The Intersection of GDPR & AI: Navigating Data Protection When Adopting AI

How does GDPR impact AI innovation, and what affects might AI have on regulations like GDPR?

According to McKinsey, 78% of companies now use AI in at least one area of their business as of July 2024. But this quick adoption brings challenges for organisations handling data from the European Union and the UK.

The main challenge for InfoSec and other enterprise leaders is clear. Using AI effectively means being able to develop faster. But ensuring that data for AI projects is GDPR-compliant can cause significant slowdowns without the right solutions.

Let’s explore how GDPR affects AI projects — and how to stay compliant while continuing to leverage AI for innovation. 

First Of All: Does GDPR Cover AI?

Yes, absolutely. GDPR applies to any technology that uses the personal data of EU citizens. This includes AI systems. AI systems often face more scrutiny under GDPR. This scrutiny is because AI systems need larger datasets for training. AI also usually processes information in complex ways that can make transparency challenging.

GDPR doesn't specifically mention AI compliance. But its principles apply to how organisations collect, process, and manage data used in AI systems. If your organisation uses data from the EU, you must abide by the GDPR. This rule applies to using AI for customer grouping, predicting trends, automated decisions, and so on.

Understanding GDPR AI Compliance

The General Data Protection Regulation (GDPR) was implemented in May 2018. It is the biggest update to data protection laws in decades. It aims to give people control over their data. It also seeks to create a unified regulatory environment for businesses operating in the EU.

GDPR Principles that Impact AI

Several GDPR principles directly affect how data is used in AI projects:

Data Minimisation

Organisations should only collect data that is necessary for their specific purpose. It's important to note that properly masked data faces fewer restrictions. With effective data masking, you can continue using secure, production-like datasets while protecting people's privacy and developing more robust features and functions.

Purpose Limitation

The GDPR mandates that you must collect personal data for specific, clear purposes. The data collected cannot be used in ways that don't match those purposes. This poses challenges for AI systems that may discover new ways to use data that were not anticipated when it was first collected. Again, properly masked data faces fewer restrictions.

Storage Limitation

Personal data should only be kept as long as needed for its original purpose. This is challenging for AI systems that may need historical data to keep learning and improving.

Lawful Basis for Processing

All data processing must apply to lawful bases. This includes consent, contract requirements, legal obligations, vital interests, public tasks, or legitimate interests. For AI systems, documenting this lawful basis is essential.

Data Subject Rights

The GDPR gives individuals specific rights over their data. This includes access, correction, deletion, limiting processing, data portability, and objection. Deletion, or the right to be forgotten, is particularly challenging for AI systems. Once an AI learns from data, it doesn't simply "forget" it when the original data is deleted.

What Are the Requirements?

When using AI with personal data, here are some of the GDPR requirements that organisations need to keep in mind:

Data Protection Impact Assessments (DPIAs)

Article 35 requires organisations to conduct DPIAs for high-risk data processing activities. This applies to AI systems that make automated decisions affecting people.

Documentation and Auditability

Organisations who use personal data must keep detailed records of their data processing activities. This includes how AI systems use the personal data. Clear audit trails are essential to prove compliance.

However, this is only the case when organisations are using actual personal data. It doesn’t apply when using masked data.

Privacy by Design

The GDPR requires data protection measures to be built into AI products and services. This means including privacy considerations from the initial design through deployment and beyond.

Evolution of GDPR Understanding Since Implementation

Companies’ understanding of GDPR has evolved alongside the revisions and updates made to it since its inception in 2018.

Initial Focus to Broader Application

At first, organisations focused on basic compliance measures. This covered updating privacy policies and implementing consent mechanisms. As the GDPR matured, attention has shifted to more complex areas. New areas of concern include:

• Legitimate interest assessments
• International data transfers
• Guidelines for automated decision systems

Enforcement Priorities

At first, GDPR enforcement targeted obvious violations and major data breaches. Now, enforcement has expanded to address system-wide issues such as insufficient technical safeguards. 

Cross-Border Data Transfer

The rules for international data transfers have changed significantly. One example is the invalidation of the Privacy Shield framework for EU-US transfers. New transfer mechanisms have been developed. This includes the updated Standard Contractual Clauses and the EU-US Data Privacy Framework. Both are particularly important for organisations training AI models across many countries.

Practical Implementation

Organisations have moved from checkbox compliance approaches to more comprehensive privacy management programs. Impact assessments have become more sophisticated, especially for new technologies like AI. 

Which Organisations Must Comply?

General and AI GDPR compliance requirements apply to:

• Organisations based in the EU that process personal data.
• Organisations outside the EU that offer goods or services to EU residents.
• Organisations that watch the behavior of EU residents.

Industries that handle high volumes of personal data are at higher risk of running afoul of GDPR. This includes healthcare organisations, financial services firms, and telecommunications companies. These industries must be especially vigilant with GDPR compliance when implementing AI solutions.

