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There is no universally secure AI coding assistant. The most defensible choices are plan- and configuration-specific: Tabnine Enterprise documents on-premises and VPC deployment with ephemeral code context; Sourcegraph Cody can run with a self-hosted application but still sends up to 28 KB of snippets per request to a cloud service by default; Amazon Q Developer Pro, organizational Microsoft Copilot, and some enterprise GitHub Copilot plans document protections that differ from their free or individual tiers. Treat each statement as a vendor claim to verify against your threat model, data flows, provider contracts, region, retention settings, and audit requirements.
What “secure” must mean for a sensitive codebase
Sensitive repositories can contain proprietary algorithms, credentials, personal data, regulated records, export-controlled material, or code whose disclosure creates a safety or competitive risk. An assistant is acceptable only when its complete processing path fits the repository’s classification and your organization’s controls. A marketing statement such as “private,” “enterprise,” or “self-hosted” is not a sufficient decision.
Evaluate seven separate questions:
- Outbound content: Which prompt text, code spans, file names, metadata, embeddings, diagnostics, and telemetry leave the developer environment?
- Recipients: Does the application send data to the vendor, an infrastructure provider, an external LLM API, a web-search service, or an agent-specific service?
- Retention and training: Is content processed transiently, stored for a stated period, used for service improvement, or used to train a model? These are different activities.
- Execution location: Where does the client, application, retrieval system, and model inference run? Application hosting and inference hosting are not the same.
- Authorization: Does retrieval enforce repository, branch, tenant, and user permissions, or can an assistant read more than the user should?
- Operations: What is logged, where are logs sent, who can access them, and can security teams audit prompts, responses, administrator actions, and configuration changes?
- Contractual coverage: Which subscription, data-processing addendum, product terms, region, and support arrangement actually cover your use?
Use the exact plan name in every approval record. A statement about a paid enterprise tier cannot be safely generalized to a free tier or a different product.
At-a-glance comparison
| Option | Documented data path | Training and retention claims | Hosting and deployment facts | Important qualification |
|---|---|---|---|---|
| Tabnine Enterprise | The client sends coding context to a Tabnine cluster for inference. | Tabnine says context is deleted after the answer, is not retained beyond the inference window, and its completion and Protected chat models are not trained on customer code. | Private deployment options include on-premises and VPC. Tabnine says its models use permissively licensed open-source code and that it uses no third-party APIs for those models. | Self-hosted operations may send metrics and logs to Tabnine for support. Air-gapped deployments can send metrics to Prometheus and logs to the customer’s aggregator. Confirm the exact telemetry configuration. |
| Sourcegraph Cody Enterprise | Cody retrieves permitted context through a Sourcegraph instance, then sends the prompt and context to an LLM API. A self-hosted instance can still send snippets to a third-party cloud service. | The default Anthropic integration is described as zero data retention for prompts and results and no model training. | The Sourcegraph instance may be self-hosted or run in a dedicated single-tenant cloud environment. Enterprise supports bring-your-own-key (BYOK). | The FAQ says self-hosted Cody sends up to 28 KB of code snippets per request to a cloud service by default and needs internet access. Different LLM APIs may have different terms. |
| Amazon Q Developer Pro | Requests and generated code are processed through AWS services under the selected account, region, and configuration. | AWS says Pro does not use customer content for service improvement. The Free Tier may use some content, including questions and generated code, for improvement. | Availability, identity integration, logging, and regional behavior depend on AWS configuration and current service support. | Rare suggestions can closely match public code. AWS says vulnerabilities are filtered where possible but cannot be completely ruled out. |
| Organizational Microsoft Copilot and Copilot Chat | Prompts, responses, and Microsoft Graph data are handled under the organization’s tenant controls and Microsoft’s documented service architecture. | Microsoft says prompts, responses, and Graph data are not used to train foundation models for organizational use. | Microsoft documents encryption, tenant isolation, identity and permission inheritance, sensitivity labels, retention, audit, and administrative controls. | Controls vary by subscription. Web-search queries go through Bing under separate handling, and agent privacy terms must be checked individually. This is distinct from GitHub Copilot. |
| GitHub Copilot | Inputs, outputs, code snippets, and associated context are processed according to the account’s plan and current settings. | GitHub announced on March 25, 2026 that, from April 24, 2026, Copilot Free, Pro, and Pro+ interaction data would be used for model improvement unless users opted out. Business and Enterprise users were excluded from that update. | Repository access, organization policy, and account settings determine the practical boundary. | This is a dated, changeable policy. Check the current settings and terms before approval; do not infer that every GitHub Copilot plan has the same treatment. |
Tabnine: private deployment with an operations caveat
What leaves the developer environment
Tabnine’s privacy documentation says the client sends the context needed for a completion or chat answer to a Tabnine cluster. It says that context is deleted after the answer and is not retained beyond the inference window. Tabnine also says its code-completion and Protected chat models are trained on permissively licensed open-source code rather than customer code, and that it uses no third-party APIs for its models.
