The latest articles on DEV Community by 叶嘉 (@_ee149a18b76a65720ad76). https://dev.to/_ee149a18b76a65720ad76 https://media2.dev.to/dynamic/image/width=90,height=90,fit=cover,gravity=auto,format=auto/https:%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Fuser%2Fprofile_image%2F3834950%2Fca6334fd-709e-4636-bc53-386ef58eaa2b.png https://dev.to/_ee149a18b76a65720ad76 en 叶嘉 Fri, 20 Mar 2026 08:25:08 +0000 https://dev.to/_ee149a18b76a65720ad76/clawvault-showcase-post-2ake https://dev.to/_ee149a18b76a65720ad76/clawvault-showcase-post-2ake <p>Once AI agents start interacting with real systems, the problem changes.</p> <p>It is no longer only about model output. Teams also need a clearer way to inspect interactions, define narrower capability boundaries, and handle risky behavior around tools, APIs, and sensitive workflows.</p> <p>That is the direction ClawVault takes.</p> <p>According to the current repository README, ClawVault is an open-source OpenClaw Security Vault for AI agents and AI applications centered on:</p> <ul> <li>Visual Monitoring</li> <li>Atomic Control</li> <li>Generative Policies</li> </ul> <p>The README also lists more concrete areas such as:</p> <ul> <li>sensitive data detection</li> <li>prompt injection defense</li> <li>dangerous command guard</li> <li>auto-sanitization</li> <li>token budget control</li> <li>a real-time dashboard</li> </ul> <p>From an engineering perspective, one of the most interesting parts is the control path described in the repo:</p> <ul> <li>a transparent proxy gateway</li> <li>a detection engine</li> <li>a guard / sanitizer layer</li> <li>audit + monitoring</li> <li>a dashboard</li> </ul> <p>That gives you a more centralized way to inspect, detect, and act on AI interactions than scattering checks across application code.</p> <p>Repository:<br> <a href="https://github.com/tophant-ai/ClawVault" rel="noopener noreferrer">https://github.com/tophant-ai/ClawVault</a></p> ai security agents