System-level security is a cybersecurity approach that protects the entire deployment environment, not just the model, application, or prompt layer. It includes identity and access control, network rules, logging, rate limits, sandboxing, secrets management, and monitoring across every component that can influence behavior.
This matters because attacks on modern AI systems and web services often succeed through the surrounding infrastructure rather than the core model. An attacker may abuse weak authentication, overly broad tool permissions, poor audit trails, or unprotected APIs to turn a legitimate service into a platform for persistence, repeated abuse, or data exposure. Defenders use system-level security to reduce that attack surface by verifying users and agents, constraining tools, detecting abnormal sequences of actions, and limiting what each component can reach. In practice, it treats the model as only one part of a larger, security-sensitive system.