There is no universally secure AI coding assistant. The right alternative depends on the exact plan, deployment, model provider, region, retention settings, repository permissions and contract. Tabnine Enterprise, Sourcegraph Cody Enterprise, Amazon Q Developer Pro and organizational Microsoft Copilot can provide documented controls for sensitive development, but each has boundaries you must verify. “Self-hosted” also does not automatically mean that inference stays inside your network.
What a secure alternative must prove
For a sensitive codebase, privacy is more than a promise that a vendor does not train on your code. An assistant may transmit prompts, retrieved files, diagnostics, telemetry, web-search queries or agent instructions to several services. A useful review therefore follows the complete data path from the developer’s editor to the model and back.
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| Control area | Question to answer before approval |
|---|---|
| Outbound content | Exactly which code, prompts, file names, metadata and generated responses leave the developer environment? |
| Recipients | Which application service, cloud region, subprocessors and LLM providers receive that content? |
| Retention and training | Is content transient, stored for a defined period, used for service improvement or used to train models? Are those rules different by plan? |
| Hosting and inference | Where does the application run, and where does the model actually run? These can be different locations. |
| Authorization | Does the assistant enforce repository, branch, tenant and user permissions before retrieving context? |
| Operations | What logs, metrics, support traces and audit events are generated, and can administrators control or export them? |
| Contract | Are no-training, deletion, residency, breach-notification and subprocessor commitments written into the applicable agreement? |
Vendor documentation establishes what a vendor claims about a configuration; it is not independent proof that the configuration satisfies your threat model. Ask your security, privacy and procurement teams to validate the current terms and settings.
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Documented alternatives and their boundaries
| Option | Documented protections | Important qualification |
|---|---|---|
| Tabnine Enterprise | Context is sent for inference and deleted after the answer; Tabnine says its models are not trained on customer code. On-premises and VPC deployment options are described for Enterprise. TLS 1.2, AES-256 at rest, password authentication and SAML2 SSO are documented for self-hosted deployment. | Operational metrics and logs may be sent to Tabnine servers for support in self-hosted deployments. Confirm telemetry settings and architecture. “No code or PII” is a vendor statement that still needs configuration validation. |
| Sourcegraph Cody Enterprise | Repository permissions are applied when Cody retrieves context. The default Anthropic integration is described as providing zero data retention for prompts and results and no model training on them. Enterprise supports bring-your-own-key (BYOK). | Self-hosted Cody sends code snippets of up to 28 KB per request to a third-party cloud service by default and needs internet access. The zero-retention terms may not apply when an administrator selects another LLM API. |
| Amazon Q Developer Pro | AWS says Pro does not use customer content for service improvement. | That statement does not apply to the Free Tier. AWS says Free Tier may use some questions and generated code for service improvement, with opt-out steps depending on the environment. Suggestions can still contain security issues or closely match public code. |
| Organizational Microsoft Copilot and Copilot Chat | Microsoft describes tenant isolation, encryption, identity and permission inheritance, sensitivity labels, retention, audit and administrative controls. Prompts, responses and Microsoft Graph data are described as not being used to train foundation models. | Controls vary by subscription. Web-search queries follow separate handling through Bing, and individual agents may have additional privacy terms. This documentation is not a statement about GitHub Copilot. |
| GitHub Copilot Free, Pro and Pro+ | Not a default recommendation for sensitive repositories under the announced consumer-plan policy. | GitHub announced on March 25, 2026 that, starting April 24, 2026, interaction data would be used to train and improve models unless users opted out. Business and Enterprise users were excluded from that update. Check the current account setting and terms rather than relying on the announcement. |
Tabnine Enterprise: private deployment with a telemetry question
How context is handled
Tabnine’s privacy documentation says the client sends context to a Tabnine cluster for inference, then the context is deleted after the answer and is not retained beyond the inference window. It says code-completion and Protected chat models are trained on permissively licensed open-source code rather than customer code, and that Tabnine uses no third-party APIs for those models.
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For Enterprise, Tabnine describes private deployment choices that include on-premises and VPC installations. Those choices can reduce the number of external services in the inference path, but the offered architecture matters: obtain a diagram showing the client, control plane, model service, update channel and support connections for your edition.
Security and identity controls
The security documentation describes TLS 1.2 for traffic between client and server, AES-256 encryption for stored data, built-in password authentication and SAML2 single sign-on for self-hosted deployment. Treat these as available controls, not evidence that your instance is configured correctly. Require your administrators to enforce SSO, least-privilege groups, certificate management, network segmentation and key rotation.
