PVML

Your Database, Virtualized for AI.

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Description

PVML virtualizes internal databases, making them instantly accessible, secure, and AI-ready. By combining live connectivity, strict permission controls, and advanced privacy protections, organizations can safely connect their valuable business data to generative AI agents and models in real time without duplicating or moving data.

Enterprises stay compliant and in control with centralized governance, differential privacy, and comprehensive audit trails. PVML enables businesses to innovate with AI while ensuring data privacy, security, and explainability across all data interactions.

Key Features

Live Data Connectivity: Access any database in real time without data movement or duplication
Differential Privacy Security Engine: Applies mathematically guaranteed privacy protections to each agent query
Personalized Permissions: Enforces granular, use-case-specific access patterns on live data
Plug-and-Play AI Integration: Instantly connect virtual databases to any AI platform using auto-generated protocols
Comprehensive Audit Trails: Logs every agent action for complete observability and governance
Centralized Access Management: Manage all permissions, privacy, and data policies in a single system
Compliance-Ready: Undergoes rigorous external audits and supports regulatory standards like SOC2
Optimized AI Data Scoping: Supplies AI models with just the right, context-rich data for trustworthy outputs

Use Cases

AI-powered data analysis on sensitive business data
Secure data anonymization and sharing across organizational units
Monetizing insights from private data with privacy guarantees
Real-time data access for chat-based analytics by employees
Safe collaboration with third parties using restricted data views

Frequently Asked Questions

What is PVML’s approach to differential privacy?

PVML enables analytics and machine learning on sensitive data with mathematically guaranteed private outputs by adding statistical noise, effectively protecting individual privacy without compromising data utility.

How does differential privacy differ from homomorphic encryption?

Unlike homomorphic encryption, which can be computationally expensive and exposes perfect outputs, differential privacy guarantees privacy at the output level, preventing reverse engineering and attribute inference attacks without added computational overhead.

Does PVML offer unique differential privacy capabilities?

Yes, PVML combines modern software engineering and applied machine learning to deliver highly accurate, privacy-preserving results that can be integrated into various industries and applications, prioritizing easy deployment and scalability.

Do I need to move my data to use PVML’s solution?

No, PVML works without duplicating or moving your data; sensitive data remains wherever it is currently stored, reducing risk and complexity.

How does PVML address regulatory demands?

PVML is verified by legal and privacy experts, designed to maintain privacy by default and undergoes rigorous external audits to comply with standards like SOC2.