Traceforce Offers Enterprise Visibility into AI App Usage

Traceforce is a new security platform designed to give enterprises visibility and control over how AI applications are used on company devices.

Developed by founders Xia and Varun, the system installs a lightweight binary and browser extension on each device, providing a dashboard for security teams to monitor AI agent activity in real-time. This includes tracking which AI apps are in use and how they connect to internal data sources, referred to as Multi-Cloud Platforms (MCPs).

The core problem Traceforce addresses is the gap in traditional security tools. Endpoint Detection and Response (EDR) systems see processes, and Cloud Access Security Brokers (CASBs) monitor network traffic, but neither offers deep visibility into application-level activity within AI tools.

"Traditional security tools fall short: EDRs see processes, CASBs see network traffic, but neither has visibility into the application-level activity happening inside AI apps."

Traceforce aims to bridge this by understanding the configurations and logs of individual AI applications, a labor-intensive process given the rapid evolution of AI features. It collects metadata and telemetry by default, with optional local content inspection for high-risk actions, ensuring user prompts are not stored unless explicitly configured.

Currently deployed across over 1,000 devices in 10 organizations, Traceforce reports discovering an average of 15 AI applications per device, each connected to multiple MCPs. The company states it has helped customers identify exposed secrets, prevent API key leaks, and warn developers against destructive commands like 'DROP TABLE' through a 'warn and acknowledge' approach.

The platform targets security, IT, and AI platform teams within small to medium enterprises (200+ employees) that are rapidly integrating AI coding assistants and other generative AI tools. While a free trial is available, specific pricing tiers have not been publicly disclosed.

This offering represents a growing need for robust governance as AI adoption accelerates within corporate environments, particularly for managing the interplay between proprietary data and external AI models.