AI coding tools assist software development: code completion in the editor, chat over a codebase, terminal agents that edit files and run commands, and AI-native IDEs. Teams use them to write and review code, generate tests, explain legacy systems and take over repetitive refactoring.
The sensitive asset is your source code, which often contains proprietary logic, internal API endpoints and occasionally credentials. Context sent to a hosted model leaves your repository, so retention, use for training and access controls matter, as do the permissions an agent receives to read files and run commands. Lock-in appears in editor-specific configuration, custom rules and workflows built around one vendor's agent, while the underlying models are frequently interchangeable. The Cyber Resilience Act also sets security requirements for the products with digital elements that you ship, so AI-generated code needs the same review as any other code.
When evaluating a European option, check whether the tool can work with a self-hosted or local model, whether it supports open editors and standard protocols rather than a proprietary fork, how it scopes access to files and commands, and whether you choose which model sits behind it.