Most AI incidents don't happen because the model gave a bad answer. They happen because nobody was governing everything around the model.
Large language models are already finding their way into everyday engineering workflows. Developers use them in IDEs, support teams rely on them to answer customer questions, analysts connect them to internal databases, and AI agents now execute tasks that used to require direct human involvement.
Getting AI into production becomes easy. The harder problem is keeping it reliable, secure, and manageable after deployment.
The problem usually becomes obvious after deployment. The first few AI integrations feel manageable, but as more models, tools, MCP servers, and applications are introduced, questions that were simple become difficult to answer.
Which AI applications are allowed to access production systems?
