For decades, cybersecurity defenses were built around structured interfaces such as APIs, identity systems, and network endpoints. Those surfaces were predictable, schema-bound, and constrained by deterministic rules. Security teams could define boundaries between valid and invalid behavior, then monitor for violations. Generative AI systems change that model. In these environments, instructions are expressed in natural language, assembled from multiple context layers, and interpreted non-deterministically. Prompts, retrieval context, system instructions, and tool outputs now influence whether software takes action. That shift matters because safety is no longer just a property of the model. It is a property of the entire architecture around it. Prompt Injection is a Systems Problem This is why prompt injection is better understood as a systems problem than a model problem. An unsafe outcome may not result from a single bad response. It may come from a chain of individually valid steps, each operating as designed, but combining into something harmful. The model may follow instructions correctly, while the overall workflow fails to enforce the right constraints. Traditional exploits break syntax or violate explicit rules to gain unauthorized access. Prompt-driven attacks work differently. They manipulate meaning, precedence, and trust relationships inside a system built to interpret ambiguous instructions. The goal is not to crash the application. It is to steer it toward a harmful but technically valid action. That makes architectural design central to security. A model with stronger guardrails can reduce risk, but it cannot compensate for excessive permissions, weak prompt governance, or poorly bounded tool access. If an agent can read sensitive data, call external services, and act on loosely scoped instructions, the surrounding system has already expanded the attack surface. Model safety helps. It does not solve the problem by itself. Risk Emerges Across Multi-Step Workflows The risk becomes clearer in multi-step AI workflows.