Episode 31 — Monitor AI metrics to spot misuse, drift, and early incident signals (Task 18)
This episode explains how continuous monitoring turns AI security metrics into early warning signals, which is exactly what Task 18 is getting at when AAISM questions ask what you should measure and how you should respond when behavior changes. You’ll connect leading indicators like unusual prompt volume, spikes in denied requests, abnormal data access patterns, output toxicity flags, and sudden shifts in response quality to practical causes such as misuse, prompt injection attempts, configuration changes, model drift, or logging failures. We’ll walk through a scenario where a customer-facing assistant begins producing inconsistent answers after a vendor model update, and you’ll practice deciding what to validate first, how to separate real drift from measurement noise, and how to document the decision path so it is defensible. Best practices include defining thresholds and escalation triggers in advance, ensuring metrics map to control objectives, and avoiding “monitoring theater” where dashboards exist without owners or response playbooks. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your educational path. Also, if you want to stay up to date with the latest news, visit DailyCyber.News for a newsletter you can use, and a daily podcast you can commute with.