Global organisations using AI across many regions face specific challenges from the GDPR. That is because data used to train models may cross country boundaries. Understanding data controller and processor responsibilities is crucial. Especially in collaborative AI projects where many parties may act as joint controllers.

In order to maintain a competitive edge with non-EU companies, organisations developing AI within the EU need to focus on developing balanced personal data strategies. There is a balance to be struck between risk of exposure and the value of the data. And it is possible to find the balance that allows you to innovate with AI, remain competitive, and comply with GDPR.  

Stay Compliant with GDPR AI Rules (Without Slowing Down)

Many companies don't fully understand the impacts AI will have on their development and analytics pipelines. This lack of awareness could lead to regulatory problems and reputational damage.

Want to learn more about balancing AI innovation with compliance? Read "AI Without Compromise: Balancing Innovation, Speed, and Data Privacy in AI & Analytics." This guide from Perforce Delphix’s resident AI expert Steve Karam explores how enterprise leaders can successfully navigate AI while complying with regulations like GDPR, the EU AI Act, and future AI regulations.

Strike Your Balance

Beyond GDPR & AI: The Current Regulatory Landscape for AI & Data

GDPR isn’t the only standard your enterprise needs to abide by, especially when it comes to AI and data compliance. New regulations are emerging to cover AI. The EU AI Act creates a risk-based framework specifically for AI systems. As with GDPR, other established regulations will continue to evolve and adapt for AI, like CCPA and HIPAA.

Aside from AI data compliance-specific regulations, other compliance regulations also continue to evolve and will impact AI and data. For example, the Digital Operational Resilience Act (DORA) introduces new rules for digital infrastructure security. 

GDPR established global standards for data protection that spread beyond the EU. As newer regulations (like the EU AI Act) emerge, they build upon GDPR's foundation. They also address specific concerns related to AI implementation. Emerging AI regulations will likely influence global practices quicker than ever before. 

Organisations should stay prepared and anticipate an increasingly complex compliance landscape.

5 Best Practices for GDPR-Compliant AI Development & Data Management

1: Design for Compliance

Build GDPR compliance into AI systems from the beginning. Use data masking techniques that provide realistic data that is kept referentially correct across multiple datasets. This approach protects personal information while keeping data useful for AI training. It reduces compliance risks while enabling AI development.

2: Establish Governance and Tracking

Create clear rules and audit trails for data use in AI systems. Document who accessed systems, what changes were made, and how data flows through your organisation. These audit records are essential for identifying and addressing potential issues.

3: Use Automation to Balance Speed with Security

Use secure automation in data pipelines to support faster AI initiatives. Automation makes a process repeatable and less prone to human error. You want processes to be repeatable because if you continuously test them, the only factors that change are the data you are working with to produce the desired outputs. 

4: Implement Smart Data Minimisation

Reducing data volume can make AI less effective. Instead, use static masked data to replace real identifying information with realistic but fake data. Properly masked data faces fewer minimisation requirements under GDPR. This allows organisations to maintain the larger datasets needed for effective AI training.

5: Manage Sensitive Data Risks Effectively

Use data discovery and classification techniques to identify sensitive information within your datasets. Put in place static data masking for development and test environments. This will establish safeguards against re-identification of individuals in datasets. These measures help ensure AI systems produce fair, unbiased decisions while maintaining GDPR compliance.

Accelerate AI Innovation & Ensure GDPR Compliance with Perforce Delphix

Don't let GDPR requirements slow down your AI initiatives. Perforce Delphix helps organisations build secure, compliant data pipelines for use in AI and analytics.

AI Regulations Are New, But Data Compliance Isn’t

AI compliance regulations may be new, but the concepts of masking data for compliance are not. 

Here at Delphix, we’ve helped enterprises around the globe mask data for compliance with regulations like GDPR. And for good reason: with Delphix, 77% more data and data environments are masked and protected.*

Hear for yourself how Delphix helped:

• Sky Italia become GDPR-compliant in 5 months and reduce operational costs by 30%.
• Cal State University stay compliant with HIPAA, PCI, GDPR, and CCPA and save $7.5M in development storage costs.
• The University of Manchester address GDPR compliance and mask more than 130M rows of sensitive data.

The Delphix Difference: Masking Data for GDPR AI & More

Delphix data masking delivers high-quality, compliant data for your teams. This means faster AI development cycles without exposing sensitive information or risking GDPR violations.

Perforce Delphix can help you:

• Automate data masking to protect personal information.
• Reduce GDPR compliance risks in AI training data.
• Simplify complex compliance processes with built-in audit trails.
• Mask 170+ sources, like Snowflake and Databricks.
• Prepare your data pipelines for future AI legislation.

Get in touch with our experts to learn more about our enterprise solutions for AI and analytics.

Mask Data for GDPR AI Compliance

*IDC Business Value White Paper, sponsored by Delphix, by Perforce, The Business Value of Delphix, #US52560824, December 2024