AI System Testing Principles & Ethics

AI testing extends beyond traditional cybersecurity into the domains of robustness, fairness, and explainability. These principles ensure that machine learning models are not only secure from external threats but also reliable, unbiased, and compliant with emerging AI regulations (such as the EU AI Act). A holistic testing strategy must evaluate the "Black Box" behavior of models to ensure they remain aligned with human values and operational safety requirements.