Assessing the Safety of GitHub Copilot in a Work Environment

GitHub Copilot functions as an AI-powered code suggestion tool that assists developers by predicting and suggesting code snippets, lines, or even entire functions based on the context of the code being written. It is trained on a massive dataset of publicly available code. While it can significantly boost productivity, its use in a professional setting raises valid concerns regarding safety, privacy, intellectual property, and security.

Key Concerns Regarding Copilot Use at Work

Several primary concerns are often discussed when evaluating the suitability and safety of GitHub Copilot for use within a company:

• Code Privacy and Confidentiality: The risk that sensitive internal code or data might be transmitted to external servers or inadvertently included in training data or suggestions for other users.
• Intellectual Property (IP) Ownership: Questions about who owns the code generated by Copilot, especially if it closely resembles code from its training data (which includes open-source projects with various licenses).
• Security Vulnerabilities: The potential for Copilot to suggest insecure code patterns or introduce bugs that could lead to security flaws in the final application.
• Licensing Compliance: Concerns about whether Copilot might generate code derived from copyleft licenses (like GPL) that could impose unwanted obligations on proprietary projects.
• Company Policy and Compliance: Ensuring that the use of Copilot aligns with the company's internal policies on code development, security, IP, and the use of third-party tools.

Addressing Privacy and Confidentiality

GitHub and Microsoft have provided clarity on how data is handled, particularly differentiating between personal/free accounts and business/enterprise offerings.

• Data Transmission: Copilot sends snippets of the code being written (the context) to its servers to generate suggestions.
• Telemetry Data: Information about usage, such as prompts and generated suggestions, is collected.
• Business/Enterprise Accounts: For organizations using GitHub Copilot Business or Enterprise, specific contractual terms often provide stronger data privacy assurances. These typically state that code snippets and suggestions are not used to train future models or shared with other users.
• Opt-out Options: Users (or administrators in business/enterprise accounts) can often configure telemetry settings, including opting out of sharing code snippets for model training purposes.

Insight: Using GitHub Copilot Business or Enterprise with appropriate configuration is generally considered safer for internal code confidentiality than using a personal account.

Navigating Intellectual Property and Ownership

The ownership of code generated by AI is a complex and evolving legal area. However, GitHub's stance is typically that the code output by Copilot belongs to the user (the developer or the company).

• Training Data Influence: While the output is owned by the user, Copilot is trained on public code. There is a possibility it might generate code that is very similar or identical to existing code in its training set.
• Potential for Copyleft Code: This similarity raises concerns, especially if the generated code is highly similar to code licensed under restrictive copyleft licenses (like GPL), which could potentially obligate the user to open-source their own code.
• Lack of Attribution: Copilot does not provide attribution to the original source code it might have derived suggestions from.

Insight: While the generated code is generally owned by the user, the risk of inadvertently incorporating code similar to licensed material (particularly copyleft) exists. Standard development practices like code review remain essential.

Evaluating Security Implications

AI models are trained on existing code, which includes code that may contain security vulnerabilities. Copilot can potentially suggest code patterns that are insecure.

• Potential for Vulnerabilities: Copoler might suggest outdated libraries, insecure API usage, or common coding errors that lead to vulnerabilities (e.g., SQL injection risks, insecure deserialization).
• Not a Security Auditor: Copilot is a code suggester, not a security analysis tool. It does not inherently understand or flag security flaws in the context it's operating within or the code it suggests.

Insight: Code generated by Copilot should be treated like any other code written by a developer – it requires thorough review, testing, and static/dynamic analysis to identify and mitigate security risks. Relying solely on Copilot without validation increases security exposure.

Understanding Licensing Compliance

A significant concern is the potential for Copilot to generate code that is substantially similar to code under restrictive open-source licenses, potentially creating compliance issues.

• Training Data Includes Various Licenses: Copilot is trained on a wide range of public code, including projects under permissive (MIT, Apache) and restrictive (GPL, AGPL) licenses.
• Similarity to Training Data: Studies and anecdotal evidence suggest Copilot can sometimes generate code snippets that are very close to or identical to code found in its training data.
• Risk for Proprietary Projects: If code substantially similar to GPL-licensed code is incorporated into a proprietary project, it could create obligations under the GPL license.

Insight: Companies working on proprietary or permissively licensed projects must be aware of the potential for Copilot to suggest copylefted code. While GitHub has implemented features like similarity detection flags (which may not be foolproof), robust code review and potentially licensing analysis tools are crucial.

Adhering to Company Policy and Establishing Guidelines

The safest approach to using GitHub Copilot at work involves clear organizational policies and developer education.

• Company Approval: Using any third-party tool that interacts with internal codebases, especially one involving external servers and AI, typically requires explicit approval from management, legal, and security teams.
• Internal Guidelines: Organizations should establish clear guidelines on how developers are permitted to use Copilot, including:
  • Which accounts are authorized (personal vs. business/enterprise).
  • Handling sensitive information in prompts.
  • Mandatory code review processes for Copilot-generated code.
  • Integration with existing security and compliance workflows.
• Developer Training: Educating developers on the capabilities and limitations of Copilot, the privacy settings, IP concerns, and the importance of vigilance is vital.

Insight: Without clear company policy and developer adherence, using Copilot can pose significant risks. Proper governance and education are foundational to safe adoption.

Practical Steps for Using Copilot Safely at Work

For organizations considering or currently using GitHub Copilot, implementing specific practices can significantly mitigate potential risks:

• Choose the Right Plan: Opt for GitHub Copilot Business or Enterprise for enhanced privacy controls and contractual assurances regarding data usage.
• Configure Telemetry: Ensure settings are configured to prevent the use of code snippets for training future models.
• Establish Clear Policies: Define acceptable use, data handling rules, and the process for reviewing Copilot-generated code.
• Mandate Code Review: Treat all code suggested by Copilot as if it were written by a junior developer; require thorough review by experienced team members.
• Integrate with Security Tools: Ensure static analysis, dynamic analysis, and vulnerability scanning tools are used on the entire codebase, including Copilot's contributions.
• Educate Developers: Train developers on the privacy implications, potential for insecure or licensed code, and the importance of critical evaluation of suggestions.
• Avoid Sensitive Prompts: Instruct developers not to include highly sensitive company secrets or customer data within the prompts or surrounding code context.
• Monitor and Audit: Periodically review how the tool is being used and check for potential issues or policy violations.

Using GitHub Copilot at work offers productivity benefits, but it requires careful consideration of the associated risks. Implementing clear policies, utilizing appropriate account types, and maintaining robust development practices like code review and security scanning are essential steps to ensure its use is as safe and compliant as possible.