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For Enterprise, Tabnine describes private deployment options that include on-premises and virtual private cloud (VPC) installations. This can reduce exposure to a shared public service, but “private deployment” does not by itself prove that every operational connection is internal.
Telemetry and security controls to verify
Tabnine says an air-gapped deployment can send metrics to Prometheus and logs to the customer’s log aggregator. It also says self-hosted deployments may send operational metrics and logs to Tabnine servers for support, while no code or personally identifiable information is sent in those cases. Ask for an architecture diagram showing every endpoint, the default and disabled telemetry paths, firewall requirements, and the support process when outbound access is blocked.
Its security documentation describes TLS 1.2 for client-server traffic, AES-256 encryption for stored data, built-in password authentication, and SAML 2 SSO for self-hosted deployment. Those are documented capabilities, not evidence that your tenant is configured correctly. Require administrators to enforce SSO, least privilege, key rotation, network restrictions, and centralized logging before production access.
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How the Cody path works
Sourcegraph describes a Cody deployment with a client, a Sourcegraph instance, and an LLM API. The instance retrieves code context subject to repository permissions. It then sends a prompt containing the user request and relevant context through the instance to the configured LLM API.
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The Sourcegraph instance may be self-hosted inside the organization’s network or operated in a dedicated single-tenant Sourcegraph Cloud environment. That choice controls where the application and retrieval layer run; it does not automatically determine where model inference runs.
Default cloud transfer
Sourcegraph’s FAQ states that self-hosted Cody sends code snippets of up to 28 KB per request to a third-party cloud service by default and that the self-hosted Sourcegraph instance needs internet access for those interactions. Therefore, do not approve “self-hosted Cody” as a fully offline or fully on-premises solution without confirming a specific supported inference configuration.
Retention, provider, and BYOK
The security whitepaper describes the default Anthropic integration for Cody Enterprise as providing 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 another LLM API, obtain that provider’s retention, training, region, subprocessors, and contractual terms separately. Enterprise supports BYOK, which can give the organization more control over the provider relationship but does not remove the need to validate the provider’s behavior.
Questions for a Cody proof of concept
- Which LLM API receives each request in the proposed region and environment?
- Can network controls prevent unintended third-party endpoints while preserving required functionality?
- What is the maximum context sent for completions, chat, code search, and agents?
- Are repository permissions re-evaluated at retrieval time for every user?
- Which prompts, results, traces, and administrator events are retained in Sourcegraph, the LLM provider, and support systems?
Amazon Q Developer: choose Pro deliberately
Pro and Free Tier are different privacy decisions
AWS states that Amazon Q Developer Pro and Amazon Q Business do not use customer content for service improvement. AWS also says the Free Tier might use certain content, including questions and generated code, for service improvement, with opt-out steps depending on the environment. An organization handling sensitive code should document the exact tier, account structure, opt-out status where applicable, and region before enabling an IDE extension or command-line integration.
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Code provenance and security limitations
AWS says Amazon Q can, on rare occasions, produce code closely matching public code and provides a reference tracker when it detects a match. It also says security vulnerabilities are filtered where possible but cannot be completely ruled out in suggestions. Keep normal code review, dependency scanning, license review, secret scanning, and security testing in place; an assistant’s privacy controls do not turn generated code into trusted code.
Approval checks
- Confirm that the account and organization policies prevent developers from silently switching to the Free Tier.
- Record the AWS region and verify service availability and applicable terms there.
