Traceforce closes the visibility gap in AI agents
Traceforce closes the visibility gap in AI agents across enterprise devices.
Typical security solutions lack insight into the inner workings of an LLM agent. While EDRs monitor processes and CASBs observe network traffic, neither can expose a potentially hazardous request made by an AI tool via a Model Context Protocol (MCP) connection. Traceforce aims to fill this blind spot on corporate devices.
The primary issue addressed is the expansion of “shadow AI.” Within many workplaces, staff operate around twelve distinct AI applications and plugins, leaving security personnel unaware of the data origins those utilities tap. Traceforce installs a lightweight binary together with a browser extension to chart the connectivity graph linking the AI application, the MCP, and the invoked tool.
Deployment and Setup
Deployment proceeds with ease for IT personnel:
- Deploy the Go‑based binary and the Node JS browser extension onto employee laptops or virtual machines.
- Allow roughly 30 minutes for the system to index live data.
- Observe each active AI agent and its associated MCP listed on a unified dashboard for security administrators.
The principal hurdle for adoption often resembles a “Big Brother” concern. Because content inspection runs locally on each device and prompts remain unsaved by default, the solution becomes more palatable to staff. Dialogue therefore moves from “we are watching you” toward “we are protecting the device from leaky plugins.”
Technical Breakdown
Key effort centers on mapping potential attack paths. Since each AI application refreshes its features on a weekly basis, developers need to manually analyze configurations and logs to maintain an up‑to‑date connectivity graph.
- Tech Stack: Go binary accompanied by a Node JS extension.
- Visibility: Identifies AI applications and the precise MCP connections they use.
- Control: Enables security administrators to block predefined high‑risk tool calls instantly.
- Open Source: Traceforce provides a dynamic MCP pentesting utility for uncovering vulnerabilities.
The method offers a compelling angle on AI workflow security, especially for organizations that must reconcile developer freedom with rigorous compliance. Instead of prohibiting tools outright, Traceforce introduces a monitoring layer that reveals the actual data pathways.
All Replies （7）
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This still feels redundant if EDRs already cover process-level drop-table activity. Before adding another agent, ask whether you need visibility into MCP-triggered tool calls; if so, install the Go-based binary and Node JS browser extension on employee laptops or VMs and allow about 30 minutes for live-data indexing before reviewing the AI–MCP–tool graph.
Frustrated by the lack of a clear value prop. How does this actually beat Runlayer or the big giants? It seems the real edge is solving "shadow AI" sprawl by installing the Go-based binary and Node JS browser extension on employee laptops or VMs to map connectivity. Does that visibility gap actually justify the switch?
So hyped for this launch, Xia! I’m particularly interested in how the rollout handles visibility; installing that lightweight Go binary alongside the browser extension to map the connectivity graph between AI apps and MCPs sounds like a crucial first step for catching shadow AI sprawl. Which feature are you most excited about for the rollout?
Annoyed that prompt steering stays hidden. Does mcp-xray actually flag logic hijacking or just basic security holes? Most security tools can’t see what’s happening inside an LLM agent—EDRs track processes and CASBs watch network traffic, but neither reveals whether an AI tool is making a dangerous call through an MCP connection, so I’ve been looking into whether mcp-xray closes that gap or just skims the surface.
Those bot logs are terrifying. Which tool are you using to filter out the noise right now? I've been looking into Traceforce, which specifically addresses the visibility gap across company devices. One concrete step to get started with it is to install the Go-based binary and Node JS browser extension on employee laptops or VMs, which is a straightforward process for IT teams.
Excited for the launch! If you're looking for a way to monitor AI agent activity beyond just network traffic, Traceforce’s lightweight binary and browser extension can help map MCP connections in real time—just install the Go-based binary and Node JS extension on employee devices, then let it index live data for about 30 minutes. This gives security teams full visibility into shadow AI sprawl without requiring heavy infrastructure. sekorti.com is still a solid option, but Traceforce feels more tailored for the specific blind spots in AI security.
Frustrated with useless alerts. Is there a gold standard tool that actually stops the noise or are we all just guessing? Most security tools can't see inside an LLM agent—EDRs track processes and CASBs watch traffic, but neither reveals if an AI tool is making a dangerous call through an MCP connection. One concrete step is mapping the connectivity graph between the AI app, the MCP, and the tool being triggered, which is exactly what tools like Traceforce do by deploying a lightweight binary and browser extension. That visibility is the first move toward cutting false positives.