Compliant AI use under EU AI and Data Act – practical implementation

Converting sensitive data into anonymized data

AI is already impacting how we automate tasks and make better decisions across many industries. Data is the oil and although large language models trained on publicly available data are very popular, some of the more important problems require data that are proprietary and sometimes highly confidential and regulated.

The Partisia Platform is developed to address data confidentiality but also transparency into the use of data. These properties address both data regulations like GDPR and the core requirements in the EU's AI Act and Data Act.

Real control and privacy of data is paramount for individuals and organizations to harness the tremendous benefits of AI without losing control of the very data that defines us.

Kurt Nielsen · Chief Executive Officer, Partner

R&D focal points

Shaping the future of AI on encrypted data

Explore our R&D efforts and witness the transformative power of AI in shaping the future of technology.

Transparency and control

Regulating AI is a global concern — poorly generated content can sway elections, public opinion, and personal decisions about health and finance. Knowing how the underlying AI was trained is crucial. Striking the balance between transparency and privacy is core to the Partisia Platform.

Separating training and inference

Trained AI models can be applied directly via the Partisia Platform so that user data needed to generate a precise response stays encrypted at all times — without compromising transparency into the applied model.

Anonymizing sensitive data

Partisia's Confidential Computing product converts sensitive data into anonymized data usable in clear text for training, building on more than eight years of work with trusted custodians like Statistics Denmark and the Danish Healthcare Data Authority.

Federated machine learning

A privacy-enhancing computational principle built into Confidential Computing — send the algorithm to the data instead of the other way around, reducing the central MPC overhead significantly.

Next-generation MPC protocols

Initial work on MPC-based neural networks and models that off-load heavy computation to one of two confidential computing parties are promising candidates for the next generation of MPC-based AI.

Compliance by design

The platform is built to address GDPR alongside the EU AI Act and Data Act — making compliant, large-scale AI use practical, not aspirational.

Shaping the future of technology

Discover Substance through our dynamic R&D projects, strategic partnerships, and innovative product sketches. Our relentless pursuit of excellence drives groundbreaking advancements, shaping the future of technology.

We are working on some very exciting projects and global partnerships in 2025. Explore our R&D .

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Compliant use of AI - implementing the EU’s AI and Data Act

Converting sensitive data into anonymized data

AI is already impacting how we automate tasks and make better decisions across many industries. Data is the oil and although large language models trained on publicly available data are very popular some of the more important problems require data that are proprietary and sometimes highly confidential and regulated.

The Partisia Platform is developed to address data confidentiality but also transparency into the use of data. These properties address both data regulations like GDPR and the core requirements in the EU’s AI act and Data act.

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“Real control and privacy of data is paramount for individuals and organizations to harness the tremendous benefits of AI without losing control of the very data that defines us.

Kurt Nielsen Chief Executive Officer, Partner

R&D focal points

Explore our R&D efforts and witness the transformative power of AI in shaping the future of technology.

01

Regulating the use of AI is a concern for both professionals in the field as well as regulators around the globe. Controlling AI is a major concern to avoid that poorly generated AI content impact decisions from elections, public opinions and personal decisions that may impact both the individuals economy and health condition. Ensuring transparency is a key tool as knowing how the underlying AI algorithms and how they were created is crucial to assessing the true value. For AI trained and applied on confidential data, privacy is equally important. Striking this balance is a core part of the Partisia Platform.

02

Separating the training from the use of AI models is crucial. To ensure transparency, it is important to keep a record of the applied AI model, which is hard if training and using part of the same process. Trained AI models can be directly applied using the Partisia Platform to ensure that the users’ private data needed to generate a precise response are kept encrypted at all time without compromising transparency into the applied AI model.

03

Converting sensitive data into anonymized data that can be used in clear text to train AI models is a great and ongoing challenge. Partisia’s Confidential Computing product is designed for this purpose and builds on more than 8 years of work with some of the most trusted data custodians like Denmark Statistics and the Danish Healthcare Data. Continuous R&D effort is needed to improve and expand on this approach to data security.

04

Federated Machine learning is a promising privacy enhancing technology and computational principle built into Partisia’s Confidential Computing product. The basic idea is that some problems can be solved by sending the algorithm to data as opposed to sending all data to the algorithm. For some AI models, this reduced the required central computation as most of the heavy computation can be done locally. Hereby the computational overhead from using MPC to secure the “central computations” is reduced significantly. Continuous R&D effort is needed to improve and expand on this approach to data security.

05

The most critical R&D effort required for using MPC in large-scale AI training is new innovative protocols. Initial work on MPC based neural network and MPC models that support off-loading of heavy computations to one of two confidential computing parties are promising candidates for the next generation of MPC based AI.

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Shaping the future of technology

Discover Substance through our dynamic R&D projects, strategic partnerships, and innovative product sketches. Our relentless pursuit of excellence drives groundbreaking advancements, shaping the future of technology.

R&D Projects, Partnerships and sketched products

More information to come. We are working on some very exciting projects and global partnerships in 2025.

Learn more

Why work with Partisia?

Partisia was founded in 2008 by global pioneers within Multi-Party Computation and advanced cryptography. While our core mission is to integrate Privacy Enhancing Technologies with the aim of improving decision-making and product development, we also pride ourselves on being one of the best in the industry.

We are an innovative software company and a trusted partner empowering companies to operate and compute encrypted data. Providing a platform where data from individuals, governments and private companies are able to stay encrypted and protected, and still fully enabled, creating the perfect balance between transparency and privacy. Choose Partisia and get a partner based on expertise and know-how, but most importantly trust.

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