References and Sources

This section lists the sources cited throughout the book. The numbered source index, ordered by source number, is the canonical bibliography: every inline citation of the form (N, author or organisation, year) links to the entry with that number. The index is grouped by category so that readers can locate European legislation, standards, research, and practitioner literature quickly. All URLs were last checked on 2026-10-31.

Numbered source index

European legislation and policy

Source [1]: European Parliament and Council of the European Union. Regulation (EU) 2024/1689 laying down harmonised rules on artificial intelligence (Artificial Intelligence Act). Official Journal of the European Union, 12 July 2024. Available at: https://eur-lex.europa.eu/eli/reg/2024/1689/oj (accessed 2026-10-31). Referenced in: Chapter 1: Introduction to AI Governance, Chapter 2: From Chat to Agents: How Large Language Models Are Used, Chapter 3: The EU AI Act and the European Regulatory Landscape, Chapter 4: Fundamental Principles, Chapter 5: Prerequisites and Readiness for Enterprise AI, Chapter 6: Geopolitics and Model Origin, Chapter 7: Procurement, Vendor Management and Open Models, Chapter 8: AI Risk Management and Classification as Code, Chapter 9: AI Governance as Code, Chapter 10: Trustworthy AI: Transparency, Fairness and Human Oversight, Chapter 11: Evidence, Assurance and Audit, Chapter 12: Organisational Design for AI, Chapter 13: Decision Forums and Governance Bodies, Chapter 14: Competence, Roles and the AI Architect, Chapter 15: Change Management and Responsible Adoption, Chapter 16: Management as Code for AI Programmes, Chapter 16A: Governing AI Economics: Cost, Value and Portfolio Decisions, Chapter 17: AI in Public Authorities, Chapter 18: Case Studies, Chapter 19: A Cautionary Case: The OpenAI–Hugging Face Incident, Chapter 20: Anti-Patterns, Chapter 21: Best Practices and Lessons Learned, Chapter 22: Future Trends, Chapter 23: Nordvik Region and Hansa Mutual: Conclusions for Two Organisations, Chapter 24: Reflections on Each Part, Chapter 25: Conclusions: A Consolidated Analysis, Appendix A: EU AI Act Obligation Mapping, About the Author.
Source [2]: European Parliament and Council of the European Union. Regulation (EU) 2016/679 on the protection of natural persons with regard to the processing of personal data (General Data Protection Regulation). Official Journal of the European Union, 4 May 2016. Available at: https://eur-lex.europa.eu/eli/reg/2016/679/oj (accessed 2026-10-31). Referenced in: Chapter 1: Introduction to AI Governance, Chapter 3: The EU AI Act and the European Regulatory Landscape, Chapter 4: Fundamental Principles, Chapter 5: Prerequisites and Readiness for Enterprise AI, Chapter 6: Geopolitics and Model Origin, Chapter 8: AI Risk Management and Classification as Code, Chapter 9: AI Governance as Code, Chapter 10: Trustworthy AI: Transparency, Fairness and Human Oversight, Chapter 12: Organisational Design for AI, Chapter 13: Decision Forums and Governance Bodies, Chapter 14: Competence, Roles and the AI Architect, Chapter 15: Change Management and Responsible Adoption, Chapter 17: AI in Public Authorities, Chapter 18: Case Studies, Chapter 20: Anti-Patterns, Chapter 23: Nordvik Region and Hansa Mutual: Conclusions for Two Organisations, Chapter 25: Conclusions: A Consolidated Analysis.
Source [3]: European Parliament and Council of the European Union. Regulation (EU) 2022/2554 on digital operational resilience for the financial sector (DORA). Official Journal of the European Union, 27 December 2022. Available at: https://eur-lex.europa.eu/eli/reg/2022/2554/oj (accessed 2026-10-31). Referenced in: Chapter 1: Introduction to AI Governance, Chapter 3: The EU AI Act and the European Regulatory Landscape, Chapter 5: Prerequisites and Readiness for Enterprise AI, Chapter 6: Geopolitics and Model Origin, Chapter 7: Procurement, Vendor Management and Open Models, Chapter 11: Evidence, Assurance and Audit, Chapter 12: Organisational Design for AI, Chapter 13: Decision Forums and Governance Bodies, Chapter 14: Competence, Roles and the AI Architect, Chapter 16: Management as Code for AI Programmes, Chapter 16A: Governing AI Economics: Cost, Value and Portfolio Decisions, Chapter 18: Case Studies, Chapter 19: A Cautionary Case: The OpenAI–Hugging Face Incident, Chapter 20: Anti-Patterns, Chapter 23: Nordvik Region and Hansa Mutual: Conclusions for Two Organisations, Chapter 24: Reflections on Each Part, Chapter 25: Conclusions: A Consolidated Analysis.
Source [4]: European Parliament and Council of the European Union. Directive (EU) 2022/2555 on measures for a high common level of cybersecurity across the Union (NIS 2 Directive). Official Journal of the European Union, 27 December 2022. Available at: https://eur-lex.europa.eu/eli/dir/2022/2555/oj (accessed 2026-10-31). Referenced in: Chapter 1: Introduction to AI Governance, Chapter 3: The EU AI Act and the European Regulatory Landscape, Chapter 13: Decision Forums and Governance Bodies, Chapter 14: Competence, Roles and the AI Architect, Chapter 16: Management as Code for AI Programmes, Chapter 19: A Cautionary Case: The OpenAI–Hugging Face Incident, Chapter 24: Reflections on Each Part, Chapter 25: Conclusions: A Consolidated Analysis.
