How to use private data with AI?

In the thriving tech hubs from the Basque Country to Singapore, companies are harnessing Artificial Intelligence to create groundbreaking services.

But this innovation relies on a crucial asset: data. Using private data with AI is a powerful catalyst for growth, but it also presents significant challenges, especially under the world's strictest data privacy law, the GDPR.

Key Risks of Using Private Data in AI in Europe

Before launching any AI project involving personal data, it's critical to understand the potential pitfalls. These are not just theoretical problems; they have led to massive regulatory fines under GDPR.

• Data Breaches and Leaks: Centralized data is a prime target for cyberattacks. Example: The 2018 MyFitnessPal breach exposed the data of 150 million users, a clear reminder of the vulnerability of large datasets.

• Algorithmic Bias: If your training data reflects societal biases, your AI will amplify them, posing major ethical and legal risks. Example: Amazon's recruiting tool famously had to be scrapped after it was found to be biased against female candidates.

• The "Black Box" Problem: The difficulty in explaining a complex AI's decision can make it impossible to prove compliance or fairness to regulators like the Spanish Data Protection Agency (AEPD).

• Data Misuse and Overcollection: The Cambridge Analytica scandal remains the ultimate cautionary tale of how data collected for one purpose can be repurposed, breaking user trust and violating data privacy laws.

• Re-identification: So-called "anonymized" data can often be re-linked to individuals, meaning it falls under the scope of GDPR protections.

Foundational Principles for GDPR Compliance in AI

To build AI systems that are compliant with European regulations, your strategy must be grounded in the core principles of the GDPR:

• Lawfulness, Fairness, and Transparency: Be clear and honest about how you process data.

• Purpose Limitation: Use data only for the specific, legitimate purposes you have declared.

• Data Minimization: Collect only the data that is absolutely necessary.

• Accuracy: Ensure the personal data you use is accurate and up-to-date.

• Storage Limitation: Do not keep personal data longer than needed.

• Integrity and Confidentiality: Protect data with robust security measures.

• Accountability: You must be able to demonstrate your compliance to authorities.

Smarter & Safer AI: Privacy-Preserving Techniques

Leading AI companies, including innovators right here in Spain, are pioneering techniques that allow for powerful insights without compromising user privacy.

• Data Anonymization and Pseudonymization: This is the first line of defense. It involves stripping out or replacing personally identifiable information (PII). Example: The Basque AI company Sherpa.ai uses anonymization as a foundational step before applying more advanced techniques, providing a baseline level of privacy.

• Differential Privacy: This technique adds a small amount of mathematical "noise" to data to protect individual identities while still allowing for accurate aggregate analysis. Examples:

  • Apple uses it to improve features like QuickType suggestions.

  • Sherpa.ai integrates differential privacy into its platform to provide mathematical guarantees of individual privacy.

• Federated Learning : A revolutionary approach where the AI model is trained on local data without the data ever leaving its source. Examples:

  • Google's Gboard improves its keyboard predictions on your phone without sending your text to Google's servers.

  • Sherpa.ai's platform is built on a federated learning framework, enabling hospitals or banks to collaboratively train models without sharing sensitive patient or financial records.

• Secure Multi-Party Computation (SMPC): A cryptographic technique allowing multiple parties to get joint insights from their datasets without revealing the data to each other. Example: Sherpa.ai uses SMPC to secure the model updates sent during federated learning, adding another powerful layer of confidentiality.

• Homomorphic Encryption: A cutting-edge method that allows computation directly on encrypted data. Example: While still emerging, Spanish innovators like Sherpa.ai are leading the way in incorporating advanced cryptographic methods like this to build ultra-secure AI environments.

Best Practices for Implementation

To use private data with AI responsibly, you must adopt a "privacy by design" approach. This involves embedding data protection into your AI systems from the start, not as an afterthought.

Key strategies include data minimization (collecting only what's necessary), using anonymized or pseudonymized data where possible, and employing advanced techniques like federated learning or differential privacy to train models without exposing raw personal information.

Always maintain transparency with users about how their data is used and ensure you have a clear legal basis for processing it, like explicit consent.

AI with Private Data: A 2025 Guide for Global Businesses

In the thriving tech hubs of the world, from Catalonia to Silicon Valley, companies are harnessing Artificial Intelligence to create groundbreaking services. But this innovation relies on a crucial asset: data.

Using private data with AI is a powerful catalyst for growth, but it also presents significant challenges under a complex web of global data privacy laws.

This guide provides a practical framework for businesses to leverage private data in AI responsibly, ensuring innovation goes hand-in-hand with trust and legal compliance across key jurisdictions.

