Academy module 1 · ISO/IEC 27090 · Lesson 1 of 8
Map AI assets and lifecycle trust boundaries
About 8 minutes · Introductory draft · Checked 2026-10-23
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Picture the topic
Remember this: Protect the data, the model and the service around it.
Explore the anchors
Track training evaluation and operating inputs.
Concept matching activity
Match this task or description to its concept:
Protect artifacts parameters and approved versions.
Choose a concept before checking the explanation.
A quick way to study this topic
- Hide the explanation and redraw these three anchors from memory.
- Explain the difference between the anchors in your own words.
- Apply the distinction to the worked example below. Check the full lesson for assumptions and exceptions.
- Recall it again tomorrow, then after a few days. A memory hint is a retrieval cue, not a replacement for understanding.
Original academy illustrations and fictional exercises. These are learning simulations, not a real virtual machine, network assessment or measure of job readiness. Activities reset when you leave or reload; they do not change quiz scores.
What you will learn
- Track training evaluation and operating inputs.
- Protect artifacts parameters and approved versions.
- Restrict interfaces tools and deployment authority.
Concepts and distinctions
An AI system includes more than its model file. Data sources, labeling workflows, training jobs, evaluation sets, artifact stores, serving interfaces and connected tools can all create security boundaries. A failure in one dependency can undermine the behavior of a system whose model metrics previously looked good.
ISO/IEC 27090:2026 is guidance on AI-specific security threats and compromises across the lifecycle. It complements ordinary cybersecurity practices rather than replacing identity controls, configuration management, logging or incident handling. This course uses public scope information and original practical explanations; its module numbers are academy references, not a verified reproduction of the standard’s detailed clauses.
Apply the process and collect evidence
Create a simple system map naming owners, data flows, trust changes and permitted actions. Distinguish people who can supply data, approve a model, deploy a version or authorize a tool action. State the attacker’s assumed access before evaluating a threat. Connect technical effects to business and safety consequences, and retain evidence of the chosen safeguards and their limits.
Work through a scenario
Fictional facts for learning. This exercise is not scored.
A document assistant uses approved model weights but can read an unmanaged document store and call a broadly privileged service account.
Compare your reasoning
The model’s approval does not establish the integrity of retrieved content or the safety of tool permissions. Map the document, model and service boundaries separately. Restrict the connected identity and validate requested actions outside the model’s generated text.
Remember
- Protect the data, the model and the service around it.
- Track training evaluation and operating inputs.
- Restrict interfaces tools and deployment authority.
Orientation and further reading
- ISO: ISO/IEC 27090:2026 · Published AI-security guidance
- NIST AI 100-2 E2025: adversarial machine learning
Original examples and explanations. Background references do not establish complete exam coverage.
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