current setting before adoption. |
| Microsoft Copilot Chat with a work or school account | Microsoft says prompts and responses are not used to train foundation models and that the account has enterprise data protection. | Microsoft also says prompts, triggered Bing queries, and responses are logged and may be searched or audited by an IT administrator. |
These examples are not universal safety ratings. They show why the account type, feature, model host, and date must be recorded when you approve a workflow.
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Who will have access to data in the AI system?
List every party that can receive, inspect, search, or derive information from the prompt and its context. Access may include more than the developer and the model.
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- Organization administrators: Work-account interactions may be retained for administrative search, audit, or security investigation.
- Provider personnel: Support, abuse-prevention, security, or quality processes may have controlled access under the provider’s terms.
- Model hosts and subprocessors: A service may route requests to another company or a separately hosted model.
- Connected services: Search, issue tracking, source control, storage, monitoring, and deployment connectors may receive retrieved context or tool output.
- Other users: Incorrect repository permissions or shared indexes can expose material to people who should not see it.
Ask the access question for both input and output. A generated explanation can reproduce a client identifier, a proprietary algorithm, or personal information even when the original prompt was minimized. Microsoft’s Microsoft 365 documentation describes Copilot as surfacing organizational data only when the user has view permission; that illustrates why underlying permissions must be accurate and least-privilege.
How do I restrict repository context, extensions, and agent permissions?
Grant only the repository, branch, directory, and actions needed for the task. An assistant that can read an entire organization or execute tools has a larger exposure than one given a single sanitized file.
- Choose a bounded workspace. Open the approved repository or a temporary reproduction rather than a parent directory containing unrelated client projects.
- Review source-control permissions. Use a dedicated account or group where practical, remove inherited access, and confirm that private repositories are not indexed for users who lack a business need.
- Inspect extension scopes. Check what an IDE extension can read, upload, cache, or execute. Disable features that are not required for the task.
- Constrain agents. Require confirmation before file writes, shell commands, network calls, issue updates, or deployments. Avoid unattended access to production systems.
- Limit connectors. Turn off repository, ticketing, cloud-storage, and search connectors unless each is approved and its data path is documented.
- Separate environments. Keep production credentials and customer exports outside the workspace used for assistance. Use test accounts and synthetic fixtures.
- Revoke temporary access. Remove the workspace, token, connector, or exception when the task ends and record the change if policy requires it.
Least privilege is not only an infrastructure control. It limits the context that a model can retrieve automatically and reduces the chance that a broad prompt accidentally includes another client’s data.
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How should an organization document and train the workflow?
A short approved-use policy is more effective than an informal warning because developers need a decision at the moment they are about to submit data. Keep the policy next to the development workflow and update it when products, models, settings, or contracts change.
Minimum policy contents
- Allowed, restricted, and prohibited data classes, with examples of code, logs, tickets, images, and credentials.
- Approved services, account types, subscription tiers, models, features, and hosting arrangements.
- Required client and internal authorization, including who can approve exceptions.
- Sanitization requirements and a human check before submission.
- Repository, extension, connector, and agent permission boundaries.
- Retention, deletion, administrator access, subprocessors, residency, and contractual commitments.
- Review responsibilities for generated code and outputs that may contain personal or confidential information.
- Incident escalation steps, including who to contact when prohibited data is submitted.
Train for the decisions developers actually make
Use short examples: a public function with a synthetic failing test, a log containing an email address, an environment file with a token, and a work-account chat that administrators can search. Ask staff to classify each example, remove unnecessary context, select the approved feature, and explain what remains in the provider’s logs.
Audit adherence periodically and whenever a vendor changes terms, models, retention, or defaults. The UK government code emphasizes access controls, documentation of retention and failure modes, and accessible information about how data is used, accessed, and stored. OAIC guidance likewise treats governance, privacy risk, human oversight, and third-party flows as part of deciding whether a use is appropriate.
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What should I compare when choosing between AI coding tools?
