As AI coding assistants become a pivotal part of modern development workflows, questions surrounding their handling of sensitive code, intellectual property, and internal data have become increasingly important. Developers are keen to know how these platforms handle sensitive code, intellectual property, and internal data, what practices they follow to protect privacy, and how they ensure compliance with industry standards.
In this article, we take a deep dive into the leading AI coding assistants in the market, compare their approaches to privacy and security, and summarize the best practices that every platform should follow. We conclude by explaining how CodeVista’s privacy and security protocols set us apart in meeting enterprise-grade expectations.
1. Training Data Sources: What datasets were used to train the AI models? Do they exclusively consist of open-source code, or could they include sensitive or licensed content?
2. Open-Source Models & Licensing: For platforms using open-source AI models, are they license-compliant, and do they allow for commercial use? How do they handle restrictive licenses like GPL?
3. Use of User Code for Training: Does the platform use customers' code (such as from private repositories) to train or fine-tune its models? Can users opt out of this practice?
4. Code Storage & Retention: Where is the code processed and stored? Is it sent to the cloud, cached temporarily, or stored long-term? Are there privacy modes or self-hosted options available?
1. GitHub Copilot
GitHub Copilot uses OpenAI's Codex/GPT models, trained on publicly available GitHub repositories. These datasets may contain both permissive and restrictive licenses (e.g., MIT, Apache, GPL). To mitigate the risks associated with license violations, Copilot employs a “Public Code Filter” that ensures suggestions do not replicate copyrighted content. For business customers, Copilot guarantees that user code and prompts are not used for training, and prompts are discarded immediately. Meanwhile, individual users must opt in for their data to be used for model improvements. GitHub’s Copilot is hosted on Microsoft Azure and complies with SOC 2 and GDPR regulations, although it does not support on-premise deployments.
2. Tabnine
Tabnine’s proprietary models are trained exclusively on open-source code licensed under permissive terms (e.g., MIT, Apache, BSD) and excludes GPL-licensed content. It does not use user code for training. For enterprise customers, Tabnine offers the option to fine-tune models on private codebases, ensuring isolation from other users' data. All code suggestions are processed in-memory, with no data retention by default. Tabnine is SOC 2 and GDPR-compliant, offering SaaS, VPC, and on-premise deployment options.
3. Cursor
Cursor integrates AI models from Fireworks.ai, OpenAI, and Anthropic. By default, user prompts and code are collected to improve model performance unless Privacy Mode is enabled. When Privacy Mode is activated, Cursor guarantees no data retention. The codebases indexed by Cursor are transformed into non-reconstructable embeddings stored in the cloud. Although Cursor does not support on-premise deployment, it meets SOC 2 Type II compliance and allows users to select external models with zero-retention options.
4. Qodo (formerly CodiumAI)
Qodo specializes in test generation and AI-driven code reviews. For free users, data may be used to improve models unless they opt out. For paid and trial users, Qodo guarantees that their data will never be used for training, underpinned by indemnity clauses. Data is temporarily retained for no more than 48 hours and then deleted. All external LLM interactions are configured with zero-retention modes. Qodo supports self-hosted deployments (e.g., with "Qodo Merge") and complies with SOC 2, GDPR, and CCPA regulations.
5. Windsurf (Codeium)
Windsurf (formerly Codeium) trains its proprietary models using filtered public code, removing content under restrictive licenses. The platform implements license filtering and fuzzy-matching to block verbatim or similar suggestions from licensed code. For teams and enterprises, zero-data retention is enabled by default, ensuring that no data is stored or used for further training. Free users may have logs collected, but these are not used for model training unless they opt in. Windsurf supports both cloud and on-premise deployments, and is aligned with SOC 2 and GDPR.
6. Augment Code
Augment trains its models exclusively on opt-in open-source projects. User code is never used for model training, regardless of subscription tier. The platform employs a Proof-of-Possession architecture, ensuring that AI can only access files that users have explicitly opened, with hashed verification to maintain privacy. Code is not exposed to unauthorized parties, and employees can access logs only with time-limited permissions. Augment stores only embeddings (not raw code) for performance, and it complies with SOC 2 Type II, GDPR, and CCPA regulations.
✅ Industry Best Practices:
1. Train Only on License-Safe, Public Data: Leading platforms rely on permissively licensed open-source data (MIT, Apache, BSD), avoiding restrictive licenses such as GPL.
2. Never Train on User Code Without Consent: Most platforms do not use user code for training unless users explicitly opt in. For enterprise users, this is typically the default.
3. Offer Zero-Data Retention or Privacy Mode: Platforms such as Cursor, Tabnine, and Codeium provide privacy or zero-retention modes, ensuring ephemeral processing of code without storing it.
4. Ensure Encrypted, In-Memory Processing: All code context is processed in-memory and transmitted over encrypted channels (TLS). Raw code is not stored unless explicitly permitted.
5. Provide Flexible Deployment Options: Top-tier tools support SaaS, VPC, and on-premise deployments, offering enterprises full control over data residency, security, and compliance.
6. Maintain Transparency and Compliance: SOC 2, GDPR, and CCPA certifications, along with clear data-handling documentation, have become industry standards in AI code assistants.
🛡️ What CodeVista Does to Ensure User Privacy
At CodeVista, we recognize that privacy and security are paramount for our users. Here’s how we align with industry best practices while offering our unique, enterprise-grade security features:
7. Trusted Model Selection with License-Safe Foundations: CodeVista operates on top of leading LLM providers, all of whom use permissively licensed open-source code. We do not train models using customer code. Users can select the LLM backend they trust, ensuring transparency, security, and compliance.
8. No Training on Customer Code (By Default): By default, user codebases and prompts are never used for training or fine-tuning. Any personalization of models is opt-in, fully isolated, and transparently documented.
9. Zero Data Retention Mode (Privacy-First by Default): CodeVista offers Zero Data Retention Mode as the default for all enterprise users. All code is processed in-memory and discarded immediately—nothing is stored, cached, or logged.
10. In-Memory, Encrypted Processing Only: Every interaction is encrypted using TLS. We ensure that no raw code is ever written to disk unless explicitly permitted by the user. All suggestions are generated in real-time and processed securely in memory.
11. Flexible Deployment Options: CodeVista supports a range of deployment options, including SaaS, dedicated VPC, and on-premise deployments, enabling full control over infrastructure, compliance, and data governance.
12. Transparency and Compliance: CodeVista is designed for SOC 2 readiness and fully complies with GDPR and CCPA. We offer transparent documentation of data flows, grant full ownership of data to users, and provide options to delete, export, and audit data at any time.
With CodeVista, you can trust that your code and data are always handled with the highest level of privacy, security, and compliance.