- Define whether generated code may enter production without human review and automated testing.
- Review CloudTrail or equivalent administrative records and set retention according to your incident-response requirements.
Microsoft Copilot and Copilot Chat: strong tenant controls with clear boundaries
Documented organizational protections
Microsoft says organizational use of Microsoft Copilot and Copilot Chat is covered by its Data Protection Addendum and Product Terms, with Microsoft acting as a processor. It documents encryption, tenant isolation, and inheritance of identity, permissions, sensitivity labels, retention policies, audit, and administrative settings. Microsoft also says prompts, responses, and Microsoft Graph data are not used to train foundation models in this organizational context.
Subscription and web-search boundaries
Microsoft explicitly warns that controls vary by subscription. Obtain the documentation for the precise license, tenant configuration, and enabled features rather than relying on a general Copilot description.
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Web-search queries are a separate data path. Microsoft describes Bing as an independent data controller for those queries and says they are not used to train its foundation large language models. Decide whether web search is allowed for sensitive work, restrict it where necessary, and verify what query, user, and diagnostic information is retained.
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Agents need individual review
Do not assume an agent inherits every control of the base Copilot service. Review each agent’s privacy terms, connectors, external APIs, storage, permissions, and logging. Microsoft 365 documentation also should not be conflated with GitHub Copilot, which is a distinct product with separate policies.
GitHub Copilot: policy changes make plan selection essential
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 Copilot Enterprise users were not affected by that update and that an existing opt-out preference would be retained.
The announcement distinguishes private-repository content actively processed by Copilot from content merely stored in a private repository. That distinction matters when writing policy: a repository’s visibility setting alone does not answer what data is sent during an interaction.
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Because the announcement is dated and policies can change, check the current account-level and organization-level settings, applicable terms, retention statements, and administrator controls at the time of approval. Block individual plans for sensitive repositories if your organization cannot centrally enforce the required settings.
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Self-hosting: questions that prevent a false sense of isolation
“Can I self-host an AI coding assistant?”
Yes, some offerings document self-hosted application or private deployment options. Tabnine describes on-premises and VPC Enterprise deployments. Sourcegraph describes self-hosting its Sourcegraph instance and supports BYOK for Enterprise. These options can place the user interface, policy layer, retrieval system, or gateway under organizational control.
“Does self-hosted mean my code stays on-premises?”
No. The Cody FAQ is a concrete counterexample: a self-hosted Sourcegraph instance sends up to 28 KB of snippets per request to a third-party cloud service by default and requires internet access. To establish true on-premises inference, the vendor must document that the model, serving stack, prompts, results, and required telemetry all remain within approved network boundaries. If any component calls an external provider, treat that provider as part of the data-processing environment.
What to demand from a self-hosted design
- A component diagram with clients, gateways, retrieval stores, model servers, support endpoints, and telemetry collectors.
- Firewall rules and DNS names for every required connection, including update and licensing services.
- A switch or documented procedure to disable outbound diagnostics and support telemetry.
- Evidence that model downloads, container images, logs, and backups follow your residency and malware-scanning requirements.
- Identity integration, repository-level authorization, administrator separation, and an auditable change history.
A verification workflow before approving any assistant
- Classify the repository. Label secrets, personal data, regulated material, export-controlled code, customer data, and ordinary proprietary code separately. Set the maximum data class the assistant may process.
- Freeze the exact offer. Record product, plan, add-ons, deployment model, region, model provider, and contract documents. A trial, free tier, or developer account is not equivalent to an enterprise subscription.
- Map the request. Capture what the IDE extension, CLI, chat panel, code indexer, agent, and web-search feature send. Include file names, surrounding lines, terminal output, diagnostics, embeddings, and user identifiers.
- Validate provider terms. Obtain written answers on training, service improvement, transient processing, retention, backups, subprocessors, support access, legal requests, and deletion. Get separate answers for every LLM API in the path.
- Test authorization. Use test repositories to verify that a user cannot retrieve another team’s private code, a deleted branch, or content outside the assigned organization. Test revoked access and inherited permissions.
- Inspect telemetry. Confirm whether prompts, snippets, generated code, errors, usage metrics, and administrator actions enter vendor systems. Route permitted logs to your SIEM and set retention; disable prohibited telemetry.
