Assessing the Safety of GitHub Copilot for Business Use

Organizations evaluating GitHub Copilot for developer teams consider its potential benefits alongside crucial safety and security implications. The primary concerns typically revolve around data privacy, intellectual property (IP), code security, and reliability. Understanding how Copilot functions and the available features is essential for determining its appropriate use within a corporate environment.

Data Privacy and Confidentiality Concerns

A key consideration for businesses is whether using Copilot could expose sensitive internal code.

• How Copilot Processes Data: When a developer uses Copilot, code snippets from the active file and surrounding files are sent to OpenAI's servers to generate suggestions.
• Potential Risk: Without proper safeguards, there is a theoretical risk that internal, proprietary code could be used in a way that compromises its confidentiality.
• GitHub Copilot Business and Enterprise: These versions offer enhanced privacy controls. By default, code snippets from prompts and suggestions are not retained by Microsoft or OpenAI and are not used to train future AI models. Limited data, such as telemetry and engagement data (whether a suggestion was accepted), is collected but is not linked to specific users and is used for service improvement.
• Mitigation: Organizations using Copilot Business or Enterprise can be confident that their private code is not being used to train the global model. Establishing clear policies on the types of code developers should work with while using Copilot adds another layer of security.

Intellectual Property and Licensing Risks

The code generated by Copilot is synthesized from a vast dataset of public code. This raises questions about potential IP infringement and licensing compliance.

• Basis of Suggestions: Copilot generates code patterns and snippets learned from public repositories, including open-source projects.
• Potential Risk: It's possible, though uncommon, for Copilot to generate code snippets that closely match existing code from its training data. If this matching code is from a project with a restrictive license (like GPL), incorporating it without attribution or compliance could create legal issues for the company.
• "Public Code Suggestion Matching" Feature: GitHub Copilot includes a feature that can detect if a generated suggestion matches code from public repositories. This feature, if enabled, can flag potential matches and provide a link to the original source code, allowing developers to review the license and determine if the code can be used.
• Mitigation: Enabling the public code suggestion matching feature is a crucial step. Organizations should also maintain robust code review processes where generated code is scrutinized just like manually written code. Static analysis tools can help identify potential licensing issues. Establishing clear internal guidelines on handling generated code is also vital.

Code Quality and Security Vulnerabilities

Code suggested by an AI might not always be perfect, secure, or align with company standards.

• Source of Knowledge: Copilot learns from a wide range of code, which includes examples of insecure coding practices or bugs.
• Potential Risk: Copilot might suggest code containing security vulnerabilities (e.g., injection flaws, weak cryptography), introduce bugs, or deviate from established coding standards and patterns used within the organization.
• Mitigation: Generated code should never be blindly accepted. Developers must understand the suggested code, review it carefully, and refactor it as needed to fit the project's requirements, standards, and security practices. Standard security testing (like static analysis security testing - SAST) and code review processes are critical safety nets that apply equally to human-written and AI-generated code.

Reliability and Accuracy

Copilot is a probabilistic tool; its suggestions are not guaranteed to be functionally correct or the most efficient solution.

• AI Nature: Suggestions are based on patterns and context but do not involve true understanding or verification.
• Potential Risk: Relying solely on Copilot without verifying the output can lead to the introduction of incorrect or inefficient code into a codebase, potentially causing bugs or performance issues.
• Mitigation: Developers should treat Copilot suggestions as helpful starting points or aids, not as definitive solutions. Thorough testing (unit tests, integration tests, etc.) of generated code is essential, just as it is for all code incorporated into a project.

Mitigation Strategies for Organizations

Ensuring the safe use of GitHub Copilot at work requires a multi-faceted approach.

• Choose the Right Plan: Utilize GitHub Copilot Business or Enterprise for enhanced privacy and control over data usage.
• Define Clear Policies: Establish internal guidelines for developers on how to use Copilot, including expectations around code review, testing, and handling suggestions flagged by the public code matching feature.
• Enable Security Features: Configure Copilot settings, particularly enabling the public code suggestion matching feature if licensing compliance is a primary concern.
• Reinforce Development Practices: Emphasize the importance of standard development workflows, including thorough code reviews by human peers, comprehensive testing, and the use of static analysis and security scanning tools. These practices are the most robust defense against potential issues introduced by any code, including AI-generated code.
• Training: Educate developers on the capabilities and limitations of Copilot and the organizational policies surrounding its use.

Tips for Developers

Developers play a critical role in using Copilot safely and effectively.

• Understand the Code: Never accept a suggestion without reading and understanding what it does.
• Review Critically: Treat AI-generated code with the same skepticism as code written by a junior developer. Look for potential errors, inefficiencies, and security vulnerabilities.
• Test Thoroughly: Ensure that any code incorporated from Copilot suggestions is covered by tests.
• Refactor as Needed: Adapt suggestions to fit the project's existing architecture, coding style, and standards.
• Be Aware of Context: Recognize that Copilot's suggestions are based on limited context (the current file and a few surrounding files). Broader architectural or domain-specific nuances might be missed.

In conclusion, while GitHub Copilot introduces new considerations regarding data privacy, IP, and code quality, its safety at work can be significantly enhanced by using the business-focused versions, implementing clear organizational policies, leveraging built-in features like public code suggestion matching, and maintaining rigorous development practices like code review and testing. It is a tool that augments developer productivity but requires oversight and critical evaluation of its output.