Air-gapped and self-hosted caveat
Tabnine says an air-gapped deployment can send metrics to Prometheus and logs to the customer’s aggregator. For self-hosted deployments, it says operational metrics and logs may be sent to Tabnine servers for support while no code or personally identifiable information is sent in those cases. Ask for the exact telemetry payload, destination, retention period and disablement procedure. A genuinely disconnected environment should be tested at the firewall and DNS layers, not inferred from a product label.
Sourcegraph Cody Enterprise: permission-aware retrieval, but not automatically offline
The documented data path
Sourcegraph describes Cody as a client connected to the organization’s Sourcegraph instance and an LLM API. The instance can be self-hosted inside the organization’s network or run in a dedicated single-tenant Sourcegraph Cloud environment. Cody retrieves code context subject to repository permissions, then sends a prompt containing the user request and relevant context through the instance to the LLM API.
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The default Anthropic integration for Cody Enterprise is described as having zero data retention for prompts and results and not using them to train models. Those assurances are tied to that integration. If an administrator configures a different LLM API, the provider’s terms must be checked separately; do not carry Anthropic’s terms over to another provider.
Why self-hosted does not equal on-premises inference
Sourcegraph’s FAQ says self-hosted Cody sends code snippets of up to 28 KB per request to a third-party cloud service by default and requires internet access for those interactions. Thus, the Sourcegraph application can be inside your network while model inference or an intermediary service remains outside it. Enterprise BYOK may change the provider relationship, but it does not by itself prove that the model is running locally. Confirm whether your chosen configuration supports private connectivity, regional processing, customer-managed keys and a fully disconnected mode.
Questions for a Cody pilot
- Which repositories and branches can each user retrieve through Cody?
- What exact context-size and file-exclusion rules are applied before a request leaves the instance?
- Which LLM endpoint receives requests, in which region, under which retention and training terms?
- What happens to prompts and results in Sourcegraph logs, provider logs and support cases?
- Can network controls demonstrate that no unapproved endpoint is contacted?
Amazon Q Developer: choose Pro deliberately
Pro versus Free Tier
AWS states that Amazon Q Developer Pro does not use customer content for service improvement. AWS separately says the Free Tier might use certain content, including questions and generated code, for service improvement; opt-out steps depend on the environment. Do not describe “Amazon Q” generally as no-training. Put the tier, account configuration and AWS region beside every privacy decision.
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AWS warns that Q can produce code closely matching public code on rare occasions and provides a reference tracker when it detects a match. AWS also says security vulnerabilities are filtered where possible but cannot be completely ruled out in suggestions. Require normal code review, dependency scanning, secret detection, license review and tests; a no-training policy does not make generated code safe.
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Deployment checks
- Confirm that the organization is using Pro accounts rather than Free Tier identities.
- Verify regional availability and the region in which requests and logs are processed.
- Review AWS identity policies, administrator controls, audit events and retention settings.
- Define how the team handles a public-code match or a suggestion that introduces a vulnerability.
Microsoft Copilot and Copilot Chat: strong enterprise controls with clear boundaries
What organizational protection covers
Microsoft says organizational use of Microsoft Copilot and Copilot Chat is covered by its Data Protection Addendum and Product Terms, with Microsoft acting as processor. It describes encryption, tenant isolation and inheritance of identity, permissions, sensitivity labels, retention policies, auditing and administrative settings. Microsoft also says prompts, responses and Microsoft Graph data are not used to train its foundation models.
These controls vary with subscription. Your approval record should name the subscription, tenant configuration, enabled features and applicable contract rather than citing “Microsoft Copilot” as one uniform service.
Web search and agents are separate paths
When Copilot uses web search, Microsoft describes Bing as handling those queries under separate practices, with Microsoft acting as an independent data controller. Microsoft says those queries are not used to train its foundation large language models, but the query still leaves the tenant through the web-search path. Decide whether web search is allowed for confidential projects and apply a policy that blocks it where necessary.
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Agents can introduce their own connectors, instructions and privacy terms. Review each agent’s data sources, permissions, logging and retention before enabling it for a sensitive repository. Do not transfer Microsoft 365 Copilot assurances to GitHub Copilot; they are distinct products.
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GitHub Copilot policy: a plan-specific warning
GitHub announced on March 25, 2026 that, beginning April 24, 2026, interaction data from Copilot Free, Pro and Pro+—including inputs, outputs, code snippets and associated context—would be used to train and improve AI models unless users opted out. GitHub said Copilot Business and Enterprise users were not affected by that update and that a prior opt-out preference would be retained.
