AI systems, now integral to sectors like banking, healthcare, and transportation, are increasingly vulnerable to cyberattacks, particularly Trojan attacks, which exploit the learning mechanics of neural networks to implant hidden triggers that activate under specific conditions. This type of attack poses severe risks, including financial losses, reputational damage, regulatory penalties, and threats to human safety, especially in healthcare. The article discusses comprehensive strategies to detect and prevent Trojan attacks, highlighting different attack types such as data poisoning, model architecture manipulation, transfer learning exploitation, and federated learning attacks. Detection strategies include anomaly detection in model behavior, neural network inspection, adversarial testing, and statistical analysis of training data. To prevent these attacks, organizations must secure the AI development pipeline, validate data provenance and integrity, employ robust training techniques, and establish continuous verification protocols. Galileo's platform offers integrated tools to monitor and protect AI systems throughout their lifecycle, ensuring resilience against these sophisticated threats.