An AI-enabled identity workflow is an authentication or review process that uses machine learning to help decide who a user, device, or service is, or whether access should be approved. Common examples include risk-based login checks, identity proofing, fraud scoring, privileged access reviews, and automated account recovery decisions.
In cyber security, these workflows can improve speed and consistency by filtering large numbers of requests and flagging suspicious behavior in real time. They also matter because they become part of the trust boundary: if the model is inaccurate, biased, overpermissive, or poorly monitored, attackers may exploit weak signals, social engineering, or unusual-but-legitimate activity to get approved. Defenders reduce this risk by limiting automation to low-risk decisions, keeping human review for sensitive cases, logging model outputs, and regularly testing the workflow for drift and abuse.