How to Train Your AI Model While Staying Compliant

AI models trained on first-party data are becoming increasingly important, driving improvements in personalization, automation, and decision-making. As companies increasingly rely on AI models trained on first-party data, ensuring AI compliance has become a key priority.

Data protection regulations such as the General Data Protection Regulation (GDPR) and the European Union's AI Act (EU AI Act) impose strict requirements on AI development, data collection, and model training.

Failure to comply with these regulations can lead to significant financial penalties, reputational damage, and a loss of consumer trust.

In this article, we explore the importance of first-party data for AI training, key legal considerations, and best practices for ensuring compliance with the GDPR and the EU AI Act.

1. Why First-Party Data Is Crucial for AI Training

First-party data is information collected directly from customers, users, or business interactions. Unlike third-party data, which is compiled from external sources, first-party data offers a number of advantages:

• Higher accuracy — While third-party sources may contain outdated or irrelevant information, first-party data comes directly from customers, ensuring its reliability for AI training.
• Better compliance — Companies have more control over data collection, storage, and processing, which reduces regulatory risks. This direct control allows companies to implement strong data governance frameworks and maintain clear audit trails for compliance purposes.
• Improved data protection control — By sourcing data directly, companies can implement strong security measures and transparency protocols tailored to their specific needs.

Companies use first-party data for AI training in many areas, benefiting from its accuracy and compliance advantages.

For example, AI models can analyze first-party data to predict customer behavior and improve customer retention. Similarly, companies use AI to tailor recommendations based on first-party behavioral data. And in fraud detection, AI systems trained on transaction data can identify suspicious patterns.

2. Key Legal Considerations for Using First-Party Data in AI Training

Under the GDPR, companies must establish a lawful basis for processing personal data for AI training.

This can include:

• Legitimate interest — AI training that benefits both the company and the consumer while minimizing data protection risks.
• User consent — Explicit consent from users for data processing.
• Contractual necessity — AI-driven services that require data processing to fulfill contractual obligations.

Transparency is another cornerstone of GDPR compliance requirements. Companies must inform users about how their first-party data is used in AI training. This includes clearly stating the purpose of data collection, detailing how long data is stored, and explaining any potential risks associated with data use.

Understanding user rights is also crucial. Data subjects have several rights regarding their personal data, including the right of access to their personal data held by an organization, the right to rectification of inaccurate or incomplete personal data, and the right to object to data processing under certain circumstances, particularly when it comes to direct marketing or profiling.

In addition, companies must comply with GDPR principles such as data minimization, which states that only the data strictly necessary for training the AI may be collected and processed. Another principle is purpose limitation, which ensures that collected data is used only for its intended purpose and is not further processed in ways incompatible with that purpose.

Finally, Article 22 of the GDPR restricts fully automated decisions (e.g., AI approving or rejecting loan applications) unless there is explicit user consent, legal safeguards such as human oversight are in place, or the decision is necessary for a contract.

These obligations serve as guidelines for companies to handle first-party data responsibly when developing AI models.

3. The Impact of the EU AI Act on Using First-Party Data to Train AI Models

The EU AI Act introduces strict requirements for AI models trained on first-party data. The Act establishes a risk-based approach to regulating AI, categorizing AI models into four risk levels:

• Prohibited AI (e.g., social scoring) is completely banned.
• High-risk AI (e.g., financial risk assessments) requires strict documentation, human oversight, and transparency.
• Limited-risk AI (e.g., chatbots) must disclose AI-generated content.
• Minimal-risk AI (e.g., spam filters) is not subject to additional obligations.

For first-party data, companies must implement robust data governance, bias mitigation measures, and risk assessments before using it to train AI.

They must also ensure continuous monitoring to detect discriminatory outcomes. Unlike the GDPR, which focuses on data protection, the EU AI Act regulates the fairness, accuracy, and ethical use of AI.

Non-compliance can result in fines of up to 35 million euros or 7% of a company's global annual revenue.

To stay compliant, companies should integrate privacy-preserving techniques, rigorous documentation practices, and AI governance frameworks when training models with first-party data.

To learn more about complying with the EU AI Act, read the 5 steps startups should take to become compliant, or simply use our AI compliance solution.

To find out how to train your AI model with first-party data while complying with both the GDPR and the EU AI Act, read on.

4. How to Train AI Models With First-Party Data While Staying Compliant

4.1 Understand Your First-Party Data Sources

Before training AI models, you need to assess and categorize your first-party data to ensure compliance with the GDPR and the EU AI Act. Knowing what type of data is collected helps you determine the legal bases for processing and the necessary safeguards.

To understand your first-party data, proceed as follows:

1. Identify which data is personal and which is not — First, check whether the data contains personal data (e.g., names, email addresses) or sensitive data (e.g., health data, biometric data). Then classify the data to understand its risk level under the GDPR and the EU AI Act's risk-based framework.
2. Define data collection methods — Next, make sure the data is collected with valid consent or on an appropriate legal basis (e.g., contractual necessity, legitimate interest).
3. Assess data storage and access controls — Finally, implement role-based access controls (RBAC) to restrict access to sensitive data and ensure that data is stored in compliance with data sovereignty laws (e.g., EU-based servers for GDPR compliance).
   
