PVML is a secure virtual database platform that enables enterprise IT teams to create virtualized database layers on top of existing infrastructure without moving or duplicating data. Core concept: - Functions similarly to virtualization (analogous to VMware for physical machines) but applied to databases - Connects live to any database via high-performance Golang connectors with zero data movement - Each virtual database is centrally defined, scoped, and governed Security and access control: - Applies dynamic, user-level permissions and context-aware access controls at the infrastructure layer, before queries reach the production database - Uses a differential privacy and security engine to enforce real-time access control on every agent query - Replaces unpredictable AI agent behavior with deterministic, mathematically-based guardrails - Prevents unauthorized data access across AI agents, BI tools, and APIs AI and agentic support: - Auto-generates AI-ready protocols (MCP, A2A, REST API) for each virtual database - Supports integration with AI models such as ChatGPT and Claude - Addresses risks from autonomous agents inheriting user permissions without context-aware controls Governance and observability: - Logs every agent action (query to response) for full audit trails - Provides centralized visibility and management of all data access, permissions, and privacy policies - Enforces resource controls to prevent outages and uncontrolled compute spend from unpredictable agent workloads Use cases include: AI-driven data analysis, data anonymization for cross-team collaboration, and data monetization for third parties while preserving privacy.