GDPR-compliant AI solutions: Building privacy-first systems

When talking about AI compliance and safety, Clara Shih, the Head of Business AI at Meta, noted:

“There is no question we are in an AI and data revolution…but it’s not as simple as taking all of your data and training a model with it. There are data security, access permissions, and sharing models that we have to honour.”

Here’s what our CEO, Dmitry Sverdlik, adds to the matter:

“Trust starts with data discipline. Privacy is an engineering requirement. Encrypt by default, minimize by design, and keep full audit trails. That’s how AI earns its license to operate.”

Both insights echo the forces changing the AI landscape. Analysts estimate the privacy-reserving AI market to reach $29.5 billion by 2032. A major leap from its current value of $2.88 billion. This growth trajectory shows that compliance and risk drive buyer demand. This study found 69% of organizations list AI-powered data leakage as their top security concern, while 47% lack AI-specific security controls entirely.

Regulatory enforcement has intensified.. In Q1 2025, EU data protection authorities issued 2,245 enforcement actions. The fines totaled €5.65 billion, averaging €2.3 million per incident. At the same time, McKinsey reports that about 75% of organizations use AI in at least one business function, with only 28% of respondents reporting CEO-level oversight. AI adoption and accountability don’t align, leading to significant liability risks.

Here’s where we’re headed: this article turns regulatory requirements into actionable implementation guidance. We map GDPR’s core principles into concrete system choices, demonstrate privacy-by-design in practice, and lay out the steps for consent management, explainability, and DPIA. You’ll see the technical patterns for compliant systems,  governance checks, cross-border data handling, and real-world implementation examples. The objective: ship AI systems that are compliant, maintain operational resilience, and ready for scale.

Understanding the GDPR: The seven principles for AI

In Article 5, the GDPR outlines seven key principles for handling personal data:

For AI systems, these measures translate into concrete architectural requirements and operational constraints. Understanding the seven principles is the first and crucial step to avoiding fines and legal action. 

Principle #1. Lawfulness, fairness, and transparency

Lawfulness, fairness, and transparency principles require documenting legal bases. Article 6.1 specifies six such bases:

1. consent;
2. contract;
3. legal obligation;
4. vital interests;
5. public tasks;
6. legitimate interests.

The legitimate interests basis stands on a three-step assessment. First, demonstrating a genuine business need, second, proving that no less intrusive alternative exists. Third, conducting a balancing test between organizational interests and individual rights.

Article 22.1 states:

“The data subject shall have the right not to be subject to a decision based solely on automated processing…which produces legal effects concerning him or her or similarly significantly affects him or her.” 

This grants users the right to refuse decisions made solely by AI, particularly when those decisions affect their lives.

For example, if an AI system denies a loan application, a human review is mandatory. In turn, when an AI solution offers personalized advertisements, no human-in-the-loop (HITL) is needed.

Principle #2. Purpose limitation

The purpose limitation principle prevents data from being repurposed without a legal justification. Training a fraud detection model doesn’t allow for using the same data for marketing. For general-purpose AI models, this creates tension. If you train a large language model (LLM) or a small language model (SLM) on customer service conversations, can you later use it for sales optimization?

Article 6.4 provides the compatibility test through five criteria:

“(a) any link between the purposes…(b) the context in which the personal data have been collected, in particular regarding the relationship between data subjects and the controller; (c) the nature of the personal data; (d) the possible consequences of the intended further processing for data subjects; (e) the existence of appropriate safeguards.” 

In other words, before reusing a data pipeline for a new purpose, organizations need to pass a five-part compatibility test. It determines whether the new use aligns with the original collection purpose.

• Compatible example: You collected customer service chat logs to “improve support quality.” Using them to “train an AI chatbot for customer support” has a clear link (both serve customer support).
• Incompatible example: You collected the same chat logs. Using them to “identify high-value customers for sales targeting” breaks the link (shifts from service to sales).

Organizations must document this analysis for each new AI solution that repurposes existing data.

Principle #3. Data minimization

The data minimization principle restricts processing to necessary data. Article 5.1 requires personal data to be “adequate, relevant, and limited to what is necessary in relation to the purposes for which they are processed.” The European Data Protection Board (EDPB) clarified that large training datasets are permissible when properly selected and cleaned.

In practical terms, it means auditing and asking some key questions:

• Does your talent sourcing AI solution need postal codes, or does it introduce geographic bias?
• Can you achieve the same level of accuracy with 100,000 training examples instead of 10 million?

Balancing AI innovation with data minimization is key. You should find a way to maintain high model performance while reducing data usage. Organizations achieve this through transfer learning and synthetic data generation, techniques that preserve accuracy while minimizing personal data collection.  

