Traceforce, co-founded by Xia and Varun, has launched out of stealth with a security platform that gives organizations visibility and control over AI applications running across company devices, including laptops, sandboxes, and virtual machines. The product is currently deployed across more than 1,000 devices at 10 organizations, according to a launch post on Hacker News dated July 16, 2026. Hacker News

The core problem Traceforce addresses is one that many security teams have not yet fully mapped: which AI apps employees are using, what data those apps can reach, and what risks those connections introduce. Traceforce discovers which AI applications are in use and how they are connected to other data sources via MCPs (Model Context Protocols). An MCP is an open protocol that lets AI applications connect to external data sources like databases, file systems, or internal APIs. On average, the company reports finding over 15 AI applications per device, with each application connected to 5 to 10 MCPs. Hacker News

Installation is lightweight: a binary and browser extension on each device. Setup takes approximately 15 minutes, per the company's website. Traceforce.ai Within 30 minutes of installation, Traceforce begins uploading live data to a centralized dashboard that displays all AI agents and apps running across the organization's devices. Hacker News

The platform goes beyond monitoring. It provides real-time threat blocking and automated remediation, and it monitors CLI (command-line interface) agents in addition to browser-based AI apps and MCP connections, according to the company's website. Traceforce.ai In practice, Traceforce has already helped customers identify exposed plaintext secrets in MCP configurations, prevent API keys from leaking through AI-generated code, and warn developers before executing potentially destructive commands such as DROP TABLE. Hacker News

Privacy architecture is a notable design decision. By default, Traceforce collects only metadata and telemetry about AI applications, MCPs, and tools running on a device. All content inspection happens locally. User prompts are never stored unless an organization's security administrators explicitly configure that capability. Hacker News

The product also ships an open-source dynamic MCP pentesting tool called mcp-xray, hosted on GitHub. GitHub Pentesting, short for penetration testing, is the practice of probing systems for security vulnerabilities. The repository allows security teams to probe MCP configurations for vulnerabilities independently of the commercial platform. Hacker News

Before building Traceforce, the founders spoke with over 50 CISOs (Chief Information Security Officers) and CIOs to validate the problem space. Co-founder Xia was previously Director of Engineering at Clumio, which was acquired by Commvault in October 2024. Hacker News

Traceforce targets small to medium enterprises with 200 or more employees that are rapidly adopting AI coding assistants, ChatGPT, Claude, and MCPs. The company offers a free trial via its website and has published a video demo on YouTube. Hacker News

The product's enforcement model is what the founders call a "warn and acknowledge" approach: developers retain the freedom to work as they choose, but receive warnings before taking actions that could result in costly mistakes. Hacker News

The broader context here is worth pausing on. Fifteen-plus AI applications per device, each wired into 5 to 10 MCPs, means a single laptop may expose dozens of data-source connections that no security team has inventoried. That is a dramatically larger attack surface than the SaaS application sprawl that dominated shadow-IT discussions a few years ago, and it has emerged quickly. MCP adoption has accelerated through 2025 and into 2026, and the protocol's open design means that a misconfigured MCP can expose databases, internal APIs, or file systems to any AI agent that connects to it.

The decision to handle content inspection locally rather than in the cloud is a pragmatic one. It sidesteps the thorniest privacy objection: the idea that a security tool would need to see or store employee prompts to be effective. Whether metadata and telemetry alone prove sufficient for detection at scale is a question that broader deployment will answer.

The open-sourcing of mcp-xray is also a telling signal. Pentesting tools for MCP configurations are not widely available, and releasing one freely gives the broader security community a concrete way to assess MCP risk without committing to a commercial product. It also positions Traceforce as a contributor to the emerging MCP security ecosystem rather than solely a vendor within it.

For organizations now managing the intersection of AI coding assistants, agentic workflows, and MCP-connected data sources, Traceforce is betting that device-level instrumentation is the right architectural layer for visibility and control. The early traction, 1,000 devices across 10 organizations, suggests the bet is resonating with at least a segment of the market. Whether that scales beyond SME deployments into larger enterprises with more complex device-management estates will depend on factors the company has not yet had to address at this stage.