As AI systems become integral to critical infrastructure, they face increasingly sophisticated threats from adversaries employing evasion attacks, which manipulate model inputs to produce incorrect outputs while appearing legitimate. These attacks exploit the statistical nature of machine learning models, creating adversarial examples that traverse decision boundaries invisibly to human observers. Various types of evasion attacks include input perturbation, feature-space, and model inversion attacks, each targeting different aspects of AI systems to induce misclassification or extract sensitive information. Attackers often employ structured methodologies, progressing from target identification to adversarial input crafting and execution, continuously refining their strategies to evade detection. In response, organizations can implement defense strategies such as adversarial training, randomized smoothing, formal verification, and ensemble methods to enhance model robustness. Additionally, real-time monitoring and adaptive defense orchestration can detect and mitigate these threats, while post-attack forensics aid in strengthening future defenses. Platforms like Galileo offer comprehensive solutions to address AI evasion attacks by providing advanced model evaluation, real-time threat monitoring, and unified security management to protect AI applications from sophisticated adversarial manipulations.