GDPR and AI risks for Codeium

Artificial intelligence has the potential of revolutionizing software development in unprecedented ways. Code assistants like Codeium, GitHub Copilot, Cursor.ai, and Amazon CodeWhisperer are becoming integral to modern software development practices with the goal of increasing productivity. However, implementing these tools requires careful risk assessment, particularly from data protection, information security, and responsible AI use perspectives.

Why We Conducted This Assessment?

As a data compliance company operating since 2016, PrivacyDesigner specializes in data protection (GDPR), information security (NIS 2, ISO 27001), and AI governance (AI Act). As part of our ISO 42001 AI management system implementation, we decided to conduct a comprehensive risk assessment of the Codeium code assistant before making the decision to purchase the tool.

This assessment is particularly significant for us because:

1. We operate as both a software development and data compliance consultation company. It is important that we act as we preach.

2. We have over eight years of experience in conducting various impact assessments, namely DPIAs. Thus, conducting risk assessments for IT tools before purchase or implementation is interwoven into our company culture and processes since day one. We also understand both the relevant technical / ICT and legal perspectives

3. We've developed our own GRC tool, PrivacyDesigner SaaS, which allows us to conduct risk assessments efficiently and in high level of detail ourselves. It saves us time, money, and helps us to identify risks that are often missed by other actors.

Our Assessment Methodology

1. Technical Analysis

Using our PrivacyDesigner SaaS tool, we conducted a comprehensive technical analysis of Codeium's architecture and operational principles. This analysis formed the foundation of our risk assessment and provided the high-level logic into the tool's data processing activities.

The basis for the analysis was the publicly available documentation available on Codeium's webpage. We thoroughly analyzed Codeium's public documentation:

(*) Privacy notices

(*) Technical documentation

(*) Terms of service

(*) Security practices

Visual data flow mapping

Our first step involved creating detailed a visual data-flow map. We identified various points in the processing, which we call 'objects' in PrivacyDesigner SaaS. We then placed the objects on the data-flow map, and connected them with connection lines with respective directions. Next, we identified various types of data, including code snippets, and how they travel through the Codeium ecosystem. We traced the complete journey from the moment code is written by a developer in the integrated development environment (IDE) through to the AI model processing and back. The journey begins with local IDE preprocessing, where code is initially handled and prepared for transmission. This data then moves through several key processing points, including large language models (LLM) utilized by Codeium.

Data type categorization

Through our analysis, we identified and categorized several distinct types of data flowing through the system. The primary data elements include source code fragments, development environment metadata, and user authentication data. More complex data types encompass model configuration parameters, API keys and credentials, code context and documentation, generated code suggestions, and user feedback data.

For each data type, we conducted a thorough sensitivity assessment, paying special attention to potential personal data that might be embedded within code comments or strings. This classification proved crucial for understanding the risk landscape and determining appropriate protection measures.

Processing roles definition

The processing chain involves multiple entities, each with distinct responsibilities and access levels. We mapped the entire ecosystem, starting with end users (developers) and extending through to the local IDE plugins, Codeium API services, and cloud infrastructure providers. Support and maintenance staff roles were also carefully documented. A critical aspect of this analysis was defining the controller/processor relationships and documenting data ownership. We paid particular attention to third-party service providers, ensuring their roles were clearly defined and their access appropriately limited. This mapping helped establish clear lines of responsibility and accountability throughout the processing chain.

Cross-border transfer identification

Given the global nature of cloud services, we conducted a detailed analysis of cross-border data flows. This began with identifying all locations where data is processed, including:

• Primary data centers and their backup locations

• Model training facilities

• Global support center locations

For data flows between the EU/EEA and third countries, we assessed the transfer mechanisms and safeguards in place. This included evaluating compliance with Schrems II requirements and documenting the technical and organizational measures implemented for international transfers. Our analysis covered both Standard Contractual Clauses (SCCs) and Binding Corporate Rules (BCRs), as well as existing adequacy decisions.

3. Regulatory Guidance Integration

We leveraged the risk assessment framework developed by two major European cybersecurity authorities:

• German BSI (Federal Office for Information Security)

• French ANSSI (National Agency for Information Systems Security)

4. Internal Workshop

We conducted a multi-disciplinary workshop to:

• Identify potential risks

• Assess risk probabilities and impacts

• Define risk mitigation measures

Examples of Key Findings

Data Protection Risks

1. Personal Data Processing in Code

Risk: Leakage of embedded personal data in code

Control: Enhanced code review processes and guidelines

2. Cross-border Data Transfers

Risk: Data transfers outside the EU

Control: Implementation of contractual safeguards

Information Security Risks

1. Code Quality and Vulnerabilities

Risk: AI-generated security vulnerabilities

Control: Automated vulnerability scanning

2. Access Management

Risk: Unauthorized access to source code

Control: Strong user management and monitoring

AI-Specific Risks

1. Model Governance

Risk: Generation of inappropriate or incorrect code

Control: Code validation and testing protocols

2. Intellectual Property

Risk: Use of copyrighted code

Control: License verification and documentation

Conclusions and Recommendations

Based on our assessment, Codeium implementation can be effectively managed with appropriate controls. We recommend:

• Development of documented guidelines

• Implementation of regular audits

• Staff training programs

• Continuous risk monitoring

Learn how you can conduct a risk assessment for Codeium

We at PrivacyDesigner have been helping organizations to conduct risk assessments for various products and services. We are creating sample privacy impact assessments and data protection impact assessments for our customers where they can start their assessments with pre-mapped data-flows and sample risks related to different tools and products. Apply to our pilot group and start building privacy into your products and services.