Episode 22 — Telemetry & Observability
This episode explores telemetry and observability, emphasizing their importance in detecting anomalies, intrusions, and misuse in AI systems. Telemetry refers to the data collected from inputs, outputs, and system components, while observability describes the ability to interpret that data to understand system health and behavior. For exams, learners must know what to log, how to ensure logs are privacy-safe, and why observability is more than just storing records—it is about actionable visibility. The exam relevance lies in being able to define these concepts and connect them to monitoring, detection, and incident response in AI environments.
Applied examples include logging of prompt inputs to detect injection attempts, embedding honeytokens in training data to reveal leakage, and monitoring unusual traffic patterns in inference APIs. Best practices emphasize tamper-resistant logging, anonymization to protect sensitive user data, and alignment with compliance requirements. Troubleshooting considerations highlight challenges such as alert fatigue, storage overhead, or difficulty distinguishing malicious anomalies from normal model drift. Learners should be able to describe observability pipelines as both a compliance necessity and a defensive mechanism, ensuring that AI systems remain transparent, accountable, and resilient under attack. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your certification path.