Source [5]: European Parliament and Council of the European Union. Regulation (EU) 2023/2854 on harmonised rules on fair access to and use of data (Data Act). Official Journal of the European Union, 22 December 2023. Available at: https://eur-lex.europa.eu/eli/reg/2023/2854/oj (accessed 2026-10-31). Referenced in: Chapter 3: The EU AI Act and the European Regulatory Landscape, Chapter 12: Organisational Design for AI.
Source [6]: European Parliament and Council of the European Union. Regulation (EU) 2024/2847 on horizontal cybersecurity requirements for products with digital elements (Cyber Resilience Act). Official Journal of the European Union, 20 November 2024. Available at: https://eur-lex.europa.eu/eli/reg/2024/2847/oj (accessed 2026-10-31). Referenced in: Chapter 3: The EU AI Act and the European Regulatory Landscape, Chapter 19: A Cautionary Case: The OpenAI–Hugging Face Incident, Chapter 22: Future Trends.
Source [7]: European Parliament and Council of the European Union. Directive (EU) 2019/1024 on open data and the re-use of public sector information (Open Data Directive). Official Journal of the European Union, 26 June 2019. Available at: https://eur-lex.europa.eu/eli/dir/2019/1024/oj (accessed 2026-10-31). Referenced in: Chapter 17: AI in Public Authorities.
Source [8]: European Parliament and Council of the European Union. Regulation (EU) 2024/903 laying down measures for a high level of public sector interoperability across the Union (Interoperable Europe Act). Official Journal of the European Union, 22 March 2024. Available at: https://eur-lex.europa.eu/eli/reg/2024/903/oj (accessed 2026-10-31). Referenced in: Chapter 17: AI in Public Authorities.
Source [9]: European Parliament and Council of the European Union. Directive 2014/24/EU on public procurement. Official Journal of the European Union, 28 March 2014. Available at: https://eur-lex.europa.eu/eli/dir/2014/24/oj (accessed 2026-10-31). Referenced in: Chapter 1: Introduction to AI Governance, Chapter 5: Prerequisites and Readiness for Enterprise AI, Chapter 7: Procurement, Vendor Management and Open Models, Chapter 16A: Governing AI Economics: Cost, Value and Portfolio Decisions, Chapter 17: AI in Public Authorities, Chapter 23: Nordvik Region and Hansa Mutual: Conclusions for Two Organisations, Chapter 25: Conclusions: A Consolidated Analysis.
Source [10]: European Parliament and Council of the European Union. Regulation (EU) 2024/1183 amending Regulation (EU) No 910/2014 as regards establishing the European Digital Identity Framework. Official Journal of the European Union, 30 April 2024. Available at: https://eur-lex.europa.eu/eli/reg/2024/1183/oj (accessed 2026-10-31). Referenced in: Chapter 17: AI in Public Authorities.
Source [11]: European Parliament and Council of the European Union. Regulation (EU) 2025/327 on the European Health Data Space. Official Journal of the European Union, 5 March 2025. Available at: https://eur-lex.europa.eu/eli/reg/2025/327/oj (accessed 2026-10-31). Referenced in: Chapter 18: Case Studies.
Source [12]: High-Level Expert Group on Artificial Intelligence. Ethics Guidelines for Trustworthy AI. European Commission, 8 April 2019. Available at: https://digital-strategy.ec.europa.eu/en/library/ethics-guidelines-trustworthy-ai (accessed 2026-10-31). Referenced in: Chapter 5: Prerequisites and Readiness for Enterprise AI, Chapter 10: Trustworthy AI: Transparency, Fairness and Human Oversight.
Source [13]: High-Level Expert Group on Artificial Intelligence. Assessment List for Trustworthy Artificial Intelligence (ALTAI) for self-assessment. European Commission, 17 July 2020. Available at: https://digital-strategy.ec.europa.eu/en/library/assessment-list-trustworthy-artificial-intelligence-altai-self-assessment (accessed 2026-10-31). Referenced in: Chapter 5: Prerequisites and Readiness for Enterprise AI, Chapter 8: AI Risk Management and Classification as Code, Chapter 10: Trustworthy AI: Transparency, Fairness and Human Oversight.
Source [14]: European Commission. The General-Purpose AI Code of Practice. European AI Office, 10 July 2025. Available at: https://digital-strategy.ec.europa.eu/en/policies/contents-code-gpai (accessed 2026-10-31). Referenced in: Chapter 3: The EU AI Act and the European Regulatory Landscape, Chapter 7: Procurement, Vendor Management and Open Models, Chapter 19: A Cautionary Case: The OpenAI–Hugging Face Incident, Chapter 22: Future Trends.
Source [15]: European Commission. European AI Office. Shaping Europe's digital future policy pages. Available at: https://digital-strategy.ec.europa.eu/en/policies/ai-office (accessed 2026-10-31). Referenced in: Chapter 3: The EU AI Act and the European Regulatory Landscape.
Source [16]: European Commission. AI Continent Action Plan. COM(2025) 165 final, 9 April 2025. Available at: https://digital-strategy.ec.europa.eu/en/library/ai-continent-action-plan (accessed 2026-10-31). Referenced in: Chapter 1: Introduction to AI Governance, Chapter 22: Future Trends.
