The adoption of Artificial Intelligence (AI) in the United States criminal justice system has enhanced capabilities in predictive policing, recidivism risk assessment, and forensic analysis. However, such systems are becoming increasingly susceptible to adversarial manipulation and data leaks which threaten to undermine the integrity of the legal process and jeopardize public safety. Here we propose a security architecture that is comprehensive and graduate level, and designed to adhere to the requirements set forth in the Criminal Justice Information Services (CJIS) Security Policy. This security architecture leverages robust optimization methods such as Projected Gradient Descent (PGD) training, alongside privacy-preserving methods like Differential Privacy (DP) and Secure Multi-Party Computation (SMPC), to address the three-prong approach of fairness, interpretability, and privacy, which we label the "Triangle". The architecture is built on a Zero Trust Architecture (ZTA) verification approach ensuring that all AI pipeline interactions are authenticated and encrypted to the FIPS 140-2 standards. We frame the defense as a minimax optimization problem where we seek to minimize empirical risk based on worst-case adversarial model perturbations. Additionally, we will provide mapping from AI technical security controls to specific CJIS Policy Areas for regulatory compliance. We find that there exists a tradeoff between model utility and security hardening, but the use of a comprehensive, multi-layered security pipeline reduces the rate of successful evasion attacks and member inference attacks, leading to an eventual safer and more trustworthy judicial AI community.