Powering your data infrastructure
Securely connect data across boundaries
Ensuring security and privacy
Enterprise-grade security
Access security
- User authentication with multi-factor authentication
- Fine-grained data access policies
- User permissions / roles
- Enterprise single sign-on (SSO)
Data security
- Federated infrastructure
- Segregated users, projects and data
- User role-based access to data
- Encryption of data
- Data export control
System security
- 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

Always protect data and IP and manage privacy risk
Privacy controls on the computational level
- 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)
- 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
- 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
Stay in full control of your data and IP, always
Safe people
Safe projects
Safe settings
Safe data
Safe outputs
Fulfilling all security and compliance standards
“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.”
Related reading
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.
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.
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.