Source [17]: European Commission. New European Interoperability Framework: Promoting seamless services and data flows for European public administrations. Publications Office of the European Union, 2017. Available at: https://ec.europa.eu/isa2/eif_en/ (accessed 2026-10-31). Referenced in: Chapter 17: AI in Public Authorities.
Source [18]: European Commission. EU model contractual AI clauses to pilot in procurements of AI. Public Buyers Community, Procurement of AI community, 2023 (updated 2025). Available at: https://public-buyers-community.ec.europa.eu/communities/procurement-ai/resources/eu-model-contractual-ai-clauses-pilot-procurements-ai (accessed 2026-10-31). Referenced in: Chapter 7: Procurement, Vendor Management and Open Models, Chapter 17: AI in Public Authorities, Chapter 21: Best Practices and Lessons Learned, Chapter 23: Nordvik Region and Hansa Mutual: Conclusions for Two Organisations, Chapter 24: Reflections on Each Part, Chapter 25: Conclusions: A Consolidated Analysis.
Source [19]: European Data Protection Board. Opinion 28/2024 on certain data protection aspects related to the processing of personal data in the context of AI models. Adopted 17 December 2024. Available at: https://www.edpb.europa.eu/our-work-tools/our-documents/opinion-board-art-64/opinion-282024-certain-data-protection-aspects_en (accessed 2026-10-31). Referenced in: Chapter 18: Case Studies.
Source [20]: Court of Justice of the European Union. Judgment in Case C-311/18, Data Protection Commissioner v Facebook Ireland Limited and Maximillian Schrems ("Schrems II"). 16 July 2020. Available at: https://curia.europa.eu/juris/liste.jsf?num=C-311/18 (accessed 2026-10-31). Referenced in: Chapter 1: Introduction to AI Governance, Chapter 6: Geopolitics and Model Origin.
Source [21]: Council of Europe. Framework Convention on Artificial Intelligence and Human Rights, Democracy and the Rule of Law (CETS No. 225). Opened for signature 5 September 2024. Available at: https://www.coe.int/en/web/artificial-intelligence/the-framework-convention-on-artificial-intelligence (accessed 2026-10-31). Referenced in: Chapter 1: Introduction to AI Governance, Chapter 3: The EU AI Act and the European Regulatory Landscape, Chapter 17: AI in Public Authorities, Chapter 22: Future Trends.
Source [22]: Organisation for Economic Co-operation and Development. Recommendation of the Council on Artificial Intelligence (OECD/LEGAL/0449). Adopted 22 May 2019, amended 3 May 2024. Available at: https://legalinstruments.oecd.org/en/instruments/OECD-LEGAL-0449 (accessed 2026-10-31). Referenced in: Chapter 1: Introduction to AI Governance, Chapter 3: The EU AI Act and the European Regulatory Landscape.
Source [23]: Ministry of the Interior and Kingdom Relations (Netherlands). The Algorithm Register of the Dutch government. Available at: https://algoritmes.overheid.nl/en (accessed 2026-10-31). Referenced in: Chapter 5: Prerequisites and Readiness for Enterprise AI, Chapter 9: AI Governance as Code, Chapter 17: AI in Public Authorities, Chapter 18: Case Studies, Chapter 24: Reflections on Each Part, Chapter 25: Conclusions: A Consolidated Analysis.
Source [24]: CEN-CENELEC. Joint Technical Committee 21 – Artificial Intelligence. Available at: https://www.cencenelec.eu/areas-of-work/cen-cenelec-topics/artificial-intelligence/ (accessed 2026-10-31). Referenced in: Chapter 3: The EU AI Act and the European Regulatory Landscape, Chapter 9: AI Governance as Code.
Source [25]: EuroHPC Joint Undertaking. AI Factories. Available at: https://eurohpc-ju.europa.eu/ai-factories_en (accessed 2026-10-31). Referenced in: Chapter 6: Geopolitics and Model Origin, Chapter 21: Best Practices and Lessons Learned, Chapter 22: Future Trends.
Source [26]: Gaia-X European Association for Data and Cloud AISBL. Gaia-X: A Federated Secure Data Infrastructure. Available at: https://gaia-x.eu/ (accessed 2026-10-31). Referenced in: Chapter 6: Geopolitics and Model Origin.
Source [27]: European Parliament and Council of the European Union. Regulation (EU) 2026/1744 amending Regulations (EU) 2024/1689, (EU) 2018/1139 and (EU) 2023/1230 as regards the simplification of the implementation of harmonised rules on artificial intelligence (Digital Omnibus on AI). Official Journal of the European Union, 24 July 2026. Available at: https://eur-lex.europa.eu/eli/reg/2026/1744/oj (accessed 2026-10-31). Referenced in: Chapter 1: Introduction to AI Governance, Chapter 2: From Chat to Agents: How Large Language Models Are Used, Chapter 3: The EU AI Act and the European Regulatory Landscape, Chapter 5: Prerequisites and Readiness for Enterprise AI, Chapter 10: Trustworthy AI: Transparency, Fairness and Human Oversight, Chapter 13: Decision Forums and Governance Bodies, Chapter 14: Competence, Roles and the AI Architect, Chapter 22: Future Trends, Chapter 23: Nordvik Region and Hansa Mutual: Conclusions for Two Organisations, Chapter 24: Reflections on Each Part, Chapter 25: Conclusions: A Consolidated Analysis.
Source [28]: European Parliament and Council of the European Union. Regulation (EU) 2022/868 on European data governance (Data Governance Act). Official Journal of the European Union, 3 June 2022. Available at: https://eur-lex.europa.eu/eli/reg/2022/868/oj (accessed 2026-10-31). Referenced in: Chapter 3: The EU AI Act and the European Regulatory Landscape.

