Private, secure, governed

With Apheris' zero-trust approach you always stay in full control of your data and models.

Powering your data infrastructure

Securely connect data across boundaries

Apheris powers your data infrastructure with federated ML and analytics offering unprecedented ways to securely connect data across boundaries.

Ensuring security and privacy

Data and IP protection is at the core of Apheris. Our platform is designed around exceptionally high standards for security, privacy and governance.

Enterprise-grade security

Apheris supports the highest requirement to network security design and security protocols. You don’t need to move data you and you always stay in full control of your data and IP.

Access security

Restrict and monitor all authorized users:

  • User authentication with multi-factor authentication
  • Fine-grained data access policies
  • User permissions / roles
  • Enterprise single sign-on (SSO)

Data security

Processes and structures within the platform to safeguard sensitive data:

  • Federated infrastructure
  • Segregated users, projects and data
  • User role-based access to data
  • Encryption of data
  • Data export control

System security

Maintain and monitor the system to ensure performant and secure operations:

  • Disaster recovery
  • Network security
  • Backups
  • Data isolation from compute environment
  • Strict isolation between different tenants
  • Audit logging
  • Vulnerability scanning & penetration testing
  • Cloud Security
  • Physical & environmental security
  • Resiliency
  • Hardware virtualization

Certifiably secure

Apheris is ISO-27001 certified and meets requirements from HIPAA, GDPR and more.

Always protect data and IP and manage privacy risk

Apheris’ modular approach to privacy enables you to set the right level of privacy while maximising the analytical value of data.

Privacy controls on the computational level

Control access and privacy of your data via fine-granular asset policies:

  • Raw data never leaves your environment
  • Code audit (human-in-the-loop) functionality to assess custom code and enforce only audited code
  • Approval mechanisms for computations and controls for returning results

Modular Privacy Enhancing Technologies (PETs)

Apheris offers the benefits of various PETs in a suite of business-ready solutions:

  • Privacy for statistics – bounding, rounding and differential privacy
  • Adversarial attack testing – inference and reconstruction attacks
  • Secure aggregation during federated learning
  • Support for differentially private ML
  • Support for cryptographic approaches

Compliance across privacy regulations

Govern your data in compliance with GDPR, HIPAA, CCPA or more:

  • Federated infrastructure - data doesn’t need to move
  • Audit logging
  • Full compatibility with on-premises systems and data
  • Geography-specific cloud environments
  • Support for DPIAs and compliance audits
  • GDPR compliant

Oversight in your data, workflows, and collaborations

The Apheris platform provides you full oversight for all your data, workflows, and collaborations – reducing risk, costs and supporting compliance audits.

Governance: oversight and full control of your data and IP, always

Manage your data collaborations via the 5 safes frameworks and securely access distributed data.

Safe people

Data access and usage is restricted to eligible users and all actions are traceable. Only accredited users are able to login to the Apheris platform with defined roles coupled to appropriate user rights. The logging system enables granular activity tracking for full auditability of which computations were run on which dataset by which user.

Safe projects

Collaborating parties leverage a transparent process for data access, being clear for which purpose they are using the data and resulting models. Traceable activities and computation results for participants and auditability of data usage for approved projects ensure trust among collaborating parties.

Safe settings

Data doesn’t need to move and stays under the full control of the data custodian. Data custodians control access to their data via asset policies. Industry-standard security controls are in place such as data encryption, no export of individual-level or raw source data, and the ability to track user activity.

Safe data

Collaborating parties agree on the data they are provisioning for federated training or analysis. Only necessary data (often de-identified) is registered in the platform. Data is encrypted both at rest and in transit. Individual-level data never leaves the Compute Gateway, and whatever aggregated data is returned is controlled via asset policies that the data custodian defines.

Safe outputs

Full control over the process to return results computed on the data. This prevents the unauthorised exfiltration of data from the Compute Gateway.

Fulfilling all security and compliance standards

MedTech

“The Apheris platform is our Trusted Research Environment of choice - their end-to-end solution fulfills all security and compliance standards for even the most sensitive data.”

Compliance officer Top-30 MedTech company

Any data, any size, anywhere

Want to learn more about how Apheris can help you power your infrastructure with federated machine learning and analytics?

Get in touch

Related reading

Publication

Security of AI  Systems: Fundamentals

Advising the German Federal Office for Information Security on the Security of AI-Systems, Apheris provides an overview on attack vectors and threats of AI systems where external data is used or trained models are exposed to third parties. Recommendations are derived on how to systematically safeguard and test AI-systems.

Guide

Buyer's Guide to Secure Data Collaboration

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.

White Paper

Privacy-preserving Data Ecosystems in Support of Drug Discovery

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.