- Check geography and resilience. Verify processing and backup regions, cross-border transfers, disaster-recovery copies, and subprocessors against legal and contractual requirements.
- Run adversarial tests. Try prompt injection in documentation, malicious repository instructions, secret exfiltration attempts, oversized context, and requests for unauthorized files. Record what the assistant returns and what appears in logs.
- Define human controls. Require review, tests, dependency and license checks, secret scanning, and secure coding analysis for generated changes. Privacy approval does not replace software assurance.
- Document residual risk. Name the accepted data paths, excluded repositories, owners, incident contacts, revocation procedure, and reassessment date. Recheck after plan, model, region, or policy changes.
Common approval mistakes and their fixes
| Mistake | Why it fails | Better control |
|---|---|---|
| “It is self-hosted, so nothing leaves our network.” | Application hosting may be internal while inference or support telemetry is external. | Trace every request and require documented internal inference if that is a hard requirement. |
| “The vendor does not train on customer code.” | Transient processing, retention, support access, logs, and third-party providers remain unanswered. | Ask separate questions for training, service improvement, retention, backups, telemetry, and subprocessors. |
| “Private repository means private processing.” | Active prompts and snippets can still be transmitted during an interaction. | Review the plan’s interaction-data policy and enforce organization settings. |
| “Enterprise documentation covers every subscription.” | Free, Pro, Business, Enterprise, and regional offers can have different terms. | Put the exact SKU and region in the security record and block unapproved tiers. |
| “No training means no security risk.” | Generated code can contain vulnerabilities or resemble public code. | Keep code review, testing, scanning, and license review mandatory. |
| “One privacy policy covers web search and agents.” | Search providers and agent connectors can have separate controllers and terms. | Disable unnecessary features and assess each data path independently. |
Practical selection by threat model
Strict network isolation
Start with a requirement for documented on-premises inference, not merely a self-hosted application. Reject any design that requires an unapproved cloud LLM or support endpoint. Tabnine’s private deployment description may be a candidate, but confirm whether the offered architecture can operate with your telemetry and air-gap rules. Cody’s default cloud transfer means its standard self-hosted behavior does not meet a fully offline requirement.
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Cloud use allowed with contractual safeguards
Consider an enterprise plan whose provider, region, retention, and training terms are explicitly approved. Amazon Q Developer Pro, organizational Microsoft Copilot, and Cody’s documented default Anthropic integration each make specific claims, but they still require account configuration, contract review, and monitoring.
Centralized repository governance
Favor a service that integrates with your identity provider, repository permissions, audit system, and organization policy. Test revocation and cross-repository isolation rather than accepting a feature list. Keep individual or free plans outside sensitive repositories when settings cannot be centrally enforced.
Mixed-sensitivity development
Use repository and workspace allowlists. Permit assistants for low-risk projects, prohibit them for restricted projects, and prevent accidental context sharing through shared terminals, copied logs, or broad workspace indexing. Train developers to remove secrets and personal data before submitting a prompt.
Operational controls after approval
- Use single sign-on and multi-factor authentication, with automatic deprovisioning when employment or project access ends.
- Limit administrator, support, and model-provider access; separate duties for policy changes and incident review.
- Monitor outbound domains, unusual request volume, large context transfers, and access to repositories outside a user’s normal pattern.
- Keep a current inventory of enabled models, providers, regions, connectors, agents, and extensions.
- Reassess when a vendor changes training policy, adds a model provider, changes retention, introduces web search, or alters a plan’s terms.
- Prepare an incident procedure that can revoke tokens, disable the integration, preserve logs, notify affected owners, and request deletion where contractually available.
Bottom line for security teams
The strongest documented fit depends on the requirement. Tabnine provides the clearest description of ephemeral customer context and private deployment choices, with telemetry that must be configured and verified. Cody offers a controllable application and provider options, but its documented default sends up to 28 KB of snippets per request to a cloud service, so self-hosting is not equivalent to offline inference. Amazon Q Developer Pro, organizational Microsoft Copilot, and enterprise GitHub Copilot policies can provide meaningful protections when the exact tier and settings are enforced. None of these claims substitutes for a data-flow review, provider contract, regional check, permission test, and written approval for the specific repository class.
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