Standards and frameworks

Source [29]: UNESCO. Recommendation on the Ethics of Artificial Intelligence. Adopted by the UNESCO General Conference, November 2021. Available at: https://www.unesco.org/en/articles/recommendation-ethics-artificial-intelligence (accessed 2026-10-31). Referenced in: Chapter 5: Prerequisites and Readiness for Enterprise AI, Chapter 10: Trustworthy AI: Transparency, Fairness and Human Oversight.
Source [30]: UNESCO (2023). Readiness Assessment Methodology: A Tool of the Recommendation on the Ethics of Artificial Intelligence. Paris: UNESCO, 28 August 2023. Available at: https://www.unesco.org/en/articles/readiness-assessment-methodology-tool-recommendation-ethics-artificial-intelligence; programme page with country reports at https://www.unesco.org/ethics-ai/en/ram (accessed 2026-10-31). Referenced in: Chapter 5: Prerequisites and Readiness for Enterprise AI, Appendix B: AI Governance Maturity Model, Appendix C: Templates and Tools.
Source [31]: UNESCO. Ethical Impact Assessment: A Tool of the Recommendation on the Ethics of Artificial Intelligence. Global AI Ethics and Governance Observatory. Available at: https://www.unesco.org/ethics-ai/en/eia (accessed 2026-10-31). Referenced in: Chapter 5: Prerequisites and Readiness for Enterprise AI, Chapter 10: Trustworthy AI: Transparency, Fairness and Human Oversight, Appendix B: AI Governance Maturity Model, Appendix C: Templates and Tools.
Source [32]: International Organization for Standardization and International Electrotechnical Commission. ISO/IEC 42001:2023 Information technology — Artificial intelligence — Management system. Geneva: ISO, December 2023. Available at: https://www.iso.org/standard/42001 (accessed 2026-10-31). Referenced in: Chapter 1: Introduction to AI Governance, Chapter 5: Prerequisites and Readiness for Enterprise AI, Chapter 9: AI Governance as Code, Chapter 11: Evidence, Assurance and Audit, Chapter 12: Organisational Design for AI, Chapter 13: Decision Forums and Governance Bodies, Chapter 16: Management as Code for AI Programmes, Chapter 21: Best Practices and Lessons Learned, Chapter 22: Future Trends, Appendix A: EU AI Act Obligation Mapping.
Source [33]: International Organization for Standardization and International Electrotechnical Commission. ISO/IEC 23894:2023 Information technology — Artificial intelligence — Guidance on risk management. Geneva: ISO, February 2023. Available at: https://www.iso.org/standard/77304.html (accessed 2026-10-31). Referenced in: Chapter 8: AI Risk Management and Classification as Code.
Source [34]: International Organization for Standardization and International Electrotechnical Commission. ISO/IEC 22989:2022 Information technology — Artificial intelligence — Artificial intelligence concepts and terminology. Geneva: ISO, July 2022. Available at: https://www.iso.org/standard/74296.html (accessed 2026-10-31). Referenced in: Chapter 1: Introduction to AI Governance.
Source [35]: International Organization for Standardization and International Electrotechnical Commission. ISO/IEC 42005:2025 Information technology — Artificial intelligence — AI system impact assessment. Geneva: ISO, 2025. Available at: https://www.iso.org/standard/44545.html (accessed 2026-10-31). Referenced in: Chapter 8: AI Risk Management and Classification as Code.
Source [36]: National Institute of Standards and Technology. Artificial Intelligence Risk Management Framework (AI RMF 1.0). NIST AI 100-1, January 2023. Available at: https://doi.org/10.6028/NIST.AI.100-1 (accessed 2026-10-31). Referenced in: Chapter 1: Introduction to AI Governance, Chapter 4: Fundamental Principles, Chapter 8: AI Risk Management and Classification as Code, Chapter 16A: Governing AI Economics: Cost, Value and Portfolio Decisions, Chapter 21: Best Practices and Lessons Learned, Chapter 22: Future Trends.
Source [37]: National Institute of Standards and Technology. Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile. NIST AI 600-1, July 2024. Available at: https://doi.org/10.6028/NIST.AI.600-1 (accessed 2026-10-31). Referenced in: Chapter 8: AI Risk Management and Classification as Code.
Source [38]: OWASP Foundation. OWASP Top 10 for Large Language Model Applications, 2025 edition. OWASP GenAI Security Project. Available at: https://genai.owasp.org/llm-top-10/ (accessed 2026-10-31). Referenced in: Chapter 2: From Chat to Agents: How Large Language Models Are Used, Chapter 10: Trustworthy AI: Transparency, Fairness and Human Oversight, Chapter 16A: Governing AI Economics: Cost, Value and Portfolio Decisions, Chapter 21: Best Practices and Lessons Learned.
Source [39]: Open Source Initiative. The Open Source AI Definition, version 1.0. 28 October 2024. Available at: https://opensource.org/ai/open-source-ai-definition (accessed 2026-10-31). Referenced in: Chapter 6: Geopolitics and Model Origin, Chapter 7: Procurement, Vendor Management and Open Models.
Source [40]: Open Policy Agent Contributors. Open Policy Agent Documentation. Cloud Native Computing Foundation. Available at: https://www.openpolicyagent.org/docs/ (accessed 2026-10-31). Referenced in: Chapter 8: AI Risk Management and Classification as Code, Chapter 9: AI Governance as Code.
Source [41]: OpenTelemetry Authors. OpenTelemetry Documentation, including the Semantic Conventions for Generative AI systems. Cloud Native Computing Foundation. Available at: https://opentelemetry.io/docs/ (accessed 2026-10-31). Referenced in: Chapter 11: Evidence, Assurance and Audit, Chapter 21: Best Practices and Lessons Learned.
Source [42]: MITRE Corporation. MITRE ATLAS: Adversarial Threat Landscape for AI Systems. Available at: https://atlas.mitre.org/ (accessed 2026-10-31). Referenced in: Chapter 21: Best Practices and Lessons Learned.
Source [43]: OWASP Foundation. CycloneDX Machine Learning Bill of Materials (AI/ML-BOM). Available at: https://cyclonedx.org/capabilities/mlbom/ (accessed 2026-10-31). Referenced in: Chapter 6: Geopolitics and Model Origin, Chapter 18: Case Studies, Chapter 19: A Cautionary Case: The OpenAI–Hugging Face Incident, Appendix A: EU AI Act Obligation Mapping.
Source [44]: Green Software Foundation. Software Carbon Intensity (SCI) Specification (ISO/IEC 21031:2024). Available at: https://sci.greensoftware.foundation/ (accessed 2026-10-31). Referenced in: Appendix B: AI Governance Maturity Model.
Source [45]: Open Source Security Foundation. Supply-chain Levels for Software Artifacts (SLSA). Available at: https://slsa.dev/ (accessed 2026-10-31). Referenced in: Chapter 21: Best Practices and Lessons Learned.