Principle #4. Accuracy

The accuracy principle focuses on data quality. Article 5.1 requires personal data to be: “accurate and, where necessary, kept up to date; every reasonable step must be taken to ensure that personal data that are inaccurate… are erased or rectified without delay.” AI systems trained on inaccurate data produce biased outcomes.

In other words, the data that AI agents use must be accurate and up to date. Imagine you are training an AI talent-sourcing model using employee data. It shows that “John Smith works in Sales,” but John actually moved to Engineering one year ago. As a result, the model learns false patterns. When someone later asks for a correction, the database must be updated and the model retrained to “forget” the incorrect input.

Organizations must have data quality controls in place. This means:

• validation controls at data collection;
• regular accuracy audits;
• clear process to correct errors.

Article 16 grants the right to rectification. People have the right to correct wrong information about themselves in your systems and add missing details that explain why you collected that data.

Don’t just fix the database record. Ask whether the incorrect data has already influenced your model’s predictions.

Principle #5. Storage limitation

The storage limitation principle poses the “machine unlearning” challenge. Article 5.1 requires personal data to be “kept in a form which permits identification of data subjects for no longer than is necessary for the purposes for which the personal data are processed”. In addition, Article 17.1 establishes the right to erasure: “The data subject shall have the right to obtain from the controller the erasure of personal data concerning him or her without undue delay.”

Complete data removal from model training demands retraining from scratch, which is expensive and time-consuming. Current approaches include:

• keeping training data separate with clear retention policies;
• implementing approximate unlearning algorithms to adjust model weights;
• documenting when full retraining occurs to ensure complete data removal.

Don’t keep training data longer than necessary. Once you’ve achieved the desired purpose, data deletion becomes mandatory. For AI, this creates a unique compliance challenge. When someone says “delete my data,” organizations must remove it from databases, backups, and logs. But what about AI models already trained on that data? 

Principle #6. Integrity and confidentiality

The integrity and confidentiality principle mandates the use of technical measures. Article 5.1 requires processing “in a manner that ensures appropriate security of the personal data, including protection against unauthorised or unlawful processing and against accidental loss, destruction or damage.” 

Article 32.1 specifies: “the controller and the processor shall implement appropriate technical and organisational measures to ensure a level of security appropriate to the risk, including… the pseudonymization and encryption of personal data.”

What this means for AI:

• During training: Encrypt all data at rest (AES-256), and when moving between systems (TLS 1.3). Restrict who can access training data. Log every access attempt.
• During deployment: Prevent malicious actors from “stealing” your model by querying it millions of times to reverse-engineer it. Secure API endpoints. Watch for unusual query patterns and limit the number of requests a single user can make.

Keep data secure from malicious attacks, unauthorized access, and accidental loss by systematically implementing technical safeguards throughout the AI lifecycle.

Principle #7. Accountability

The accountability principle is all about demonstrating compliance through documentation and processes. Article 5.2 establishes: “The controller shall be responsible for, and be able to demonstrate compliance with, paragraph 1 (‘accountability’).”

Organizations cannot just claim compliance. They need to prove it with documentation, audits, and systematic processes. For AI solutions, accountability means maintaining clean records, including:

1. Records of Processing Activities (RPA) document all instances of personal data usage.
2. Data Protection Impact Assessments (DPIAs) for high-risk systems.
3. Training logs showing data sources and timing.
4. Model cards document training data sources and limitations.
5. Audit trails of who accesses what data and when.
6. Incident response records show how the company handled breaches or failures.

The accountability principle takes GDPR from a checkbox exercise to an operational discipline. Without strong documentation and governance, even technically superior AI systems become regulatory risks.

These seven GDPR principles are the backbone of compliant AI development. Without understanding those fundamental requirements, moving forward with technical implementation becomes guesswork.

They translate into architectural decisions and operational controls that determine whether an AI solution respects individual rights or creates regulatory liability. The real challenge lies in embedding these principles into the development process from day one. And this is where privacy-by-design comes into play.

Privacy-by-design and more about data minimization

In February 2025, the Commission Nationale de l’Informatique des Libertés (CNIL) issued a regulation allowing extended retention of training data with appropriate security measures. Organizations no longer need to constantly retrain AI models when users request the withdrawal of their personal information. They can maintain training datasets for model updates without re-collection. The only criterion is to have strong security controls in place. The ambiguity introduced by GDPR regarding data retraining is now resolved. However, this flexibility does not validate that “collect now, think later” is a sound policy.

Strong security controls start with privacy-by-design. When training models, teams must integrate data protection at the very beginning. That’s when data minimization becomes essential, following a simple three-fold rule:

1. gather only what you need;
2. anonymize where possible;
3. keep the training data only as long as it is necessary.

These approaches reduce the potential attack surface, limit regulatory liability, and make it much easier to follow data subject requests.

