Traceforce: Security and control in AI agent programming
Discover how Traceforce addresses cybersecurity in AI applications through real-time visibility and open-source pentesting tools.

The new frontier of AI security
The mass adoption of artificial intelligence-based tools, such as ChatGPT or Claude, has radically transformed programming and corporate workflows. However, this speed of implementation has left a critical void: the lack of visibility into how these tools interact with corporate data. This is where Traceforce comes in, a solution designed to provide total control without sacrificing developer agility.
As we explored in articles on the evolution of agents, such as Coasty: The evolution of computer-use agent programming, security must evolve at the same pace as the execution capabilities of these systems.
Deep visibility: Beyond traditional EDR
Traditional security teams often rely on tools like EDR or CASB, which fall short when faced with the complexity of Model Context Protocols (MCPs). Traceforce solves this using a lightweight binary and a browser extension developed in javascript (Node.js), capable of mapping connections between AI applications and data sources.
How does it ensure security?
The platform operates under a scheme of transparency and local control:
- Telemetry monitoring: Collection of metadata regarding which applications and MCPs are running on devices.
- Local inspection: Analysis of tool calls occurs directly on the user's device, ensuring privacy.
- Active prevention: Ability to block or warn about risky actions, such as the execution of destructive commands (e.g.,
DROP TABLE) or API key leakage.
"Security teams can monitor agent activity in real-time, implement controls, and receive alerts about security risks as they emerge," explain its founders, Xia and Varun.
Commitment to the open source ecosystem
One of Traceforce's pillars is its collaborative approach. The company has released a dynamic pentesting tool under an open source license called mcp-xray. This resource is essential for organizations to audit their own MCPs and detect vulnerable configurations before they are exploited.
Conclusion
The current challenge is not to curb the use of AI, but to provide an environment where employees can innovate securely. Traceforce not only identifies risks like plaintext secret exposure, but also fosters a "warn and recognize" culture that allows developers to work without fear, knowing they have a robust layer of protection operating under the hood.
Sources:
- Hacker News (https://news.ycombinator.com/item?id=48937020)
- Traceforce (https://www.traceforce.ai/)
Related articles
9 de septiembre de 2026
Mastering Microsoft Entra: An Essential Guide to the SC-900 Certification
Learn the pillars of cloud identity, from access management to Conditional Access, which are key to passing the SC-900 certification.
1 de septiembre de 2026
Nori Robotics: Democratizing Humanoid Robot Programming
Nori Robotics launches a dual-arm humanoid robot for under $1,700, aiming to democratize robotics research and artificial intelligence.
25 de agosto de 2026
Phonebook: The open-source catalog for mobile UI previews
Discover Phonebook, the open-source tool that turns your SwiftUI and Compose previews into a static visual catalog for your entire team.
18 de agosto de 2026
AI Agents: The New Security Challenge in Modern Programming
Autonomous AI agents are transforming cybersecurity: when the reasoning model becomes the attack vector, the architecture must change.
Loading comments...