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Building for EU AI Act compliance

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:

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

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Privacy and Security Whitepaper

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:

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

Reaching universal agreement on how to regulate AI is impossible. But a carefully designed cyclical process can lead to efficiencies in an enterprise's journey towards trustworthy, accountable, and sustainable AI. In this blog, I navigate the regulation space through the eyes of a data custodian.
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Customers do more with data through programmatic access for compliant ML workloads

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.
White Paper

Privacy and Security Whitepaper

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:

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