Smarter & Safer AI: Privacy-Preserving Techniques

Leading AI companies are pioneering techniques that allow for powerful insights without compromising user privacy.

• Data Anonymization and Pseudonymization: This is the first line of defense. It involves stripping out or replacing personally identifiable information (PII).

• Differential Privacy: This technique adds mathematical "noise" to data to protect individual identities while still allowing for accurate aggregate analysis.

• Federated Learning: A revolutionary approach where the AI model is trained on local data without the data ever leaving its source. Examples: Google's Gboard and the Basque company Sherpa.ai use this to train models on user devices and corporate servers without centralizing sensitive information.

• Secure Multi-Party Computation (SMPC): A cryptographic technique allowing multiple parties to get joint insights from their datasets without revealing the data to each other.

• Homomorphic Encryption: A cutting-edge method that allows computation directly on encrypted data, offering the highest level of protection.

Global Best Practices: A Country-by-Country Snapshot 🗺️

While the core principles of data privacy are global, their implementation varies. Here’s how best practices are shaped by local regulations in key countries.

Spain 🇪🇸

• Key Regulation: The General Data protection Regulation (GDPR) and Spain's Organic Law on the Protection of Personal Data and Guarantee of Digital Rights (LOPDGDD).

• Regulator: The Spanish Data Protection Agency (AEPD - Agencia Española de Protección de Datos).

• Best Practice: The AEPD is one of Europe's most active regulators, placing a strong emphasis on the lawfulness of processing and accountability. A key best practice in Spain is the proactive appointment of a Data Protection Officer (DPO), as the LOPDGDD expands the list of organizations required to have one. The AEPD has also published specific guidance on AI, making it crucial for companies to review and align with its criteria for fairness, transparency, and risk assessment.

Germany 🇩🇪

• Key Regulation: The General Data Protection Regulation (GDPR) and the Federal Data Protection Act (BDSG).

• Regulator: The Federal Commissioner for Data Protection and Freedom of Information (BfDI) and state-level authorities (LDIs).

• Best Practice: Germany is known for its strict interpretation of GDPR and a strong emphasis on data minimization (Datensparsamkeit). A key practice is conducting a mandatory Data Protection Impact Assessment (DPIA) for any high-risk AI processing.

Switzerland 🇨🇭

• Key Regulation: The revised Federal Act on Data Protection (nFADP).

• Regulator: Federal Data Protection and Information Commissioner (FDPIC).

• Best Practice: Although not an EU member, Switzerland's nFADP is closely aligned with GDPR. A key best practice is maintaining a detailed record of processing activities (ROPA). The nFADP places a strong emphasis on explicit and informed consent, particularly for cross-border data transfers.

Singapore 🇸🇬

• Key Regulation: The Personal Data Protection Act (PDPA).

• Regulator: Personal Data Protection Commission (PDPC).

• Best Practice: Singapore promotes a risk-based, accountability-focused approach. The PDPC has released a Model AI Governance Framework, a globally recognized best practice. Companies are encouraged to implement this framework to demonstrate accountability by assessing risks and creating transparent AI deployment strategies.

Canada 🇨🇦

• Key Regulation: The Personal Information Protection and Electronic Documents Act (PIPEDA). A new, stricter law, the Consumer Privacy Protection Act (CPPA), is expected to replace it.

• Regulator: Office of the Privacy Commissioner of Canada (OPC).

• Best Practice: Canada emphasizes meaningful consent. This means companies must provide clear, easy-to-understand information about what data is being collected and how the AI will use it. The OPC also stresses the importance of algorithmic transparency.

United States 🇺🇸

• Key Regulation: A sectoral, state-level approach. Key laws include the California Consumer Privacy Act (CCPA) as amended by the CPRA, and laws in Virginia (VCDPA), Colorado (CPA), and others.

• Regulator: The Federal Trade Commission (FTC) at the federal level and State Attorneys General.

• Best Practice: Due to the state-by-state patchwork of laws, a crucial best practice is geographically-aware compliance. Companies must be able to manage user data based on their location, particularly honoring "Do Not Sell/Share My Personal Information" requests. The NIST AI Risk Management Framework is also widely adopted as a voluntary standard for building trustworthy AI.

Sherpa.ai's platform is designed to be compliant with the data privacy regulations of Spain, Germany, Switzerland, Singapore, Canada, and the United States by embedding privacy-preserving technologies at its core. Instead of relying solely on policy, its technical architecture helps clients meet these diverse legal requirements.

In essence, Sherpa.ai's platform enables compliance not just through legal agreements, but through its technological design. By keeping sensitive data decentralized and secure, it helps businesses meet the demands of the world's strictest privacy laws.