video1.0<iframe src="https://www.loom.com/embed/977ee0718d39436a81a5b22632f4e296" frameborder="0" width="1280" height="960" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>9601280Loomhttps://www.loom.com9601280https://cdn.loom.com/sessions/thumbnails/977ee0718d39436a81a5b22632f4e296-90a0101e959e525d.gif182.6In this video, I discuss the limitations of current AI coding tools, specifically their tendency to generate code that appears correct but can disrupt architectural integrity. I highlight our solution, Port-Cullis, which not only identifies vulnerabilities but also understands the dependencies and intent behind code changes, as demonstrated through our testing with Django. Unlike Copilot, Port-Cullis centralizes security checks and provides a risk score, ensuring a more reliable coding process. As we refine this technology for human-in-the-loop development, I emphasize the need for a governance layer to build trust in AI-driven coding. I invite you to consider how Port-Cullis can enhance your development processes.