The announcement distinguishes private-repository content actively processed by the service from private content merely stored at rest. Because account policies and settings can change, inspect the current organization or user setting, applicable terms, retention behavior and plan before allowing a repository to be used. A consumer-plan opt-out is not a substitute for an enterprise contract and technical enforcement.
Self-hosted, private cloud and on-premises are different
Self-hosted application
The application or control plane runs on infrastructure you operate. This can improve identity integration, network control and auditability, but the application may still call a hosted model or support service.
Self-hosted inference
The model serving the request runs on infrastructure you operate, with no unapproved external inference endpoint. This is a stricter requirement and must be confirmed separately. A self-hosted application that sends snippets to a cloud service is not self-hosted inference.
Air-gapped operation
An air-gapped design blocks external network paths altogether. Verify update delivery, license checks, telemetry, support channels, identity dependencies and model downloads. A vendor’s statement that no code is sent does not establish that the system can operate without internet access.
A pre-approval verification process
- Classify the repository. Mark secrets, regulated data, customer information, export-controlled code, unreleased intellectual property and third-party licensed material. Define which classes may be sent to an assistant, if any.
- Draw the request path. Include the editor extension, local index, application host, retrieval service, model endpoint, web-search service, telemetry collector, support channel and log store.
- Identify every provider. Record the vendor, LLM provider, cloud region, subprocessors and whether BYOK changes any recipient or contract.
- Separate processing from training. Ask whether content is retained transiently, stored in logs, reviewed for abuse, used for service improvement or used to train a model. Obtain durations and deletion triggers for each location.
- Verify plan and contract. Capture the exact edition, tenant, order form, Data Processing Addendum, product terms, subprocessor list, residency promise and breach-notification obligations.
- Test authorization. Use test accounts with different repository, branch and file permissions. Confirm that retrieval and generated answers cannot expose inaccessible content.
- Inspect telemetry. Collect outbound DNS and firewall records, then inspect metrics, traces, crash reports and support bundles for code, prompts, file paths or personal data.
- Set technical guardrails. Exclude secret files, generated artifacts and high-risk directories; restrict extensions to managed devices; disable web search or agents where policy requires; enforce SSO and least privilege.
- Run a controlled pilot. Use synthetic or sanitized code, canary secrets and intentionally restricted repositories. Record every endpoint contacted and compare observed behavior with the vendor’s diagram.
- Define exit and incident actions. Document how to revoke tokens, disable the extension, delete indexes and logs, notify the vendor, investigate exposure and preserve audit evidence.
Decision patterns for different sensitivity levels
Highly restricted or regulated code
Prefer a configuration with demonstrable private inference or a formally approved private network path, contractual no-training and deletion commitments, disabled web search, controlled telemetry and tested repository authorization. If a product cannot provide a complete data-flow answer, keep it outside this class.
Confidential commercial code
An enterprise plan may be acceptable when the provider, region, retention, training status, identity integration and support access are documented and monitored. Use allow-listed repositories and sanitize prompts rather than granting unrestricted workspace access.
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Consumer or free plans may be operationally reasonable, but review public-code matching, licensing, vulnerability and account-policy implications. Do not let a low-risk project establish a permissive default for sensitive repositories.
Common approval mistakes and fixes
- Mistake: “No training” means no exposure. Fix: separately document transient processing, logs, telemetry, support access and subprocessors.
- Mistake: treating self-hosted as offline. Fix: prove where inference runs and test network traffic; Cody’s documented 28 KB cloud transfer is a concrete example.
- Mistake: applying one plan’s policy to another. Fix: record whether the deployment is Tabnine Enterprise, Cody Enterprise, Amazon Q Pro, a Free Tier account or a particular Microsoft subscription.
- Mistake: ignoring web search and agents. Fix: review their separate providers, contracts, permissions and retention before enabling them.
- Mistake: assuming generated code is secure or licensable. Fix: keep human review, automated security tests, dependency checks and license procedures mandatory.
- Mistake: accepting marketing language as an audit. Fix: request current architecture, terms and settings, then validate them with logs, firewall controls and a pilot.
What to record in the approval file
Keep a dated record containing the product and plan, deployment topology, model and provider, regions, data categories, retention and training settings, identity and repository controls, telemetry destinations, web-search and agent status, contractual documents, pilot evidence, approved repositories, responsible owner and review date. Recheck after plan changes, provider changes, policy updates or a material architecture change.
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