This session addresses a critical gap in high-stakes certification and licensure programs: traditional security monitoring that catches cheating only after the damage is done. Two operational papers demonstrate how proactive, data-driven approaches can shift programs from reactive case management to real-time intervention—limiting item exposure, reducing repeat violations, and strengthening the defensibility of credentialing decisions. The first applies machine-learning data drift detection and cosine similarity to identify emerging content compromise during live testing. The second uses enriched candidate account data to flag restricted candidates who attempt to circumvent delivery controls through duplicate account creation. Participants will leave with protocols for layered automated monitoring, threshold calibration, review workflows, and escalation rules—so that programs can detect threats earlier, intervene faster, and protect the validity and fairness of their programs.