Abuse monitoring is a security control that watches for misuse, fraud, or policy violations by analyzing prompts, model outputs, metadata, and derived signals such as risk scores or classifier results. In AI and cloud services, it helps defenders detect behavior that may be harmful even when no single request looks suspicious on its own. The goal is to identify patterns, not just isolated events.
This matters because many attacks are coordinated or subtle: prompt injection, account sharing, spam generation, policy evasion, and automated probing often become visible only across multiple interactions. In defense, abuse monitoring can trigger rate limits, human review, or account restrictions, and it can support investigations without relying on raw content alone. In privacy-sensitive deployments, the challenge is to keep enough signal for detection while minimizing retention of customer data.