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Explore an overview of data privacy, industry, and forthcoming AI regulations, examining their intersections in data usage. Delve into the emerging opportunities and challenges, amidst existing risks, and discover how to navigate this intricate regulatory terrain effectively with Apheris.

Every organization using AI or ML in Europe needs to pay attention - the clock is ticking!
Are you prepared for the upcoming EU AI Act? Read our infographic to learn more!

Discover the significance of agility and compliance in today's evolving regulatory landscape and delve into how a federated learning approach, backed by robust governance, can empower organizations to innovatively and securely harness sensitive data for trusted AI solutions.

In the current data-driven landscape, regulations like the EU AI Act are increasingly important due to the growth of AI and global privacy concerns. The Act provides a legal framework for AI risks and applies to businesses, both within and outside the EU. This short ebook argues that preparing for this regulation is not just about compliance but also about leadership and innovation. It will discuss the Act's risk-based approach, the urgency of preparations, and offer insights on achieving compliance through Apheris.

In this ebook, you will:

Learn a little about the EU AI Act and why it's needed
Understand requirements for high-risk systems under the tiered approach of the Act
Why you shouldn't wait to prepare for the regulation
Where Apheris fits and how we can help with preparedness

In a world where data is pivotal, its full potential is often overshadowed by risks. As foundation models proliferate, data emerges as the unique advantage. The challenge? Leveraging it safely.
Our short ebook introduces Apheris' solution for secure computational access to data, connecting federated data sets for ML without the risks of sharing or centralization. A primer for data readiness for federated learning, prioritizing security and privacy.

The EU AI Act marks a crucial step in regulating AI, addressing inherent risks and fostering innovation. Applicable to entities within and outside the EU, this article delves into the EU AI Act's structure, the urgency of preparation, and offers insights into achieving compliance.

A leading data aggregator in healthcare research partnered with Apheris to provide life sciences customers with granular access to patient data while ensuring GDPR compliance. Apheris enabled fully federated machine learning, optimizing the model training process and enhancing customer satisfaction by addressing long-standing GDPR issues. This partnership illustrates Apheris's potential to unlock data assets securely and efficiently.

An industry-leading data-as-a-service (DaaS) provider, operating globally and facing increasing demand for machine learning (ML) capabilities, partnered with Apheris to provide programmatic access to data while ensuring GDPR compliance. Apheris's non-movement of data and computational access governance led to several benefits, including quicker insights for their customers, reduced time and cost on regulatory compliance processes, and increased revenue by offering programmatic access for compliant ML as an added product feature.

Discover the true potential of federated machine learning and analytics, implement it seamlessly with Apheris, and ensure data privacy and security while building cutting-edge ML models.

In this comprehensive guide, you'll explore:
What is federated ML and analytics and why does it matter?
Key stakeholders and practical considerations for successful implementation
Balancing privacy and security requirements in federated machine learning
Uncovering new opportunities and use cases for federated ML and analytics
Putting federated ML into practice and maximizing data value

A detailed look into how data-hungry organizations no longer need to rely on data sharing for collaboration, and can preserve privacy and IP while working across boundaries.

Big or small, organizations the world over are waking up to the value of federated data. In this ebook, we explore:

Federated vs centralized
Examples of federated learning in industry
Common misconceptions around federated learning
Unlocking the power of federated learning
The Apheris approach to federated ML and analytics

Privacy and security considerations are an important part of adopting a federated infrastructure for machine learning and analytics. This whitepaper outlines the various security techniques and privacy controls used by Apheris to safely collaborate and build data applications and AI across organizational and geographical boundaries.

In this whitepaper you will learn about:

The privacy and security landscape, and how Apheris operates within it
Defining attack vectors and threat models as the basis for a privacy and security strategy
Using the 5 Safes as a framework for privacy and security in federated ML and analytics
Practical advice for making the move to a federated data infrastructure

Breakthroughs in healthcare are faster and more reliable with federated data ecosystems. By processing patient data without risking its integrity, data collaboration is safer and more effective than data sharing.
This e-book highlights real-world examples and explains how to implement a federated data ecosystem in pharma and healthcare:

Benefits of federated data ecosystems in pharma and healthcare
Examples in drug discovery, clinical trials and commercial growth
How data collaboration compares with data sharing
How to get started with federated data ecosystems

The world's biggest challenges are going to be solved in Collaborative Data Ecosystems.
Learn how in our free-to-access infographic!