    

4.2 Use Privacy-Compliant Data Collection Techniques

Collecting raw user data increases compliance risk. Privacy-enhancing technologies (PETs) enable AI training while preserving user anonymity and security.

With differential privacy, for example, controlled noise is added to datasets so that trends can be extracted without revealing the identity of individuals. Apple uses differential privacy for AI-based user analytics without storing personal data.

Federated learning is another privacy-preserving technique in which raw data is not centralized; instead, AI models can be trained across multiple decentralized devices. For example, Gboard, Google's keyboard app, learns typing patterns without uploading private text input.

Where possible, use anonymization and pseudonymization to protect personal data while still allowing AI models to extract useful insights.

Use pseudonymization when re-identification is required. This technique replaces personal identifiers with unique but reversible codes. For example, an AI finance model could replace customer names with unique IDs while keeping a key for re-identification.

In cases where re-identification is not required, use anonymization. Anonymization removes all identifiers so that data can never be linked to an individual. For example, you can aggregate customer data for trend analysis without tracking individual users.

By applying these privacy-preserving techniques, you can strike a balance between leveraging valuable data for AI model training and protecting user privacy.

4.3 Implement the Principles of Data Minimization and Purpose Limitation

Under the GDPR, companies must ensure that they collect and process only the minimum amount of data required for a specific, well-defined purpose.

So avoid collecting data "just in case" — instead, clearly document the business need for each dataset. For example, instead of collecting full customer profiles, an AI recommendation system may only need purchase history.

Likewise, AI training data should not be retained longer than necessary. Use automated deletion policies to remove outdated datasets.

To further improve compliance with the data minimization principle, consider using Secure Multi-Party Computation (SMPC). This technique enables joint data analysis without directly sharing raw data. Different companies can jointly compute results while protecting their individual data. For example, banks can collaborate on AI models for fraud detection while protecting customer data.

4.4 Store and Process Data Securely

AI training data must be stored and processed securely to prevent unauthorized access, breaches, or compliance violations. Ensuring the security of first-party data during AI model training is therefore crucial.

Here's how to secure your data storage:

1. Implement zero-trust security architectures — This approach requires continuous authentication for users accessing AI training datasets. It minimizes the risk of unauthorized access by enforcing least-privilege access rights.
2. Use homomorphic encryption — This encryption protocol allows AI models to process encrypted data without decrypting it.
3. Use encryption for data at rest and in transit — Secure your data through encryption, e.g., with AES-256 for stored data and TLS 1.2+ for data transmission.

4.5 Transparent Data Governance and Documentation

AI governance ensures compliance with both the GDPR's documentation requirements and the EU AI Act's transparency obligations, which require companies to keep detailed records of data processing activities such as collection methods and storage practices.

For this reason, you should keep careful records of the origin of all data used for AI training. This includes stating how data is collected, processed, and stored to ensure that all actions are traceable. Make sure the documentation of your AI models includes dataset sources and preprocessing methods. With AI compliance software like heyData's AI solution, which automates compliance documentation, you can generate automated compliance reports, reduce administrative overhead, and ensure your company is always audit-ready. The software creates and stores pre-filled compliance reports in line with EU AI Act requirements that can be easily accessed for audits.

Also, establish comprehensive policies for user consent and opt-out mechanisms for AI training. Clearly inform users about how their data is used and offer them simple ways to withdraw their consent if they wish. This approach protects users' rights while facilitating the responsible use of data.

4.6 Monitor Compliance Continuously

AI compliance is not a one-time exercise — regulatory requirements evolve, and AI models must be reviewed regularly to ensure ongoing compliance.

Conduct regular AI risk assessments to identify potential bias, discrimination, or security risks in AI model outputs. This includes third-party audits of AI decision-making processes, identifying vulnerabilities, and implementing corrective measures to address them. AI compliance solutions like heyData's can automate this process by comparing your AI's functions against legal definitions and classifying your system accordingly. Automated classification saves you time and takes the guesswork out of risk assessment.

It is important to stay up to date on regulatory developments, especially the GDPR and the EU AI Act, as changes may require you to adapt your AI model to comply with evolving legal frameworks and ensure that data protection and ethical considerations always take priority. And since many companies lack in-house expertise in AI governance and regulatory compliance, our team of experts at heyData is happy to support you with interactive trainings for your teams and ongoing compliance support.

Conclusion

Balancing AI innovation with data protection and compliance is essential for sustainable growth. AI compliance is crucial for companies looking to train AI models with first-party data while complying with the GDPR and the EU AI Act.

By implementing these best practices, you can train your AI model with first-party data while fully complying with these regulations.

AI compliance software like heyData's plays an important role in automating risk assessments, generating audit reports, and ensuring regulatory compliance for companies that want to train their AI models with first-party data while staying compliant.

As companies increasingly embrace AI and first-party data, a proactive approach to compliance will be key to achieving both innovation and regulatory adherence.