Research literature

Source [46]: Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., Spitzer, E., Raji, I. D. & Gebru, T. (2019). Model Cards for Model Reporting. Proceedings of the Conference on Fairness, Accountability, and Transparency (FAT* '19), pp. 220–229. Available at: https://doi.org/10.1145/3287560.3287596 (accessed 2026-10-31). Referenced in: Chapter 4: Fundamental Principles, Chapter 11: Evidence, Assurance and Audit, Chapter 13: Decision Forums and Governance Bodies.
Source [47]: Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Daumé III, H. & Crawford, K. (2021). Datasheets for Datasets. Communications of the ACM, 64(12), pp. 86–92. Available at: https://doi.org/10.1145/3458723 (accessed 2026-10-31). Referenced in: Chapter 4: Fundamental Principles.
Source [48]: Sculley, D., Holt, G., Golovin, D., Davydov, E., Phillips, T., Ebner, D., Chaudhary, V., Young, M., Crespo, J.-F. & Dennison, D. (2015). Hidden Technical Debt in Machine Learning Systems. Advances in Neural Information Processing Systems 28 (NeurIPS 2015). Available at: https://papers.nips.cc/paper/2015/hash/86df7dcfd896fcaf2674f757a2463eba-Abstract.html (accessed 2026-10-31). Referenced in: Chapter 1: Introduction to AI Governance, Chapter 4: Fundamental Principles, Chapter 5: Prerequisites and Readiness for Enterprise AI, Chapter 16A: Governing AI Economics: Cost, Value and Portfolio Decisions, Chapter 20: Anti-Patterns.
Source [49]: Bommasani, R. et al. (2021). On the Opportunities and Risks of Foundation Models. Stanford Center for Research on Foundation Models. Available at: https://arxiv.org/abs/2108.07258 (accessed 2026-10-31). Referenced in: Chapter 1: Introduction to AI Governance.
Source [50]: Bender, E. M., Gebru, T., McMillan-Major, A. & Shmitchell, S. (2021). On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (FAccT '21), pp. 610–623. Available at: https://doi.org/10.1145/3442188.3445922 (accessed 2026-10-31). Referenced in: Chapter 10: Trustworthy AI: Transparency, Fairness and Human Oversight.
Source [51]: Greshake, K., Abdelnabi, S., Mishra, S., Endres, C., Holz, T. & Fritz, M. (2023). Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection. Proceedings of the 16th ACM Workshop on Artificial Intelligence and Security (AISec '23). Available at: https://arxiv.org/abs/2302.12173 (accessed 2026-10-31). Referenced in: Chapter 2: From Chat to Agents: How Large Language Models Are Used, Chapter 20: Anti-Patterns.
Source [52]: Zaharia, M., Khattab, O., Chen, L., Davis, J. Q., Miller, H., Potts, C., Zou, J., Carbin, M., Frankle, J., Rao, N. & Ghodsi, A. (2024). The Shift from Models to Compound AI Systems. Berkeley Artificial Intelligence Research blog, 18 February 2024. Available at: https://bair.berkeley.edu/blog/2024/02/18/compound-ai-systems/ (accessed 2026-10-31). Referenced in: Chapter 1: Introduction to AI Governance, Chapter 20: Anti-Patterns, Chapter 22: Future Trends.
Source [53]: Anthropic. Building effective AI agents. Anthropic research blog, 19 December 2024. Available at: https://www.anthropic.com/research/building-effective-agents (accessed 2026-10-31). Referenced in: Chapter 20: Anti-Patterns.
Source [54]: Breck, E., Cai, S., Nielsen, E., Salib, M. & Sculley, D. (2017). The ML Test Score: A Rubric for ML Production Readiness and Technical Debt Reduction. Proceedings of the 2017 IEEE International Conference on Big Data, pp. 1123–1132. Available at: https://doi.org/10.1109/BigData.2017.8258038 (accessed 2026-10-31). Referenced in: Chapter 21: Best Practices and Lessons Learned.
Source [55]: Paleyes, A., Urma, R.-G. & Lawrence, N. D. (2022). Challenges in Deploying Machine Learning: A Survey of Case Studies. ACM Computing Surveys, 55(6), Article 114. Available at: https://doi.org/10.1145/3533378 (accessed 2026-10-31). Referenced in: Chapter 5: Prerequisites and Readiness for Enterprise AI, Chapter 20: Anti-Patterns, Chapter 24: Reflections on Each Part.
Source [56]: Selbst, A. D., boyd, d., Friedler, S. A., Venkatasubramanian, S. & Vertesi, J. (2019). Fairness and Abstraction in Sociotechnical Systems. Proceedings of the Conference on Fairness, Accountability, and Transparency (FAT* '19), pp. 59–68. Available at: https://doi.org/10.1145/3287560.3287598 (accessed 2026-10-31). Referenced in: Chapter 10: Trustworthy AI: Transparency, Fairness and Human Oversight.
Source [57]: Raji, I. D., Smart, A., White, R. N., Mitchell, M., Gebru, T., Hutchinson, B., Smith-Loud, J., Theron, D. & Barnes, P. (2020). Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing. Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* '20), pp. 33–44. Available at: https://doi.org/10.1145/3351095.3372873 (accessed 2026-10-31). Referenced in: Chapter 9: AI Governance as Code, Chapter 11: Evidence, Assurance and Audit, Chapter 21: Best Practices and Lessons Learned, Chapter 25: Conclusions: A Consolidated Analysis.
Source [58]: Westrum, R. (2004). A typology of organisational cultures. BMJ Quality & Safety, 13(suppl 2), pp. ii22–ii27. Available at: https://doi.org/10.1136/qshc.2003.009522 (accessed 2026-10-31). Referenced in: Chapter 5: Prerequisites and Readiness for Enterprise AI.
Source [59]: Conway, M. E. (1968). How Do Committees Invent? Datamation, 14(4), pp. 28–31. Available at: https://www.melconway.com/Home/Committees_Paper.html (accessed 2026-10-31). Referenced in: Chapter 12: Organisational Design for AI.
Source [60]: Stanford Institute for Human-Centered Artificial Intelligence. Artificial Intelligence Index Report 2025. Stanford University, April 2025. Available at: https://aiindex.stanford.edu/report/ (accessed 2026-10-31). Referenced in: Chapter 16A: Governing AI Economics: Cost, Value and Portfolio Decisions.
Source [61]: Schlatter, J., Weinstein-Raun, B. & Ladish, J. (2026). Incomplete Tasks Induce Shutdown Resistance in Some Frontier LLMs. Transactions on Machine Learning Research; arXiv:2509.14260v2. Available at: https://doi.org/10.48550/arXiv.2509.14260 (accessed 2026-11-01). Referenced in: Chapter 13: Decision Forums and Governance Bodies.
Source [62]: Gama, J., Žliobaitė, I., Bifet, A., Pechenizkiy, M. & Bouchachia, A. (2014). A survey on concept drift adaptation. ACM Computing Surveys, 46(4), Article 44. Available at: https://doi.org/10.1145/2523813 (accessed 2026-10-31). Referenced in: Chapter 8: AI Risk Management and Classification as Code.
Source [63]: Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W., Rocktäschel, T., Riedel, S. & Kiela, D. (2020). Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. Advances in Neural Information Processing Systems 33 (NeurIPS 2020). Available at: https://arxiv.org/abs/2005.11401 (accessed 2026-10-31). Referenced in: Chapter 18: Case Studies.
Source [64]: Zheng, L., Chiang, W.-L., Sheng, Y., Zhuang, S., Wu, Z., Zhuang, Y., Lin, Z., Li, Z., Li, D., Xing, E. P., Zhang, H., Gonzalez, J. E. & Stoica, I. (2023). Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena. Advances in Neural Information Processing Systems 36 (NeurIPS 2023). Available at: https://arxiv.org/abs/2306.05685 (accessed 2026-10-31). Referenced in: Chapter 21: Best Practices and Lessons Learned.
Source [65]: Lynch, A., Wright, B., Larson, C., Troy, K. K., Ritchie, S. J., Mindermann, S., Perez, E. & Hubinger, E. (2025). Agentic Misalignment: How LLMs could be insider threats. Anthropic, 20 June 2025; arXiv:2510.05179. Available at: https://www.anthropic.com/research/agentic-misalignment (accessed 2026-11-01). Referenced in: Chapter 25: Conclusions: A Consolidated Analysis.

