Every blue team sits inside an operating model, a decision about which of its work is done by the organisation's own people and which is bought. AI changes that decision in three ways: it lowers the cost of the repetitive work that providers sell, it removes the volume that justified a large in-house shift, and it adds a party to the arrangement, the provider's AI, which may act on the customer's behalf at a level of autonomy nobody on the customer's side has decided. This chapter describes the operating models open to a European enterprise or public authority, what must stay in-house whichever model is chosen, how the provider's AI is made visible by contract, and how regulation, sovereignty, procurement and exit shape the arrangement. It concerns all five blue team functions: B1 Anticipate, because intelligence is often bought; B2 Detect, because detection content and its ownership are the first thing a contract either secures or loses; B3 Investigate, because the case record must outlive the provider; B4 Respond, because decision rights for containment cannot be outsourced with the monitoring; and B5 Learn, because a provider's findings must reach the customer's learning loop.
The chapter builds on three conclusions of the earlier volumes. AI Red Team, Chapters 8 and 28, concluded that vendor AI in security products and managed services is the Mode 0 blind spot: it may already act at an autonomy nobody decided, so its use and autonomy are established, recorded and required by contract before they are relied on (64, Nordqvist, 2026). AI Platform and Security, Chapter 13, concluded that digital sovereignty is a set of verifiable properties and not a location: knowing where workloads run, which law governs them, being able to move them and being able to continue if a provider fails (63, Nordqvist, 2026). AI Governance, Chapter 6, treated model origin as a risk question separate from hosting, decided by a risk-tiered position that is reviewed rather than set once, and Chapter 12 of the same volume showed that organisational design is architecture by other means, so that the shape of an organisation appears in the shape of its systems (62, Nordqvist, 2026).
The purpose of this section is to name the models, so that the later sections can say what each retains and what each puts at risk.
Primary topics explored
- Four models
- What AI changes
- What must be kept
- The provider's AI
- Regulation and third-party risk
- Sovereignty and data location
About AI Blue
AI Blue is Book 8 of The Transformed Architecture. It examines how the blue team, the people and functions that defend an organisation, is transformed when artificial intelligence takes part in their work: anticipating, detecting, investigating, responding and learning, the team's own AI and how to verify and protect it, operating models, roles, decision rights, regulation and measurement, and the careers and wellbeing of the people involved, for European enterprises and public authorities.
It builds on the conclusions of Architecture as Code, AI Governance, AI Platform and Security, AI Red Team, AI Threat, The Transformed Security Architect and AI Boss.
Chief information security officers, heads of security operations, SOC managers, analysts, detection engineers and incident commanders, security and AI architects, boards and management bodies, procurement leads, works councils and unions, data protection and audit functions, and public-sector leaders.
Gunnar Nordqvist
Conceived, directed and published the books of The Transformed Architecture.
Continue with the full chapter
Chapter 16: Operating Models: In-House, Managed, Hybrid and Sovereign Security Operations in AI Blue gives the detailed explanations, practical examples and implementation patterns behind this summary.
This whitepaper belongs to “The Team in the Organisation” and is one of 32 drawn from the manuscript.