Secure the ML lifecycle. Govern the agent at runtime.
Protect AI secures models and pipelines across the ML lifecycle. VANGUARD governs what your agents actually do once they reach production.
Protect AI and VANGUARD solve different layers.
Most AI security tools solve a different layer. VANGUARD focuses on runtime governance: what an AI system is allowed to do, in this context, for this tenant, with this tool, using this instruction.
Strongest fit: Lifecycle AI and ML security
Protect AI focuses on MLSecOps - securing models, pipelines and the ML lifecycle - rather than runtime agent behaviour.
Governed AI execution in production
VANGUARD controls trusted instructions, context access, tool execution, and audit evidence once AI systems start taking action - across agents, RAG pipelines, and MCP-enabled workflows.
VANGUARD vs Protect AI, layer by layer.
Protect AI is strong in its layer - VANGUARD governs the runtime above it. The two are complementary; teams often run both.
Govern actions, not just prompts.
Secure MCP & tool-calling
VANGUARD mediates API access, tool execution, retrieved context, and runtime workflow decisions with tenant-aware policies.
Control stateful AI risk
Agentic threats emerge across memory, retrieved content, tool output, and multi-step execution - not one turn at a time.
Produce governance evidence
Every runtime decision is recorded with ZKP evidence that supports audits, assurance reviews, and internal policy checks.