Books and practitioner literature

Source [66]: Nordqvist, G. (2025). Architecture as Code. The Transformed Architecture, Book 1. Published at aac.geon.se. Available at: https://aac.geon.se/ (accessed 2026-10-31). Referenced in: Chapter 1: Introduction to AI Governance, Chapter 2: From Chat to Agents: How Large Language Models Are Used, Chapter 4: Fundamental Principles, Chapter 5: Prerequisites and Readiness for Enterprise AI, Chapter 6: Geopolitics and Model Origin, Chapter 7: Procurement, Vendor Management and Open Models, Chapter 8: AI Risk Management and Classification as Code, Chapter 9: AI Governance as Code, Chapter 10: Trustworthy AI: Transparency, Fairness and Human Oversight, Chapter 11: Evidence, Assurance and Audit, Chapter 12: Organisational Design for AI, Chapter 13: Decision Forums and Governance Bodies, Chapter 14: Competence, Roles and the AI Architect, Chapter 15: Change Management and Responsible Adoption, Chapter 16: Management as Code for AI Programmes, Chapter 17: AI in Public Authorities, Chapter 18: Case Studies, Chapter 20: Anti-Patterns, Chapter 21: Best Practices and Lessons Learned, Chapter 22: Future Trends, Chapter 25: Conclusions: A Consolidated Analysis, Appendix A: EU AI Act Obligation Mapping, Appendix B: AI Governance Maturity Model, Appendix C: Templates and Tools, About the Author.
Source [67]: Huyen, C. (2022). Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications. Sebastopol, CA: O'Reilly Media. ISBN 978-1-0981-0796-3. Referenced in: Chapter 14: Competence, Roles and the AI Architect.
Source [68]: Huyen, C. (2025). AI Engineering: Building Applications with Foundation Models. Sebastopol, CA: O'Reilly Media. ISBN 978-1-0981-6630-4. Referenced in: Chapter 14: Competence, Roles and the AI Architect.
Source [69]: Skelton, M. & Pais, M. (2019). Team Topologies: Organizing Business and Technology Teams for Fast Flow. Portland, OR: IT Revolution Press. ISBN 978-1-942788-81-2. Referenced in: Chapter 12: Organisational Design for AI, Chapter 14: Competence, Roles and the AI Architect, Chapter 16: Management as Code for AI Programmes, Chapter 20: Anti-Patterns.
Source [70]: Forsgren, N., Humble, J. & Kim, G. (2018). Accelerate: The Science of Lean Software and DevOps. Portland, OR: IT Revolution Press. ISBN 978-1-942788-33-1. Referenced in: Chapter 5: Prerequisites and Readiness for Enterprise AI, Chapter 14: Competence, Roles and the AI Architect, Chapter 16: Management as Code for AI Programmes, Chapter 16A: Governing AI Economics: Cost, Value and Portfolio Decisions, Chapter 20: Anti-Patterns, Chapter 21: Best Practices and Lessons Learned, Chapter 25: Conclusions: A Consolidated Analysis.
Source [71]: Richards, M. & Ford, N. (2020). Fundamentals of Software Architecture: An Engineering Approach. Sebastopol, CA: O'Reilly Media. ISBN 978-1-4920-4345-4. Referenced in: Chapter 4: Fundamental Principles.
Source [72]: Barocas, S., Hardt, M. & Narayanan, A. (2023). Fairness and Machine Learning: Limitations and Opportunities. Cambridge, MA: MIT Press. ISBN 978-0-262-04861-3. Available at: https://fairmlbook.org/ (accessed 2026-10-31). Referenced in: Chapter 10: Trustworthy AI: Transparency, Fairness and Human Oversight.
Source [73]: Molnar, C. (2022). Interpretable Machine Learning: A Guide for Making Black Box Models Explainable. 2nd edition. Available at: https://christophm.github.io/interpretable-ml-book/ (accessed 2026-10-31). Referenced in: Chapter 10: Trustworthy AI: Transparency, Fairness and Human Oversight.
Source [74]: Shneiderman, B. (2022). Human-Centered AI. Oxford: Oxford University Press. ISBN 978-0-19-284529-0. Referenced in: Chapter 10: Trustworthy AI: Transparency, Fairness and Human Oversight, Chapter 15: Change Management and Responsible Adoption, Chapter 25: Conclusions: A Consolidated Analysis.
Source [75]: Kotter, J. P. (2012). Leading Change. Boston, MA: Harvard Business Review Press. ISBN 978-1-4221-8643-5. Referenced in: Chapter 15: Change Management and Responsible Adoption.
Source [76]: Rogers, E. M. (2003). Diffusion of Innovations. 5th edition. New York, NY: Free Press. ISBN 978-0-7432-2209-9. Referenced in: Chapter 15: Change Management and Responsible Adoption.
Source [77]: Kvadrat AB. Kvadrat – Sweden's largest network of self-employed consultants. Available at: https://www.kvadrat.se/ (accessed 2026-10-31). Referenced in: About the Author.
Source [78]: Nygard, M. Documenting Architecture Decisions. Cognitect blog, 15 November 2011. Available at: https://cognitect.com/blog/2011/11/15/documenting-architecture-decisions (accessed 2026-10-31). Referenced in: Chapter 3: The EU AI Act and the European Regulatory Landscape, Chapter 13: Decision Forums and Governance Bodies, Chapter 16: Management as Code for AI Programmes.
Source [79]: Dehghani, Z. (2022). Data Mesh: Delivering Data-Driven Value at Scale. Sebastopol, CA: O'Reilly Media. ISBN 978-1-4920-9239-1. Referenced in: Chapter 5: Prerequisites and Readiness for Enterprise AI.

AI-enabled threats and vulnerability management

Source [80]: Charrier, C. & Weiner, R. (2024). How Low Can You Go? An Analysis of 2023 Time-to-Exploit Trends. Mandiant, Google Cloud Blog, 15 October 2024. Available at: https://cloud.google.com/blog/topics/threat-intelligence/time-to-exploit-trends-2023 (accessed 2026-11-01). Referenced in: Chapter 24: Reflections on Each Part.
Source [81]: Mandiant (2026). M-Trends 2026: Data, Insights, and Strategies From the Frontlines. Google Cloud Blog, 23 March 2026. Available at: https://cloud.google.com/blog/topics/threat-intelligence/m-trends-2026 (accessed 2026-11-01). Referenced in: Chapter 22: Future Trends, Chapter 23: Nordvik Region and Hansa Mutual: Conclusions for Two Organisations, Chapter 24: Reflections on Each Part.

