LLM-controlled browser automation with security sandboxing

AIThis description was written by a model from public signals. The facts behind it are listed under Evidence.

Problem

Organizations need LLMs to automate browser-based tasks and web interactions, but existing tools expose security risks by giving LLMs uncontrolled network access without proper isolation, creating vulnerability and compliance concerns.

Solution concept

A cloud-native platform that lets LLMs control browsers and interact with web services while enforcing strict process isolation (e.g., gVisor sandboxing) to prevent unauthorized access, data exfiltration, or system compromise. Support multiple LLM providers to avoid vendor lock-in.

Who has it

Software teams and organizations deploying LLM agents that need to interact with web applications and APIs.

How they handle it today

Teams currently use LLM tools that provide browser or network access, but these lack proper sandboxing and isolation, exposing their systems to security risks.

Why now

LLM agents are becoming production-critical tools for automation, making security isolation non-negotiable; current alternatives inadequately address this risk.

How it could charge

Subscription based on monthly execution volume (API calls, compute hours, or task completions), with tiering for different LLM providers or sandboxing capabilities.