THE AI CONTROL ARCHITECTURE. Cet article n’est pas disponible.
Langue : anglais
Edité par Lisa Nicole Publishing, 2026
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N° de réf. du vendeur L2-9798184327174
- Titre
- THE AI CONTROL ARCHITECTURE
- Auteur
- London, Kai
- Éditeur
- Lisa Nicole Publishing
- Année de publication
- 2026
- État de l'article
- New
- Reliure
- PAP
- Langue
- anglais
- ISBN à 13 chiffres
- 9798184327174
- Poids de l'article
- 889 grammes
Capability is loud. Control is quiet. And the gap between what your AI can do and what you can govern is now the most expensive liability in business.
A tribunal held Air Canada responsible for its own chatbot — "the algorithm did it" was no defence. A self-learning benefits algorithm helped topple the Dutch government. Zillow wrote off around half a billion dollars when a pricing model misjudged the market. In every case the technology worked. The control around it did not.
The AI Control Architecture is the board-to-build operating manual for governing artificial intelligence under the EU AI Act, ISO/IEC 42001, the NIST AI RMF and the 2030 regulatory horizon. It rejects the false choice between moving fast and staying safe and proves the opposite: control is the brake that lets you press the accelerator.
At its heart is the seven-layer CONTROL Architecture™ — Charter, Ownership, Navigation, Trust, Resilience, Operations, Leverage — deployed through a 90-Day System that takes you from ungoverned AI to a defensible, revenue-ready baseline in three phases, then into five sector playbooks (financial services, healthcare, hiring, customer-facing/generative, public sector).
You will learn how to:
Inside the toolkit: an AI policy template, a use-case risk-triage checklist, a governance RACI, a regulatory map, a model-documentation pack, a board-report one-pager, a CONTROL maturity scorecard, an ISO/IEC 42001 alignment map, a dissected catalogue of recent AI incidents (2018–2025), and a Claims & Sources provenance appendix — because if it cannot be evidenced, it cannot be defended.
Essential for chief executives, founders and board members; risk, compliance, legal and GRC leaders; CTOs, heads of AI and product leaders; and every operator who must turn an AI strategy slide into governed, revenue-ready reality.
Govern the model. Prove the control. Bank the value.
A tribunal held Air Canada responsible for its own chatbot — "the algorithm did it" was no defence. A self-learning benefits algorithm helped topple the Dutch government. Zillow wrote off around half a billion dollars when a pricing model misjudged the market. In every case the technology worked. The control around it did not.
The AI Control Architecture is the board-to-build operating manual for governing artificial intelligence under the EU AI Act, ISO/IEC 42001, the NIST AI RMF and the 2030 regulatory horizon. It rejects the false choice between moving fast and staying safe and proves the opposite: control is the brake that lets you press the accelerator.
At its heart is the seven-layer CONTROL Architecture™ — Charter, Ownership, Navigation, Trust, Resilience, Operations, Leverage — deployed through a 90-Day System that takes you from ungoverned AI to a defensible, revenue-ready baseline in three phases, then into five sector playbooks (financial services, healthcare, hiring, customer-facing/generative, public sector).
You will learn how to:
- WIN regulated, high-value contracts on the strength of evidenced, provable governance.
- AVOID director liability, voided insurance, a frozen AI pipeline, and the viral failure that drains trust overnight.
- PROVE — to a regulator, a buyer, and a board — that every model is owned, safe, lawful, and accountable.
Inside the toolkit: an AI policy template, a use-case risk-triage checklist, a governance RACI, a regulatory map, a model-documentation pack, a board-report one-pager, a CONTROL maturity scorecard, an ISO/IEC 42001 alignment map, a dissected catalogue of recent AI incidents (2018–2025), and a Claims & Sources provenance appendix — because if it cannot be evidenced, it cannot be defended.
Essential for chief executives, founders and board members; risk, compliance, legal and GRC leaders; CTOs, heads of AI and product leaders; and every operator who must turn an AI strategy slide into governed, revenue-ready reality.
Govern the model. Prove the control. Bank the value.
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