Overview of AI Model Security Practices

This document is a guide on AI Model Security, an enterprise application designed to enforce security standards for machine learning models in production environments. It addresses the security gap where machine learning models often lack the rigorous validation applied to other data inputs. The guide outlines the importance of securing AI models against various threats, including deserialization threats, neural backdoors, and runtime threats. It details the core components of AI Model Security, which include Security Groups, Sources, Rules, and Scans, providing a framework for organizations to establish and enforce security standards. The document explains how to create deployment profiles, configure identity and access management, and install AI Model Security. It also highlights the significance of audit trails and compliance reporting capabilities to meet regulatory requirements. The guide emphasizes the need for organizations to implement consistent security standards across all machine learning model deployments to mitigate operational risks associated with unvalidated or compromised models.