PVML is a product by PVML in the Data Access Governance category. It covers Database Security, Agentic AI Security and LLM Security.

What is the pricing for PVML?

PVML is a commercial product. For pricing, visit https://pvml.com/ or contact PVML.

What are alternatives to PVML?

Alternatives to PVML listed on CybersecTools: 1. Dymium - Zero-copy, real-time governed data access layer for AI, apps, and analytics. (Commercial) 2. Formal Protocol Security - Protocol-aware reverse proxy for datastores & APIs enforcing access policies (Commercial) 3. FileOrbis Platform - Enterprise file security platform for secure sharing, governance & access control. (Commercial) 4. TrustLogix TrustAccess - Data access governance platform enforcing least-privilege across data platforms (Commercial) 5. Immuta Data Access Governance - Centralized data access governance platform for cross-platform policy mgmt. (Commercial) Compare all PVML alternatives at https://cybersectools.com/alternatives/pvml

What deployment and organization size does PVML fit?

PVML is available as cloud or on-premises (hybrid). It is listed for mid-market companies and enterprises. Pricing is commercial. Ask PVML for pricing and support terms.

PVML

byPVML

Secure virtual database layer with AI-ready access controls and differential privacy.

Data Protection Data Access GovernanceHybridMid-Market · EnterpriseCommercial

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What is PVML?

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.