AI migration is a project move that may mean using AI to speed up an existing workflow, building a system around AI, or leaving an AI platform for a different one. The term is broad, so the first security step is to define which direction the migration takes.
In cyber security, the distinction matters because each path creates different risks. If AI is used as a tool, teams must review prompts, input data, and output validation. If AI is the target, governance, model testing, and data protection become part of the build. If the project is exiting an AI platform, defenders must watch for dependency mapping, data export, and vendor lock-in. Attackers often exploit confusion during these transitions, when controls are incomplete or owners are unclear. Good migration planning helps reduce exposure, preserve integrity, and keep access decisions aligned with the real system design.