Data collaborations are required if we're going to solve our biggest challenges. This guide discusses challenges with the status quo and provides answers in working with federated data and breaking down barriers while establishing a collaborative data ecosystem.

In this ebook we explore:

What is a Collaborative Data Ecosystem
Challenges with data sharing and why this is not the same as data collaboration
The concept of complementary data value
Establishing a Collaborative Data Ecosystem and working with federated data
Unlocking data's potential with Apheris

The world's biggest challenges are going to be solved in Collaborative Data Ecosystems.

Learn about the most important facts in our infographic!

What are Collaborative Data Ecosystems
The three most important roles in Collaborative Data Ecosystems
The types of capabilities organizations need today to collaborate securely on data
Why collaboration and deeper complexity of data sharing gives organizations a competitive advantage

Learn how to leverage federated and sensitive data at scale and how you can collaborate with your partners to advance and evolve the practice of life sciences.

In this case study you will learn:

How two pharma companies use protein-ligand interaction data and AI models to predict target binding of novel compounds
How a pharma company fueld their drug discovery activities by leveraging molecule data from their suppliers
How an AI-focussed health tech company developed AI models to improve decision-making processes for cancer treatments

We need to make biomedical data easily accessible and securely usable, to find new medicines and diagnostics, and to establish data-based insights that evolve the practice of medicine.

In this case study you will learn:


How a MedTech company built up the capability for reliable and strategic data access to thousands of annotated MRI scans from several hospitals
How a data science team in a pharma company collaborated with hospitals and institutions to securely leverage cancer patient data for AI
How a genomics company analyzed genomics data from laboratories at scale

Learn how multiple companies across a manufacturing value chain have securely collaborated on data while protecting the IP of any party, data, or model.

In this case study you will learn:

How a coating supplier increased output quality to consistently meet the OEM's targets
How an Industrial Solution Provider developed AI-features for its immersion heaters and implemented them at his customers' environment
How several automotive suppliers for high-performance plastics parts collaboratively developed AI models to predict material behavior

Joining complementary data sets creates stronger machine learning capabilities. But valuable data containing intellectual property cannot be shared and must adhere to regulatory frameworks. Healthcare companies can use federated learning to work with decentralized data, allowing secure collaboration without sharing data.

In this white paper, you will learn:

About vertically distributed data
Workflow of privacy-preserving data science
Deep-dive into privacy-preserving record linkage
Deep-dive into federated and privacy-preserving analytics

This case study shows the effectiveness of Apheris’ platform and services for data science on data that is not directly accessible and distributed. Federated learning decentralizes learning algorithms to access multiple data sets, maintaining privacy. Without pooling sensitive data, new insights can be uncovered securely.

In this white paper, you will learn:

How federated learning, privacy testing and other tools that enable data science on not directly accessible and distributed data
Sowcase of federated learning, privacy testing and other tools that enable data science practices on not directly accessible and distributed data, at the example of QSAR models.

In recent years, Deep Learning has gained considerable traction in many fields, but none more so than in the realm of drug discovery. Applications range from generating de novo hit-like molecules, predicting drug-disease associations and activity, toxicology estimation, to the analysis of medical images.

In this white paper, you will learn:

How you can preserve intellectual property while generating value from sensitive data
Deep dive into federated and secure QSAR models
How Apheris prevents uncontrolled data sharing
How you can pave the way to precision medicine with federated and secure QSAR model training

Secure data collaborations with multiple parties are the next source of getting a competitive edge. However, only a few data leaders know what to look for to get projects going. This buyer's guide will help you evaluate platforms and highlight considerations for AI and analytics across organizational boundaries.

In this guide you will learn:

What is a Platform for Secure Data Collaboration
Why do you need it today
What should you look out for when choosing a platform

DevOps and MLOps are common methodologies in every company that wants to become software and data science driven by weaving AI into the core fabric of their business. Read what is required to securely collaborate with partners on data and AI at scale.