Evidence of the security gap

To understand whether there is a gap between AI adoption and security maturity, consider these numbers:

• 90% of organizations aren’t prepared to secure AI systems.
• 77% lack foundational data and AI security practices.
• 22% have clear policies or training for generative AI (GAI).
• 25% use encryption or access controls.
• 2% have implemented cyber resilience practices across operations.

When it comes to regional discrepancies, the numbers paint an even more dire picture.

Together, the numbers show how few organizations follow the rule we discussed. Integrate privacy and security from the very start.

Techniques for data minimization

Many teams treat privacy-by-design as something abstract, although it becomes fully practical once you anchor it in specific engineering methods:

• Pseudonymization and tokenization. Replace identifiers with tokens. As a result, data cannot be linked back to individuals without extra information. From GDPR’s perspective, it means you can train models without exposing real identities. Even if a data breach happens, it will expose useless tokens instead of personal data.
• Differential privacy. Introduce noise to datasets or outputs. Prevent reverse engineering of individual records. This enables GDPR-compliant analytics. An AI model learns population trends without memorizing specific individuals. It will be impossible to identify whether someone’s data was in your training set.
• Federated learning. Keep training data on local devices or services. Exchange only model parameters.
• Retention policies. Define clear schedules for deleting or archiving data. Automatic deletion scripts enforce storage limitations without manual intervention. 

Applying these methods significantly limits the blast radius of any potential breach. It also helps sustain compliance by processing the minimum amount of personal data necessary for the task.

Access controls and points of entry

Technical privacy measures protect data from external threats, while GDPR also requires protecting data from inappropriate internal access. Even strong encryption fails if all employees can access raw training data. Human error remains responsible for the overwhelming 95% of data breaches.

Proper access control implementation requires role-based and context-based models to work together. Role-based access control (RBAC) presents these permissions:

• Data scientists. Read access to de-identified training data. Submit training jobs. Deploy models to staging. No access to production data, PII databases, or raw logs.
• Privacy officers. Access audit logs, manage consent records, view processing activities, and generate compliance reports. No access to raw PII or database queries.
• ML engineers. Deploy models to production, configure inference infrastructure, and track performance. Access aggregated metrics but not individual predictions.

Executing this consistently often requires a mature data platform. Data engineering and platform modernization services enable organizations to build correct pipelines. These enforce data minimization and maintain audit trails across distributed systems. All critical capabilities for maintaining GDPR compliance at scale.

Cost of poor practices

GDPR non-compliance comes at a great price, often in tens or hundreds of millions.

On average, a GDPR-related fine comes to about €2.36 million. If the penalty follows a data breach, add an extra $4.4 million in incident-related costs, including forensics, customer notification, legal work, downtime, and compensation. 

Only 10% of companies are “reinvention ready.” This means they can adapt to compliant security measures and are less likely to face advanced AI-related attacks. Even with basic math, it is clear: investing in privacy and compliance upfront pays for itself many times over.

The important role of DPIAs and ethical governance

The GDPR requires DPIAs when your data processing might affect people’s rights. Any AI system that can influence people’s rights typically falls into this category, which is why most enterprise AI initiatives require a DPIA before deployment.

AI projects usually trigger DPIA requirements when they involve one or more of the following activities:

• automatically score or evaluate people at scale;
• make important decisions that affect people’s lives;
• process huge amounts of sensitive data;
• monitor people systematically.

Article 35.3 specifies when DPIAs are mandatory: 

“A data protection impact assessment… shall in particular be required in the case of: (a) a systematic and extensive evaluation of personal aspects relating to natural persons which is based on automated processing, including profiling, and on which decisions are based that produce legal or similarly significant effects concerning the natural person; (b) processing on a large scale of special categories of data… or of personal data relating to criminal convictions and offences; or (c) a systematic monitoring of a publicly accessible area on a large scale.”

Any AI system that evaluates creditworthiness, handles medical information, performs customer risk scoring, or analyzes behavioral patterns represents high-risk processing. DPIA before deployment is a must. There is no exception for early prototypes or “small” AI projects.

The five-step DPIA process

A DPIA should be viewed as far more than just paperwork. It is a systematic approach to identifying and fixing privacy risks early, before they become regulatory violations. The DPIA assessment follows five steps, designed to evaluate whether your AI solution is necessary, proportionate, and adequately protected throughout its lifecycle.

Step #1. Identify processing

Start with a complete mapping of how data enters, moves through, and leaves the system. This requires a clear, visual representation of all components and interactions:

• Sources (user input, sensors, third-party APIs).
• Storage (databases, data lakes, backups).
• Processing (training, inference, analytics).
• Outputs (interfaces, downstream systems).
• Retention.

Classify data sensitivity using a tiered framework:

1. Public (non-personal or openly available data).
2. Internal (basic personal identifiers).
3. Confidential (financial, location).
4. Restricted (health information, biometric identifiers, or other special category data).

This stage creates a full picture of the personal data lifecycle. You need to know precisely where information originates, where it travels, who interacts with it, and how sensitive each element is. 

