The Intersection of GDPR and AI and 6 Compliance Best Practices
What Is GDPR?
GDPR or General Data Protection Regulation is a regulation, enacted in the European Union in 2008, which focuses on data protection and privacy. It not only applies to the European Union but also affects the transfer of personal data outside the EU and EEA areas, as well as organizations outside the EU who do business with EU citizens. The objective of GDPR is to empower individuals to control their personal data and to simplify the regulatory environment for international business by unifying the regulation within the EU.
Under GDPR, organizations are required to ensure the privacy and protection of personal data, provide data breach notifications, uphold the safe transfer of data across borders, and maintain certain practices to be in compliance. Non-compliance could lead to large fines, up to €10 million or 2% of a firm’s annual revenue.
GDPR is reshaping the way organizations approach data privacy and protection, and has far-reaching implications across all sectors. It has changed the business landscape in many ways, one of them being how artificial intelligence (AI) systems use and process personal data.
Related content: This is part of an extensive series of guides about GDPR compliance.
The Intersection Between GDPR and AI
The General Data Protection Regulation (GDPR) considerably influences the creation and application of Artificial Intelligence (AI) technologies, which typically require processing large volumes of data. AI systems, especially Large Language Models (LLMs), have to adhere strictly to GDPR’s demands if they use data belonging to EU citizens, or plan to be deployed for usage in the EU. Here are the key ways in which GDPR impacts AI development:
Justifiable Grounds for Data Management
GDPR defines the requirement for explicit consent for the usage of personal data by AI models. AI developers must guarantee that consent is willingly provided, specific, informed, and unequivocal.
In some cases, AI can handle personal data based on the justified grounds of “legitimate interest”. Nevertheless, this necessitates a delicate balance to ensure that the data subject’s rights are not compromised.
Data Minimization and Purpose Restriction
The GDPR stipulates that for any specific purpose, only the minimal required data should be used. AI mechanisms must abide by this, preventing the collection or manipulation of unnecessary data. In addition, data gathered for one aim should not be repurposed without additional consent.
Anonymization and Pseudonymization
AI mechanisms, such as LLMs, should employ anonymization and pseudonymization methods. Anonymization permanently prevents identification, whereas pseudonymization substitutes private identifiers with fake identifiers or pseudonyms. These techniques can safeguard individual privacy while allowing AI systems to derive insights from large datasets.
Protection and Accountability
The GDPR expects that personal data is manipulated in a way that guarantees its protection. AI systems must integrate security practices to prevent data infringements and unauthorized access.
AI developers and users are both held accountable for adhering to the GDPR. This involves keeping records of data manipulation activities, carrying out impact assessments, and incorporating data protection by design and by default.
Individual Rights
The GDPR grants the following individual rights in connection with the use of data in AI models:
- Access and portability: Individuals have the right to access their data and obtain it for reuse. AI systems must uphold this right by allowing individuals to recover their data and relocate it to another service if required.
- Right to explanation: Individuals are entitled to understand the reasoning behind decisions made through automated processing. AI systems should be transparent and provide understandable decision-making methodologies.
- Right to be forgotten: Individuals can demand the erasure of their personal data. AI systems must have mechanisms to ensure data can be completely erased upon request.
6 Best Practices to Ensure AI Development and Implementation Complies with GDPR
GDPR compliance for AI systems is at its early stages. Here are some best practices that can help you get this process off the ground:
1. Integrating Data Security and Privacy into AI Development
To align AI development with GDPR regulations, it’s important to prioritize data security and privacy from the outset. This includes:
- Security reviews for API endpoints: APIs are the bridges through which data enters and exits an AI system. It is critical to ensure they are securely designed and implemented. This can prevent both non-compliant ingestion of private data and accidental leakage of data.
- SDLC audit: AI systems require a comprehensive audit of the full software development life cycle (SDLC), including both static and dynamic testing of applications. This guarantees that security measures are at place at each stage of the process, from design to deployment.
2. Defining Data Governance Standards for AI Projects
Organizations must put in place clear, precise, and transparent data governance standards for AI projects. These standards need to detail how data is gathered, analyzed, stored and used within AI systems. Such governance lets all stakeholders, from developers to end-users, understand their duties in upholding data accuracy and privacy.
An important aspect of data governance standards is determining ethical use cases of AI systems, and providing them to developers as part of the non-functional requirements of the project. Just like developers aim to build AI systems that are reliable and performant, they should strive to make AI systems compliant with ethical guidelines to prevent harm to individuals or society.
3. Purpose Specification and Documentation
For GDPR compliance, it is important to define and record the specific, explicit, and justified purposes for which the AI system will use private data. This documented purpose should direct the design operation of the AI system, and also ensures transparency and accountability. It helps to align the AI’s objectives with legality and ethics, avoiding data abuse or application of data for unintended purposes.
4. Execution of DPIAs
Data Protection Impact Assessments (DPIAs) per Article 35 of the GDPR are a requirement for AI systems handling high-risk processes. DPIAs assist in detecting and mitigating risks affiliated with data processing tasks. Given the intricacy of AI systems and their potential effects on individuals’ privacy, it’s crucial for AI systems to undergo this analysis. Incorporating DPIAs into the project lifecycle allows for early detection and resolution of potential data protection problems.
5. Informing Users About AI-Driven Decision Logic
GDPR demands that users be informed about the reasoning behind AI-driven choices. This entails revealing how the AI system processes data and makes decisions. Transparency not only fosters user trust but also enables individuals to understand and, when required, contest decisions impacting them. This is particularly vital in sectors where AI decisions bear substantial repercussions, such as finance or healthcare.
6. Implementation of Ongoing GDPR Compliance Monitoring
The GDPR requires organizations to define procedures for ongoing compliance supervision and AI system audits. Continuous monitoring can help identify and rectify compliance problems as they happen. Regular checks on AI systems ensure that they function as desired and stay compliant with GDPR stipulations. These procedures should be part of a continual commitment to uphold data privacy and security, and adapt to legal and technological shifts.
Related content: Read our guide to GDPR requirements.
GDPR Compliance with Exabeam
At Exabeam, trust is the cornerstone of how we operate — encompassing everything from how we build our products to how we run our operations. We understand that one of your most valuable assets is your data, and we focus on ensuring your data is secure, data privacy rules are followed, and the platform has a high uptime.
Exabeam AI-driven Security Operations Platform provides a centralized mechanism where each application team can send events to the audit log for compliance and threat detection use cases. Users will store audit events for the duration of their contract terms, search and action the events as they would any other 3rd party log in the Exabeam Platform. Users may configure correlation rules against the audit log to detect non-compliance events and may configure dashboards with any events in the event store, including audit log events.
Audit logs represent the user, object, or setting events in your organization. Specific events related to all Exabeam users are logged, including activities within the user interface and configuration activities. Exabeam stores all audit logs and provides a query interface in Search that you can use to find and export audit logs. This, along with visualizations and tables tagged by audit needs in Dashboards, is especially useful for reviewing activities for GDPR audits.
For more info, visit the Exabeam Compliance page