Observability for AI Systems: Strengthening visibility for proactive risk detection
ID: 84b21d9e-27ea-5e68-b604-8e9e5951ac40
STIX ID: report--84b21d9e-27ea-5e68-b604-8e9e5951ac40
Feed Name: Microsoft Security
Date Published: 2026-03-18
Date Updated: 2026-04-28
Author: Angela Argentati, Matthew Dressman, Habiba Mohamed and Microsoft AI Security
## Executive Summary This Microsoft blog-style guidance explains why AI observability is essential for secure deployment of generative and agentic AI systems, describes AI-native telemetry (logs, metrics, traces, evaluation, governance), and provides five SDL-driven steps to operationalize observability—instrumentation from design, full-context capture, behavioral baselining, and unified agent governance.
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