AI Model Runtime Security: Protecting AI Systems During Inference
AI Model Runtime Security: Protecting AI Systems During Inference AI security does not end when a model enters production. Once deployed, an AI system processes live requests, interacts with applications, and may access sensitive data or external tools. These interactions create risks that development-stage controls alone cannot address. What is AI model runtime security? AI model runtime security encompasses controls that monitor and protect AI systems while they serve requests. It focuses on live inputs, model outputs, user access, connected tools, and suspicious activity. Unlike training-stage safeguards, runtime defenses must respond to threats in active environments. Key threats during inference Prompt injection. Malicious instructions embedded in user messages, retrieved documents, or external content can attempt to redirect an AI application. Sensitive data exposure. Inadequate permissions or output controls may allow confidential information to appear in model responses...