Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

A SOC analyst starts a shift with a queue full of alerts. The first few investigations reveal repeated authentication failures from the same service account, blocked network scans from a known internal scanner, and endpoint events that three different sensors reported separately. Somewhere in that queue may be a real compromise, but the analyst has to work through the noise before finding it.

Human oversight is the only control in an AI program that can stop working while producing exactly the same evidence as when it worked. A failed encryption control throws errors. A monitoring pipeline that breaks stops delivering alerts. A review step that has become a formality still generates approvals, timestamps and sign-offs, and the compliance file looks identical. ‍ The asymmetry makes the design question secondary to the measurement one.

A cyber loss model produces one distribution. How that distribution gets drawn changes what a reader can see in it, and the conventional projection hides the part most decisions depend on. ‍ The two views below contain identical data. One of them is close to unreadable for anything except the extreme tail, and the difference is worth understanding before the next time somebody asks what the number means. ‍

Agent observability tools capture traces, metrics, logs, and evaluations across AI agent workflows. They help teams reconstruct execution paths, inspect tool calls and handoffs, diagnose failures, and monitor latency, cost, and output quality. This guide also covers complementary security platforms that discover agents, enforce runtime policies, or control the privileges agents receive. Agents don’t fail in straight lines.