Operational AI security is the set of controls, monitoring, and incident-response practices used to secure AI systems while they are actively running in production. It goes beyond policy and approval checklists by focusing on what happens after deployment: access control, logging, drift detection, abuse detection, and safe rollback when behavior changes.
This matters because AI can be attacked through real-world inputs and workflows. Adversaries may try prompt injection, malicious data poisoning in connected pipelines, model misuse, or integration flaws that let an AI agent take unsafe actions. Defenders use operational AI security to test systems under realistic conditions, watch for abnormal outputs or access patterns, limit what the model can reach, and recover quickly if a model or workflow is abused. In practice, it treats AI as a live service that needs continuous security operations, not a one-time governance review.