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Privacy-preserving ML & analytics across organizational boundaries
Data teams in regulated industries, such as financial services, insurance, and healthcare, are transforming how they manage risk by leveraging machine learning and advanced analytics. Apheris allows you to access high quality data from your suppliers, customers, and partners in a secure and privacy-preserving way across organizational boundaries.
EXAMPLE USE CASES
Risk management & modelling
Credit risk management
Train robust and interpretable ML models on third-party consumer data to assess credit risk. Integrate your existing model and data pipelines for validation and QA to ensure you comply with regulation.
AI risk management
Organizations that adopt AI and ML are heavily investing into their AI risk management capabilities. Leverage Apheris for responsible and privacy-safe use of data. Develop fair and trustworthy AI systems through secure access of data that spans across boundaries.
Cross-border data analysis
Work collaboratively across borders while ensuring compliance with privacy regulation and residency requirements. Integrate Apheris into your ML workflows and ensure that data stays protected and never moves across borders.
Why risk modelling teams choose Apheris
Data never needs to move
The Apheris federated infrastructure ensures that data always stays where it resides and under the full control of the data custodian, meeting data residency requirements.
Integrated SDK
ML teams can leverage their existing ML and data pipelines to ensure that models are trustworthy, robust and interpretable.
Traceability
The Apheris platform provides logging of every interaction to assist with compliance and audit obligations.
Compliance
Apheris offers state-of-the art security, privacy, and governance to ensure compliance with industry and privacy regulations including GDPR, CCPA, HIPAA, and more.
Helping you maintain regulatory compliance
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Data without borders
Organizations across the globe are quickly waking up to the need for safe, hassle-free data collaboration without the limitations, barriers, and security issues associated with data sharing. Apheris' Compute Gateway is making secure data collaboration across boundaries a reality.
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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.
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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.