Filtering

Filtering strategies add protective layers around your AI system through input and output filtering when core system design alone isn't sufficient. Best practices cover implementing privacy-preserving filters to remove sensitive data, security safeguards to block unwanted or adversarial inputs, and safety filters to catch harmful content before it reaches users, among others. This filtering approach is especially valuable for foundation model services where you have limited control over core training but need to meet specific safety and compliance-aligned requirements.