AI security is not replacing classic cybersecurity. It is extending it.
The same old attack classes are back with new interfaces. Injection becomes prompt injection. Privilege escalation becomes tool abuse through over-permissive agents. Poisoning moves from code repositories into training and retrieval pipelines. Persistence appears as long-lived memory contamination or policy drift across sessions.
A useful mental model: prompt injection is close to SQL injection 2.0. In both cases, untrusted input changes system behavior by hijacking intent. With SQLi, input rewrites queries. With LLM systems, input rewrites instructions and can redirect actions if tool use is enabled.
This is why “safe prompts” are not a security strategy. You need hard technical controls: strict instruction hierarchy, tool permission boundaries, output validation, high-risk action confirmations, and independent policy enforcement outside the model.
Organizations that separate “AI security” from “cybersecurity” create blind spots by design. Are your AppSec, SOC, and platform teams threat-modeling LLM workflows together, or still operating in parallel worlds with parallel assumptions?