The process resembles tracking a package through a delivery network, where every checkpoint must be visible. If teams cannot produce an accurate diagram, it signals that the system is not fully understood and therefore cannot be adequately secured.

Step #2. Check necessity

Apply necessity tests documenting genuine need, less intrusive alternatives, and proportionality. Here’s the example statement:

“We considered training fraud detection on transaction metadata alone. But, testing showed 23% higher false positive rate compared to models including IP addresses and device fingerprints. The accuracy improvement justifies extra data collection because false positives freeze legitimate transactions.”

This step always begins with a simple question: “Do we need this data, or do we just want it?” Test whether a model can achieve acceptable results with less sensitive information. If collecting more data is unavoidable, prove it with numbers. Show that the privacy cost is worth the benefit.

Step #3. Assess risks

Evaluate the risks associated with processing. Most DPIAs use a standard matrix based on:

• Likelihood (rare/possible/likely/certain).
• Severity (minimal/moderate/significant/severe).

Focus on high-likelihood, high-severity risks. These can be discrimination from biased models, privacy loss through re-identification, unauthorized profiling, and security breaches.

For example, focus on a risk like a biased hiring AI solution that’s already showing gender discrimination in testing (likely) and would deny people jobs (severe). Don’t waste time on theoretical risks that are unlikely and minor.

Step #4. Define safeguards

Safeguards form the backbone of the DPIA. Each identified risk must be matched with controls that reduce either the likelihood or the impact.

1. Encryption (AES-256 at rest, TLS 1.3 in transit).
2. Differential privacy (epsilon 0.1-1.0 for highly sensitive data).
3. Federated learning.
4. Homomorphic encryption.
5. Multi-party computation.

Organizational measures include human oversight, ethics review boards, bias auditing, and staff training. Contractual measures include Standard Contractual Clauses (SCC), Data Processing Agreements (DPA), and joint controller agreements.

Strong protection relies on the combined effect of technical, organizational, and contractual measures. No single safeguard is sufficient. The goal is to build multiple layers so that if one control fails, others continue to protect the system.

Step #5. Document and review

Record decisions, rationale, and safeguards. Consult a Data Protection Officer (DPO) before deployment. Review annually, as well as when processing changes materially.

Make sure everything is noted. What risks were found, why each choice was made, and what protections were implemented. Keep in mind, it is not a one-time checklist. Reviews must be conducted annually or whenever there are significant changes to the AI solution. Have documents to explain your decisions to a regulator a year from now.

Ethical frameworks

Beyond DPIAs, ethical governance requires articulating guiding values. These must correlate with respect for autonomy, prevention of harm, fairness, and explicability.

A quote from the Ethics Guidelines for Trustworthy AI (2019):

“Trustworthy AI should be: (1) lawful – respecting all applicable laws and regulations; (2) ethical – respecting ethical principles and values; and (3) robust – both from a technical perspective while taking into account its social environment. Trustworthy AI requires three components working in harmony: it should be lawful, ethical and robust. Each pillar is essential, and failings in any one could undermine the whole system… Trustworthy AI has four ethical principles rooted in fundamental rights: respect for human autonomy, prevention of harm, fairness and explicability.”

These values align with both the GDPR and laws like the EU AI Act.

Real-life implementation example: Microsoft’s Responsible AI Standard

Microsoft created a Responsible AI Standard with implementation requirements:

• Required for all AI releases. Every team must complete a “Responsible AI Impact Assessment” before launching any AI feature or product.
• Sensitive use cases committee. High-risk applications (facial recognition, predictive policing) need executive-level approval.
• Example blocking deployment. Microsoft declined to sell facial recognition to police departments without strong regulations. The company cited potential harm and fairness concerns.

A successful governance framework must have genuine decision-making power. Ethics reviews need to be mandatory, well-documented, and capable of halting projects when risks outweigh benefits. Advisory-only structures rarely change outcomes.

Takeaways

Terms like “GDPR-compliant,” “privacy-first AI,” must be more than just marketing labels. To build compliant AI solutions, you need to do the following:

1. Understand regulatory requirements.
2. Implement a privacy-by-design framework.
3. Minimize data collection.
4. Follow the DPIAs.
5. Incorporate ethical governance.
6. Monitor evolving regulations.

Compliance is an ongoing operational discipline.

The fundamental shift is that privacy-first architectures improve AI solutions rather than constrain them. Federated learning enables collaboration across organizational boundaries, something previously impossible due to data-sharing restrictions. Differential privacy allows publishing insights from sensitive datasets that would otherwise remain locked. Homomorphic encryption enables outsourcing computation while maintaining confidentiality.

The window is open. The tools exist. The market rewards early adopters. Building privacy into AI from the start prepares organizations for long-term regulatory, technical, and competitive success.