Enterprise-grade AI security platform. Unified governance, compliance automation, and real-time threat detection across your entire AI infrastructure.
Unified risk matrix across all AI assets. See security posture by domain, layer, and category at a glance.
7 layers of protection — deep visibility into HEAP, network, processes, and runtime secured at kernel level via eBPF instrumentation
Autonomous agents orchestrating complex tasks across your infrastructure
Model Context Protocol servers exposing tools, resources, and capabilities
Foundation models from OpenAI, Anthropic, and internal deployments
External and internal APIs accessed by agents and tools
Databases, vector stores, and file systems with sensitive data
Model weights, dependencies, and third-party packages
Containers, sandboxes, and execution environments
Real security at the operating system level — not just network proxies
Extended Berkeley Packet Filter
AI Hypervisor attaches eBPF probes directly to the Linux kernel. Every system call, network packet, and file operation is observed without modifying application code.
Runtime Memory Analysis
Inspect all loaded libraries, memory allocations, and runtime state of AI agents. Detect malicious code injection, pickle exploits, and unauthorized memory access.
Unlike network proxies that only see API traffic, AI Hypervisor monitors the complete runtime: every loaded module, memory allocation, and system call.
Community-driven, AST-complete, and cryptographically signed capabilities for secure AI agent operations
Open specification developed by the AI security community. Avoid proprietary lock-in with transparent, auditable certificate schemas.
Abstract Syntax Tree complete definitions ensure machine-readable, deterministic parsing. No ambiguity in capability declarations.
Every capability is signed with PKI. Tamper-evident and verifiable without code scanning or runtime analysis.
Get a live demo of AI Hypervisor in your environment