The VEIL.AI Anonymization Engine solves health data privacy problems. Utilize high-quality, individual-level anonymized and synthetic health data. 

Personal health data is very valuable and can also be used for research, innovation and development. However, secondary use of health data is only possible if the data is of high quality and privacy protection can be secured.

Introducing the VEIL.AI Anonymization Engine, an AI-based solution that transforms sensitive health data into high-quality, invidual-level anonymized and synthetic data that is GDPR- and HIPAA compliant. This offers great new opportunities to utilize health data for Pharma and MedTech companies, Hospitals and Researchers, and all organizations that want to use health data securely for secondary use purposes. 

VEIL.AI method vs. Old state-of-the-art methods

Did you know that there is not only one way to anonymize a dataset? There are actually an astronomical number of ways to do it. So how can you get the best anonymization results for your specific use case?

During our years of working with health data de-identification we realized that the quality of anonymized data was not ideal. Old state-of-the-art anonymization methods lose a significant part of the most interesting and relevant data in the anonymization process. Therefore, you cannot draw the same conclusions from such a dataset compared to the original data.

This is why we created next-generation anonymization.

With the VEIL.AI Anonymization Engine you can control and customize the anonymization process to optimize quality. Artificial Intelligence searches for the optimal anonymization result for your use case. And you can draw the same conclusions from the next-generation anonymized dataset as from the original data.

Our Solutions


We specialize in health data anonymization, synthetic data and pseudonymization. Our unique (patented) technology helps  life science and healthcare stakeholders utilize sensitive data in ways that were not possible before.

Next-generation Anonymization

Combine and anonymize sensitive data from multiple parties in real-time with unmatched scalability. Invidual-level data anonymized with VEIL.AI outperforms the competition in utility, quality, and performance.



Utilize synthetic data to gain real world insights or use synthetic data for realistic training and testing purposes. Hospitals and pharma companies can use synthetic data for e.g. AI algorithm development.

Pseudonymization & 

consent management

Achieve compliance (GDPR, HIPAA, etc.) with sensitive data. We also offer pseudonymization, data minimization and consent management services for, for example, hospitals and biobanks.

What do our customers say?

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.

Jussi Leinonen,

Strategic Project Lead


Bayer’s “Future Clinical Trials” project utilizes VEIL.AI’s next-generation anonymization

Global life science company Bayer carried out a three-year multi-million-euro development project called Future Clinical Trials (FCT), which utilized AI-enhanced next-generation anonymization technology from VEIL.AI.

This case study demonstrates:

  • How anonymized data can be of such a high quality that the same conclusions can be drawn from it, as from traditional pseudonymized, individual-level research data
  • How sensitive data can be anonymized, after which it can be transferred to another country to be enriched and further utilized
  • How anonymization enables reuse of organization’s Legacy data
  • What were the steps to build a synthetic control arm for a clinical trial

Utilization of anonymization techniques to create an external control arm for clinical trial data

Download the research on "Utilization Of Anonymization Techniques To Create An External Control Arm For Clinical Trial Data"

VEIL.AI designed a study together with our partners Bayer and MedEngine to compare the use of anonymized and pseudonymized Real-World Data (RWD) in creating external control arms (ECAs) for single-arm randomized controlled trials (RCTs) .

The study, published in BMC Medical Research Methodology, showed that one could draw the same conclusions from anonymized data as from traditional pseudonymized, individual-level research data.

VEIL.AI makes sensitive data safe to use

What are the main differences between sensitive data, pseudonymized data, anonymized data and synthetic data? 

Since GDPR does not apply to anonymized or synthetic data, using these methods opens up new possibilities for using health data in novel ways.

Thanks to VEIL.AI’s unique technology, you can now get better access to health data and easily combine, analyze and share data in more use cases than were possible before.

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