High-quality data for AI development
Privacy risks, regulations, and limited access to sensitive data slows down innovation.
We unlock the full potential of your data — by means of advanced anonymization or synthetic data - without compromising privacy
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Data for AI : key challenges
Using sensitive data in AI development comes with major challenges. With VEIL.AI BONSAI, you can turn your sensitive data into a safe, compliant, and high-utility resource — ready for AI innovation and collaboration
Privacy Risks
Personal information can be exposed or misused, leading to legal and reputational damage.
Regulatory Barriers
Strict data protection laws (EU AI Act and GDPR) limit access to and use of real-world data.
Limited Data Sharing
Organizations hesitate to share and use valuable datasets due to confidentiality concerns, which hampers innovation.
Biases & Incompleteness
Access restrictions can result in smaller, less representative datasets, harming AI performance.
VEIL.AI BONSAI
Unlock safe, high-quality data for AI — INSTANTLY
With BONSAI, you can create high-quality anonymized and synthetic datasets for AI use — fully compliant with privacy regulations. Every dataset comes with automated quality and privacy reports for full transparency and trust.
✅ Comply effortlessly with the EU AI Act and GDPR
✅ Access safe, privacy-preserving data for AI training and other AI use
✅ Build comprehensive, high-quality anonymous datasets
✅ Collaborate securely with different organizations
Available as a customer-hosted, VEIL.AI-hosted, Saas solution, also on Snowflake Native App, it’s easy to procure, integrate, and scale within your existing data pipelines — ensuring privacy, compliance, and speed.
VEIL.AI BONSAI opens up new possibilities
Using sensitive data in AI development comes with major challenges. With VEIL.AI BONSAI, you can turn your sensitive data into a safe, compliant, and high-utility resource — ready for AI innovation and collaboration
Model performance tied to data quality
Rich, diverse, compliant datasets drive better AI outcomes.
Privacy limits data access
Anonymization unlocks sensitive real-world data for AI.
Need for broader data sources
Cross-institutional collaboration requires anonymization.
Compliance pressure
High-quality anonymized data ensures fairness, transparency, and regulatory compliance.
Use Case: Enabling Privacy-Compliant AI Research and Innovation at SCALE
Challenge
A research hospital aims to unlock the potential of its extensive healthcare data for AI-driven research and innovation — while fully complying with strict data protection regulations, such as GDPR, the EU AI Act, and the European Health Data Space (EHDS).
Solution
To enable safe, scalable, and regulation-compliant use of structured patient data, the organization initiated a project to build a privacy-first data infrastructure. Advanced anonymization technology was selected to transform sensitive datasets into high-quality, anonymized formats that maintain data utility for research and AI development.
- Objective: Build a high-quality anonymized dataset from different therapy areas, including hundreds of thousands of patient records.
- Scope: Anonymized datasets are used for AI development, machine learning, research, and healthcare innovation.
- End-Users:
- Global MedTech companies leveraging anonymized data for product development and innovation.
- Research groups and academic institutions using data for clinical and scientific research.
- Technology companies developing AI models and large language models (LLMs) based on real-world anonymized health data.
Impact
The initiative ensures that sensitive health data can be securely accessed and used for groundbreaking research and AI innovation — without compromising privacy, patient rights, or regulatory compliance. It creates a trusted foundation for collaboration across healthcare, research, and industry sectors.