Data custodians stay in full control of what happens with their data
Data doesn't need to move
Asset policies define who can do what with data
Code audit to assess custom code
Control how computation results are returned
Built in privacy preservation
Logging of data access and computations
Set controls down to the computational level
When a user submits their computation, the Compute Gateway validates authentication, and enforces asset policies, including privacy, and algorithmic controls.
This keeps the data custodian in control and ensures the computation complies with the defined asset polices.
Private data stays private
Compute Gateway:
- Ensures data is properly stored, accessed, and used
- Auditing and logging of user activities, actions, and executed computations
- Data custodian controls what happens with compute results
- Data access layer supports auditability and traceability, data versioning, and data lineage and provenance
Compute Orchestrator:
- Asset policies ensure computations adhere to pre-defined access and privacy controls
- Role based access controls and asset policies
User - SDK
- Data quality assessment
- Model performance and evaluation
- Enables output or model versioning and reproducibility
- Allows users to submit code for approval by an external party
Securely access distributed data via the 5 safes framework
Safe projects
Safe people
Safe settings
Safe data
Safe outputs
Learn more
Securing ML Models: Apheris' Contribution to ML Security
Together with the German Federal Office for Information Security we've developed frameworks and recommendations for ML practitioners to help secure ML models and maintain appropriate security measures.
E-book - Federated Data Ecosystems in Pharma & Healthcare
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.
Beyond MLOps - How Secure Data Collaboration Unlocks the Next Frontier of AI Innovation
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.