Output handling is the process of reviewing, validating, and safely using AI-generated content before it is reused in another system, document, or workflow. In cybersecurity, this matters because model output should be treated as untrusted data, not as finished truth. A generated answer may be wrong, biased, malformed, or influenced by prompt injection, and if it is copied into code, a policy, an email, or a command, it can cause real harm.
Weak output handling can turn a simple mistake into an injection or data-loss problem. For example, an LLM may produce a script with unsafe commands, a summary that exposes sensitive details, or text that breaks a downstream parser. Good defenses include human review, schema and type validation, allowlists, escaping, sandboxing, and clear limits on what AI output is allowed to do. In practice, output handling is one of the main controls that keeps generative AI useful without letting it become a security risk.