Generative AI models, such as ChatGPT and Deepseek, are increasingly integrated into daily life. However, concerns about reliability, cybersecurity, transparency, and data privacy are growing through their use. While Secure by Design (SbD) and Explainable AI (XAI) offer theoretical guidelines, their practical combined application to AI-generated content remains unclear. This study empirically evaluates AI security, cybersecurity and transparency using a structured interrogation method directly addressed to AI models. We assessed multiple text-based open-source and proprietary AI systems on cybersecurity claims, update transparency, and privacy compliance. Preliminary results reveal some discrepancies between AI declarations and actual adherence to SbD principles. While most models incorporate ethical safeguards, they lack clarity on security updates and data management, particularly regarding training data. We propose a user-centered audit framework to test transparency and AI security commitments. The findings emphasize the need to adapt current Secure by Design standards to AI ecosystems while ensuring verifiable transparency.