Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Most teams buy detection on a single number. The datasheet says “millisecond detection,” the proof-of-concept fires the instant a test payload lands, and the box gets checked. Then a real AI agent incident runs in production, and the postmortem shows the attack completed its objective well before anyone contained it, even though the alert, technically, fired in milliseconds. The number was real. It just measured the wrong thing.

The first time a security team needs an AI agent audit trail is usually 72 hours after the agent has already done something it shouldn’t have. Detection fires. Someone pulls every relevant log from the SIEM (Kubernetes audit, container runtime, cloud audit) and three hours in realizes the events that actually matter were never written. Which prompt triggered the tool call. Which parameters the agent passed. Which output left the cluster.

Every AI-SPM tool runs posture and detection with a single arrow: runtime evidence flowing back to rank posture findings. The load-bearing direction runs the opposite way, and almost nothing runs it — posture flowing forward to tell the detection layer what an attack even looks like.