Design and implementation of a scalable zero-trust AI security methodology for Criminal Justice Information Services (CJIS) incorporating dual-stream validation for adversarial defense, data protection, and compliance assurance

The adoption of artificial intelligence (AI) and machine learning (ML) technologies in criminal justice information services (CJIS) has enabled unprecedented capabilities for law enforcement; however, these technologies can also expand the attack surface for highly sophisticated adversarial threats. Traditional perimeter-based information security models are not adequate to protect sensitive criminal justice information (CJI) against evasive attacks, data-poisoning, and model inversion. A new, scalable zero-trust AI Security Methodology for CJIS compliance is proposed. The methodology employs a Dual-Stream Validation engine that separates security event monitoring into an Input Integrity Stream and Latent Representations Stream. In addition to the framework's use of graph neural networks (GNN) for structural threat mapping, federated learning is used for privacy-preserving intelligence. This methodology's framework guarantees that AI-driven decisions are auditable and reliable while also employing a blockchain-based immutable logging mechanism to ensure a tamper-proof audit history for judicial transparency and compliance. Experiment simulations reveal a significantly higher level of detectable adversarial perturbed inputs within the dual-stream modality, while maintaining compliance with low-latency mission-critical law enforcement operations. This research bridges the gap between theoretical adversarial defense work with the operational regulatory requirements of federal information systems, providing a pathway for resilient, compliant, trustable AI for public safety.