CoSupport AI
What is PVML?
PVML provides a unified platform that transforms enterprise data into a secure and flexible foundation for AI and analytics. It streamlines permissions management, monitoring, and best-in-class privacy enforcement, regardless of where data is stored or how it is accessed (BI, API, AI).
This empowers enterprises to unlock AI's full potential with complete security and control. PVML's proprietary Privacy Engine leverages Differential Privacy (DP) to enforce permissions and privacy policies at the computation level in real time. This allows for secure, AI-driven insights, unified access policies, and maintained compliance – without compromising privacy or scalability. PVML’s GenAI Optimization Module provides a secure, structured, and efficient AI pipeline, with features such as Secure RAG integration, model flexibility, and built-in auditability.
Features
- Agnostic & Flexible Architecture: Connect any data source, use any access method (AI, BI, or API), and swap AI models freely.
- Differential Privacy Data Protection Engine: Enforces permissions and privacy policies at the computation level in real time using Differential Privacy.
- GenAI Optimization Module: Provides a secure, structured, and efficient AI pipeline, featuring a Semantic and Behavioral Layer, Secure RAG integration, model flexibility, and built-in auditability.
Use Cases
- Analyze Data with AI
- Data Anonymization
- Data Monetization
FAQs
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How is Differential Privacy different from Homomorphic Encryption?
As opposed to Homomorphic Encryption, Differential Privacy has no overhead in computation and memory cost, and it also guarantees privacy at the output level, preventing reverse engineering and attribute inference attacks. -
Does PVML offer unique Differential Privacy capabilities?
PVML prioritizes applicable algorithmic capabilities, beyond what science can currently provide in the field of Differential Privacy. -
How do you address regulatory demands?
PVML has been verified by legal and technological experts in the privacy field. We work alongside a legal team and various security and privacy experts who provide guidance and validation throughout our development process. -
Do I still need PVML if my data does not contain any identifiable features?
Yes, anonymization is an outdated technique that leaves expensive data value on the table and fails to guarantee privacy, especially in the current age of AI. -
Do I need to move my data in order to use PVML's solution?
No. Your sensitive data stays wherever it is located (on-premise / on-cloud) and our platform does not require any duplication or modification of the data.
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