Galois Adversary Resistance is a research and development initiative focused on protecting AI/ML systems from adversarial attacks. The product addresses threats ranging from physical infrastructure destruction to subtle input manipulations designed to degrade model performance and decision-making capabilities. The solution employs neuro-symbolic and bio-inspired techniques to enhance AI/ML model resilience against adversarial inputs, including imperceptible noise that can significantly impact model performance. The approach leverages formal methods and cognitive sciences to train models that better account for complex contextual factors. The product targets vulnerabilities in AI/ML systems that adversaries exploit to bias or degrade inferential capabilities. This includes protection against attacks on data sources, visual perception systems, and other input vectors that could lead to misguided actions or analysis. Galois Adversary Resistance aims to ensure AI/ML models maintain intended functionality and trustworthiness under challenging conditions or active attacks. The solution fills reasoning gaps that could otherwise be exploited by adversaries attempting to compromise model integrity.