My research shows that AI cannot immediately catch complex threats like advanced malware or insider attacks. Many developers use AI without fully understanding the generated code, which creates hidden bugs and security flaws. When critical code is fed into external AI platforms, it often ends up in public training data, exposing private company assets to competitors.
​Because developers might switch companies, the knowledge retained by these AI models creates a direct risk of insider data leaks. Therefore, trusting AI entirely to write core architecture is a threat to corporate stability.
​The best way forward is to use AI only for logic, while keeping sensitive code and security protocols strictly with human developers. Companies must enforce zero-retention policies to properly protect their work.My research shows that AI cannot immediately catch complex threats like advanced malware or insider attacks. Many developers use AI without fully understanding the generated code, which creates hidden bugs and security flaws. When critical code is fed into external AI platforms, it often ends up in public training data, exposing private company assets to competitors.
​Because developers might switch companies, the knowledge retained by these AI models creates a direct risk of insider data leaks. Therefore, trusting AI entirely to write core architecture is a threat to corporate stability.
​The best way forward is to use AI only for logic, while keeping sensitive code and security protocols strictly with human developers. Companies must enforce zero-retention policies to properly protect their work.