Maintain the quality and utility of your data with next-generation anonymization that preserves essential details.
Use privacy preserving technology to meet the rigorous standards of GDPR, the EU AI Act, and US NIST guidelines.
Securely train and validate artificial intelligence systems with anonymous high quality data, compliant with GDPR and EU AI Act.
Seamlessly share and utilize data across borders with full compliance under the European Health Data Space framework.
This is a significant achievement. In our study, we could draw the same conclusions from anonymized data as from traditional pseudonymized, individual-level research data.
Strategic Project Lead, BAYER
In AI development, data quality and data privacy are essential.
VEIL.AI next-generation data anonymization, generates anonymous individual-level data meeting the strictest European laws GDPR, EHDS, and EU AI Act. The high level data privacy is also in line with US NIST recommendations. Our technology transforms sensitive data into high-quality datasets, allowing you to train powerful AI models without compromising privacy. VEIL.AI enables you to innovate confidently, providing the data needed for AI-driven healthcare solutions that enhance patient care and streamline clinical workflows, all while mitigating privacy risks.
VEIL.AI technology is not just compliant with GDPR and EHDS – we are a recognized expert in data privacy, GDPR, and privacy-enhancing technologies. Based in Finland, a benchmark country for secondary use of health data and EHDS, our protocol has been accepted by the Finnish Social and Health Data Permit Authority (Findata).
We developed an unique protocol that allows authorities and Data Protection Officers to verify the anonymity of datasets before release. This protocol was crucial in Bayer Future Clinical Trials project, where Findata approved the export of anonymized data to another European country for further research and enrichment in Bayer’s secure environment. With VEIL.AI, you can confidently engage in cross-border health research, knowing your data meets the highest standards of privacy and usability.
Leading organizations trust VEIL.AI for their most sensitive data needs. From global pharmaceutical companies to top-tier research institutions to health data access bodies, our customers rely on us to deliver secure, high-quality data anonymization that drives research, innovation and development.
Accelerate clinical trials and boost Real-World Evidence (RWE) with high-quality anonymized data. Achieve faster clinical study design, and stay compliant with privacy regulations.
Unlock the value of your sensitive data. High quality anonymization can accelerate clinical trial collaboration with industry and enable easier cross-border research collaboration with other research centers.
Gain new capabilities to verify data anonymity and safeguard data privacy. VEIL.AI tools can be used for verification of data privacy and to increase the process efficiency.
From creating external control arms for clinical trials to enabling cross-border data sharing, VEIL.AI’s technology has been proven to deliver results across various industries and use cases.
Global life science company Bayer carried out a four-year multi-million-euro development project called Future Clinical Trials (FCT), which utilized AI-enhanced next-generation anonymization from VEIL.AI. Our unique technology enabled the secure and efficient use of high-quality, anonymized data. For the first time, Findata approved the data as GDPR-compliant, allowing its transfer across borders for further enrichment and innovative research.
This case demonstrates:
VEIL.AI’s expertise is essential in the PHEMS project, which focuses on the secure and compliant sharing of pediatric health data across multiple European countries. By combining VEIL.AI’s next-generation anonymization with federated learning, the project allows hospitals to collaborate on sensitive data without the need to transfer raw data between institutions. This innovative approach ensures data privacy and compliance while enabling valuable cross-border research collaborations in health care.
In the Bayer Future Clinical Trials project, VEIL.AI worked with Findata to establish a groundbreaking protocol for verifying the anonymity of sensitive health data. This collaboration set a new standard for GDPR-free data sharing, allowing Bayer to securely transfer anonymized datasets across borders for global research use, which opens up many new possibilities.
VEIL.AI’s technology is built to provide the highest quality of individual-level anonymized data, ensuring that your data retains its full analytical value while meeting the strictest privacy standards. Our unique blend of AI-driven processes and proprietary technologies goes beyond compliance with regulations like GDPR, EHDS, and HIPAA, delivering data that is both privacy-secure and rich in detail, ready for advanced research and innovation.
Key Benefits of our technology:
VEIL.AI Anonymization Engine surpasses legacy anonymization methods, ensuring markedly superior data quality.
Unlike aggregate approaches, VEIL.AI operates at the individual record level, preserving granular data integrity.
Tailor anonymization to specific variables, significantly accelerating computation speed by several orders of magnitude.
Effortlessly anonymize continuous data streams and complex longitudinal data, simplifying a previously daunting task.
Anonymize data locally and seamlessly combine it later, facilitating secure inter-organization collaboration while maintaining privacy.
As 2024 draws to a close, VEIL.AI celebrates a year marked by innovation, collaboration, and significant milestones that have
As the year draws to a close, it’s the perfect moment to reflect on one of the highlights of
VEIL.AI has been selected to present at the Tech Tour AI for Health event, scheduled for 22-23 January 2025
We believe in the power of collaboration. Our partnerships with leading organizations around the world are a testament to our commitment to advancing data privacy. Together, we are creating new possibilities for the secondary use of sensitive data.