Agents that exceed their mandate and AI incidents

Source [82]: Von Arx, S., Chan, L. & Barnes, B. (2025). Recent Frontier Models Are Reward Hacking. METR, 5 June 2025. Available at: https://metr.org/blog/2025-06-05-recent-reward-hacking/ (accessed 2026-11-01). Referenced in: Chapter 19: A Cautionary Case: The OpenAI–Hugging Face Incident, Chapter 24: Reflections on Each Part, Chapter 25: Conclusions: A Consolidated Analysis.
Source [83]: Nolan, B. (2025). An AI-powered coding tool wiped out a software company's database, then apologized for a 'catastrophic failure on my part'. Fortune, 23 July 2025. Available at: https://fortune.com/2025/07/23/ai-coding-tool-replit-wiped-database-called-it-a-catastrophic-failure/ (accessed 2026-11-01). Referenced in: Chapter 2: From Chat to Agents: How Large Language Models Are Used.
Source [84]: OWASP Foundation (2025). OWASP Top 10 for Agentic Applications for 2026. OWASP GenAI Security Project, 9 December 2025. Available at: https://genai.owasp.org/resource/owasp-top-10-for-agentic-applications-for-2026/ (accessed 2026-11-01). Referenced in: Chapter 2: From Chat to Agents: How Large Language Models Are Used, Chapter 16A: Governing AI Economics: Cost, Value and Portfolio Decisions.
Source [85]: AI Security Institute (2026). Incident Report: unsanctioned agent behaviour during cyber testing. UK AISI, 4 August 2026. Available at: https://www.aisi.gov.uk/blog/incident-report-unsanctioned-agent-behaviour-during-cyber-testing (accessed 2026-11-01). Referenced in: Chapter 19: A Cautionary Case: The OpenAI–Hugging Face Incident.
Source [86]: OpenAI (2026). OpenAI – Hugging Face Incident Technical Report. 26 August 2026. Available at: https://cdn.openai.com/pdf/67869394-cb91-4c12-888c-5cbd85c7814c/OpenAI-Hugging-Face%20Incident-Technical-Report.pdf (accessed 2026-11-01). Referenced in: Chapter 19: A Cautionary Case: The OpenAI–Hugging Face Incident, Chapter 22: Future Trends, Chapter 23: Nordvik Region and Hansa Mutual: Conclusions for Two Organisations, Chapter 24: Reflections on Each Part, Chapter 25: Conclusions: A Consolidated Analysis.

AI-enabled threats, defence and incidents in 2026

Source [87]: Blum, Mainor, Nambiar et al. (2026). ARTEX and Friends: Enter Agentic Offensive Frameworks. Google Threat Intelligence Group and Mandiant, Google Cloud Blog, 30 October 2026. Available at: https://cloud.google.com/blog/topics/threat-intelligence/artex-and-friends-enter-agentic-offensive-frameworks (accessed 2026-11-01). Referenced in: Chapter 24: Reflections on Each Part.
Source [88]: Grunewald, R., Mazumdar, S. & Vanderlee, K. (2026). Vulnerability Discovery and Exploitation Trends in the AI Era. Google Threat Intelligence Group, Google Cloud Blog, 30 September 2026. Available at: https://cloud.google.com/blog/topics/threat-intelligence/vulnerability-discovery-and-exploitation-trends-in-the-ai-era (accessed 2026-11-01). Referenced in: Chapter 24: Reflections on Each Part.
Source [89]: Wijk, H., Cotra, A. & Greenblatt, R. (2026). Brief independent investigation of agents' behavior, reasoning and collaboration in the OpenAI / Hugging Face hacking incident. METR, 26 August 2026; Greenblatt of Redwood Research contracted with METR. Available at: https://metr.org/blog/2026-08-26-openai-hugging-face-incident-investigation/; PDF https://metr.org/hugging-face-incident-report-aug-2026.pdf (accessed 2026-11-01). Referenced in: Chapter 19: A Cautionary Case: The OpenAI–Hugging Face Incident.
Source [90]: National Cyber Security Centre (2026). NCSC statement in response to recent incidents resulting from frontier AI evaluations. 4 August 2026. Available at: https://www.ncsc.gov.uk/news/ncsc-statement-in-response-to-recent-incidents-resulting-from-frontier-ai-evaluations (accessed 2026-11-01). Referenced in: Chapter 19: A Cautionary Case: The OpenAI–Hugging Face Incident.
Source [91]: Hugging Face (2026). Security incident disclosure — July 2026. Hugging Face blog, 16 July 2026. Available at: https://huggingface.co/blog/security-incident-july-2026 (accessed 2026-11-01). Referenced in: Chapter 19: A Cautionary Case: The OpenAI–Hugging Face Incident.
Source [92]: Larcher, H., Carreira, A. et al. (2026). Anatomy of a Frontier Lab Agent Intrusion: A Technical Timeline of the July 2026 Incident. Hugging Face blog, 27 July 2026. Available at: https://huggingface.co/blog/agent-intrusion-technical-timeline (accessed 2026-11-01). Referenced in: Chapter 19: A Cautionary Case: The OpenAI–Hugging Face Incident, Chapter 24: Reflections on Each Part.
Source [93]: Five Eyes cyber security agencies: ASD's ACSC, Canadian Centre for Cyber Security, NCSC-NZ, NCSC-UK, NSA and CISA (2026). The AI shift in cyber risk: why leaders must act now. Joint statement, 22 June 2026. Available at: https://www.ncsc.gov.uk/sites/default/files/2026-06/Five-Eyes-cyber-security-agencies-statement-ai-shift.pdf (accessed 2026-11-01). Referenced in: Chapter 24: Reflections on Each Part.
Source [94]: Carlini, N., Cheng, N., Lucas, K. et al. (2026). Assessing Claude Mythos Preview's cybersecurity capabilities. Anthropic Frontier Red Team, 7 April 2026. Available at: https://www.anthropic.com/research/mythos-preview (accessed 2026-11-01). Referenced in: Chapter 24: Reflections on Each Part.
Source [95]: Five Eyes cyber security agencies: ASD's ACSC, CISA, NSA, Canadian Centre for Cyber Security, NCSC-NZ and NCSC-UK (2026). Careful adoption of agentic AI services. Joint guidance, 1 May 2026. Available at: https://www.ncsc.govt.nz/assets/guidance/Documents/Careful-adoption-of-agentic-AI-services_FINAL.pdf (accessed 2026-11-01). Referenced in: Chapter 13: Decision Forums and Governance Bodies.
Source [96]: National Cyber Security Centre (2026). Preparing for a 'vulnerability patch wave'. NCSC blog, 1 May 2026. Available at: https://www.ncsc.gov.uk/blogs/prepare-for-vulnerability-patch-wave (accessed 2026-11-01). Referenced in: Chapter 24: Reflections on Each Part.