Compare the complete workflow rather than a headline privacy claim. Record the result for the exact plan and feature that developers will use.
| Comparison area | Questions for the approval record |
|---|---|
| Training and improvement | Are prompts, code, completions, telemetry, or feedback used to train or improve models? Is opt-out available and effective for this account? |
| Retention and deletion | What is retained, for how long, in which product surfaces, and who can delete it? |
| Human and administrator access | Can provider staff, support teams, organization administrators, or auditors view or search interactions? |
| Subprocessors and connected services | Which model hosts, search providers, storage systems, source-control systems, or other services receive data? |
| Residency and contracts | Where is data processed and stored, and what confidentiality, data-processing, and cross-border commitments apply? |
| Permission integration | Does retrieval respect repository and document view permissions, and how are inherited permissions handled? |
| Extension and agent controls | Can you restrict directories, commands, network access, writes, connectors, and unattended execution? |
| Sharing controls | Can optional training, telemetry, indexing, human review, or synchronization be disabled and verified? |
Keep a dated copy or link to the terms and settings you relied on. Vendor policies are volatile; an approval that was accurate for one model or subscription may not apply to another.
What should I do if prohibited client data was submitted?
Treat an accidental submission as a potential data incident, even if the tool claims not to train on it. Do not attempt to hide the event or assume that deleting the chat removes every copy.
- Stop further exposure. Close the session, disable the connector or extension if necessary, and prevent the same prompt from being reused.
- Contain secrets immediately. Revoke or rotate exposed keys, tokens, passwords, certificates, and signed URLs according to your security procedure.
- Preserve facts. Record the account, service, plan, model, feature, time, data categories, recipients, and any available interaction or audit identifiers.
- Escalate internally. Notify the security, privacy, legal, and client-account owners named in your policy.
- Ask the provider precise questions. Request the applicable retention, deletion, access, subprocessor, and incident-handling details for that feature and account.
- Follow contractual and legal duties. The responsible privacy or legal owner should determine whether client notice, regulator notice, or other action is required.
- Correct the workflow. Update permissions, defaults, training, and approval records so the same path cannot silently accept the data again.
Do not describe a deletion request, opt-out, or training exclusion as complete until the provider’s documented process and your own records support that conclusion.
How can I make the workflow repeatable for every coding task?
Use a short pre-submission gate that fits into code review or issue triage:
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- Can a synthetic or de-identified reproduction answer the question?
- Is the exact service, plan, model, feature, and account approved for that class?
- Do the client agreement, internal policy, and applicable law authorize this use?
- Have identifiers, secrets, unrelated files, and production records been removed?
- Who can access, search, retain, or process the interaction and its output?
- Are repository, connector, extension, and agent permissions limited to the task?
- Has a human reviewed the generated code and checked the output for personal or confidential information?
If any answer is unknown, pause and use a safer reproduction or obtain an explicit decision from the appropriate owner. This gate turns privacy protection into a normal engineering control instead of a one-time vendor decision.
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Frequently Asked Questions
What should an AI-use clause in a client contract cover?
Specify which AI services and account tiers may process client material, which data classes are excluded, where processing may occur, how long interactions are retained, who may access them, and how deletion and incident notification work. Identify whether subcontractors or model hosts are permitted and who approves a change.
Does opting out of model training delete prompts that were already stored?
Not necessarily. Training controls, retention, and deletion are separate settings, so ask the provider what happens to previously logged interactions and use the documented deletion process for the exact feature and account.
Can generated code or explanations create a privacy problem even when my prompt was sanitized?
Yes. Outputs can reproduce personal information or confidential details retrieved from connected context or learned from the input. Review generated text, patches, logs, and comments before committing or sharing them.
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Reassess whenever the provider changes a plan, model host, feature, default setting, retention rule, or terms, and whenever a client contract or applicable law changes. A dated review owner and trigger list prevents an old approval from being treated as permanent.
Is an IDE extension covered automatically when the vendor’s chat service is approved?
No. Extensions, agents, command-line tools, and web chat can have different permissions, telemetry, connectors, and retention. Approve each surface that developers will use and test what context it actually sends.
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