Article
You Can’t Assure What You Can’t See: Our New AI Monitoring Architecture Cheat Sheet
Most organizations monitoring AI systems today are watching the wrong layer. Infrastructure dashboards report healthy GPU utilization and acceptable latency while the failures that actually matter — an agent taking an unintended but syntactically valid action, retrieved content silently steering behavior, output quality drifting week over week — produce no error code at all.
That gap is the subject of our newest practitioner cheat sheet, AI Monitoring Architecture (ODA3-2026-07-CHT-SEC-002), now available as a free download.
What’s Inside
The cheat sheet is a single, full-depth reference covering:
- A vendor-neutral reference architecture — from instrumented application layer through collection, processing, dual storage (real-time detection plus an immutable evidence store), detection and analytics, response, and governance reporting
- Eight telemetry layers and seven monitoring domains, reconciled against each other so blind spots are identifiable rather than invisible
- The four signal classes — security, quality, operational, and compliance — and why building for one does not give you the other three for free
- A reference detection rule set, retention baselines, and a decision framework that scales monitoring depth to system risk
- Agentic and MCP-specific guidance, including why the OWASP MCP Top 10 now recognizes lack of audit and telemetry as a distinct risk category, and what the EchoLeak disclosure (CVE-2025-32711) teaches about the telemetry needed to detect zero-click injection against production AI assistants
- An assessor’s lens — verification versus validation of monitoring, evidence sufficiency, residual risk, and the three claims that should be treated as red flags in any maturity assessment
Every factual claim carries an inline evidence tier tag ([T1]–[T4]), and — in keeping with how we publish — the document states plainly what current evidence does not support. There is, as of this writing, no stable industry-standard telemetry schema for generative AI, no vendor-neutral benchmark for detection efficacy, and no field-validated data on real-world detection latency. We say so, because guidance that hides its own limits isn’t guidance.
Who It’s For
Written primarily for Security Architects, CISOs, AI Governance Leads, and Compliance Officers — with leadership-oriented framing and board-level questions called out separately throughout, so the same document serves a risk-committee conversation.
This is the second entry in our practitioner cheat sheet series, following Prompt Injection Mitigation Controls. Both are reviewed quarterly.