AI output is text, code, images, audio, or video produced by a machine learning model instead of a human. In cybersecurity, the key issue is not only what the content says, but whether its origin can be trusted. Model-generated content can look polished and authoritative, which makes it useful for legitimate automation but also for abuse such as phishing emails, fake support chats, synthetic identities, and code snippets that hide malicious logic.
Security teams care about AI output because it can be copied, edited, translated, or pasted into other systems, often losing visible signs of origin. Defenses may use provenance metadata, invisible watermarks, or policy checks to identify tagged content and route it through review. These controls do not stop misuse by themselves, but they help organizations classify content, enforce disclosure rules, and reduce the risk of trusting machine-generated material as if it were human-authored.