Every defensive capability in this book starts from a list: nobody can triage, scope, patch or restore an asset whose existence is a surprise. An estate that includes AI adds assets that the classic inventory of servers, endpoints and applications was never designed to hold: models with versions and origins, agents with identities and tool permissions, prompts and skills that steer behaviour, connectors that give a model reach into other systems, the data that grounds them, and AI features that suppliers switch on inside licensed products. Some of these assets act, so an inventory that records a server but not the agent running on it with write access to production records the wrong thing.
This chapter treats one CSF 2.0 Category, ID.AM Asset Management, whose outcome is that the assets enabling the organisation to achieve its business purposes, including data, hardware, software, systems, services and people, are identified and managed consistent with their relative importance to organisational objectives and the risk strategy (19, NIST, 2024). It reads the Category through the three focus areas of the NIST Cyber AI Profile (20, NIST, 2025).
The chapter builds on two conclusions from the earlier volumes. AI Governance concluded that governance is effective when it is codified, evidenced and proportionate to usage mode and risk (AI Governance, Chapters 2 and 25) (72, Nordqvist, 2026), so an inventory that does not record each AI asset's usage mode cannot support that proportionality. AI Platform and Security concluded that a platform is under control when every model call and tool call passes a policy point outside the model (AI Platform and Security, Chapter 25) (73, Nordqvist, 2026); that policy point is also the most reliable source of truth about AI use, and this chapter puts it to work as a discovery source.
Primary topics explored
- Assets in an estate that includes AI
- AI-assisted discovery and classification
- Shadow AI and supplier-enabled features
- Criticality and context
- Summary
- Sources
About AI Red Team
AI Red Team is Book 4 of The Transformed Architecture. It shows how large European enterprises and public authorities put artificial intelligence to work as an active part of cyber defence: AI-assisted red teaming and adversary emulation, red teaming of AI systems themselves, the AI-assisted security operations centre, detection engineering, threat intelligence, incident response and purple teaming, all under a lawful mandate and with evidence a supervisor can inspect.
It builds on the conclusions of Architecture as Code, AI Governance and AI Platform and Security.
CISOs, SOC leads, red and blue team practitioners, security architects, AI architects and technical decision-makers.
Gunnar Nordqvist
Conceived, directed and published the books of The Transformed Architecture.
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Chapter 9: Asset Management for an Estate That Includes AI in AI Red Team gives the detailed explanations, practical examples and implementation patterns behind this summary.
This whitepaper belongs to “Identify” and is one of 32 drawn from the manuscript.