Building AI Features? Don't Leave Security Until the End 🔐

Adding an AI feature to an application introduces more than model-integration challenges. It can also create new risks around sensitive information, permissions, and untrusted input.
A few practices worth considering:

1. Protect credentials. Keep API keys in environment variables or an appropriate secrets manager.
2. Validate inputs. Treat user-submitted content as untrusted.
3. Control access. Make sure users can retrieve only information they're authorized to see.
4. Handle failures. Avoid exposing internal errors or sensitive details in responses.
5. Test the complete workflow. Check how the application behaves with unexpected inputs, unavailable services, and failed requests.

Python developers can practise these principles by building a small API first and then adding an AI-powered feature.

The Full-Stack Python For AI Developers project explores Python, web programming, and AI-related development.

Which security consideration do you think developers most often overlook when integrating AI into an existing application?