Secure every AI system.
From the runtime up.

Teams adopt models, SDKs, agents, and personal API keys faster than security inventories can track them.

Aurva observes AI traffic from the runtime and maps every workload, model, provider, credential, and destination without application changes.

Prompts, responses, and embeddings can carry regulated data to providers and destinations that security never approved.

Aurva classifies sensitive content in motion, evaluates the destination and identity, then applies the right policy inline.

Agents often inherit broad roles, long-lived keys, and permissions that exceed what they actually use at runtime.

Aurva connects each action to its workload and identity, compares access granted with access used, and detects permission drift continuously.

Multi-step agents cross models, MCP servers, tools, vector stores, and external APIs without leaving one complete evidence trail.

Aurva reconstructs the workflow from runtime activity and connects every model hop, tool invocation, identity, and downstream destination.

Configuration shows what an AI system was allowed to access. It does not prove what the system actually accessed or why.

Aurva records sensitive data access with the complete identity chain, destination, policy decision, and timing needed for investigation and audit.

A workload can begin sending sensitive data to a new model provider or external endpoint before volume-based controls react.

Aurva baselines expected destinations, identifies first-time egress, and attaches the originating agent, identity, and data class to the finding.