The OpenAI–Hugging Face incident: further sources

Source [97]: OpenAI (2026). OpenAI and Hugging Face partner to address security incident during model evaluation. 21 July 2026. Available at: https://openai.com/index/hugging-face-model-evaluation-security-incident/ (accessed 2026-11-01). Referenced in: Chapter 19: A Cautionary Case: The OpenAI–Hugging Face Incident.
Source [98]: JFrog (2026). JFrog Security Advisories: CVE-2026-66384, CVE-2026-65616, CVE-2026-65617 and related Artifactory advisories. July–August 2026. Available at: https://docs.jfrog.com/releases/docs/jfrog-security-advisories (accessed 2026-11-01). Referenced in: Chapter 19: A Cautionary Case: The OpenAI–Hugging Face Incident.
Source [99]: Landman, Y. (2026). Fast Remediation Is the New Trust Model: JFrog and OpenAI Collaboration on Zero-Day Security Findings. JFrog, 27 July 2026, updated 5 August 2026. Available at: https://jfrog.com/blog/jfrog-and-openai-collaboration-on-zero-day-security-findings/ (accessed 2026-11-01). Referenced in: Chapter 19: A Cautionary Case: The OpenAI–Hugging Face Incident.
Source [100]: Linux kernel CNA (2026). CVE-2026-53362: ipv6: account for fraggap on the paged allocation path. CVE Program, 4 July 2026. Available at: https://www.cve.org/CVERecord?id=CVE-2026-53362 (accessed 2026-11-01). Referenced in: Chapter 19: A Cautionary Case: The OpenAI–Hugging Face Incident.
Source [101]: Satter, R., Seetharaman, D. & Cai, K. (2026). Exclusive: Its AI agent spent days hacking a company, but sources say OpenAI did not notice for a week. Reuters, 24 July 2026 (syndicated by The Star). Available at: https://www.thestar.com.my/tech/tech-news/2026/07/25/exclusive-its-ai-agent-spent-days-hacking-a-company-but-sources-say-openai-did-not-notice-for-a-week (accessed 2026-11-01). Referenced in: Chapter 19: A Cautionary Case: The OpenAI–Hugging Face Incident.
Source [102]: Bertuzzi, L. (2026). EU's AI rules face first enforcement test over lab incidents. Euronews, 14 October 2026. Available at: https://www.euronews.com/2026/10/14/eus-ai-rules-face-first-enforcement-test-over-lab-incidents (accessed 2026-11-01). Referenced in: Chapter 19: A Cautionary Case: The OpenAI–Hugging Face Incident, Chapter 22: Future Trends, Chapter 25: Conclusions: A Consolidated Analysis.
Source [103]: Bastians, H. & Herbosch, M. (2026). The EU AI Act Mandates AI Incident Reporting—If We're Lucky. Lawfare, 19 October 2026. Available at: https://www.lawfaremedia.org/article/the-eu-ai-act-mandates-ai-incident-reporting-if-we-re-lucky (accessed 2026-11-01). Referenced in: Chapter 19: A Cautionary Case: The OpenAI–Hugging Face Incident.

Information security management standards

Source [104]: International Organization for Standardization and International Electrotechnical Commission (2022). ISO/IEC 27001:2022 Information security, cybersecurity and privacy protection — Information security management systems — Requirements. Geneva: ISO, October 2022. Available at: https://www.iso.org/standard/27001 (accessed 2026-11-01). Referenced in: Chapter 13: Decision Forums and Governance Bodies.

DevSecMLOps loop and its risks

Source [105]: Agent Skills (2026). Agent Skills Overview and Specification. agentskills.io. Available at: https://agentskills.io/ (accessed 2026-11-01). Referenced in: Chapter 14: Competence, Roles and the AI Architect.
Source [106]: Lamanna, C. (2025). Microsoft Agent 365: The control plane for AI agents. Microsoft 365 Blog, 18 November 2025. Available at: https://www.microsoft.com/en-us/microsoft-365/blog/2025/11/18/microsoft-agent-365-the-control-plane-for-ai-agents/ (accessed 2026-11-01). Referenced in: Chapter 2: From Chat to Agents: How Large Language Models Are Used.
Source [107]: Beurer-Kellner, L., Kudrinskii, A., Milanta, M., Bonde Nielsen, K., Sarkar, H. & Tal, L. (2026). Snyk Finds Prompt Injection in 36%, 1467 Malicious Payloads in a ToxicSkills Study of Agent Skills Supply Chain Compromise. Snyk blog, 5 February 2026. Available at: https://snyk.io/blog/toxicskills-malicious-ai-agent-skills-clawhub (accessed 2026-11-01). Referenced in: Chapter 2: From Chat to Agents: How Large Language Models Are Used.
Source [108]: GitGuardian (2026). The State of Secrets Sprawl 2026. GitGuardian, March 2026. Available at: https://www.gitguardian.com/state-of-secrets-sprawl-report-2026 (accessed 2026-11-01). Referenced in: Chapter 2: From Chat to Agents: How Large Language Models Are Used.
Source [109]: Khan, A. (2026). Clinejection — Compromising Cline's Production Releases just by Prompting an Issue Triager. 9 February 2026, updated 18 February 2026. Available at: https://adnanthekhan.com/posts/clinejection/ (accessed 2026-11-01). Referenced in: Chapter 2: From Chat to Agents: How Large Language Models Are Used.
Source [110]: Milanta, M. & Beurer-Kellner, L. (2025). GitHub MCP Exploited: Accessing private repositories via MCP. Invariant Labs, 26 May 2025. Available at: https://invariantlabs.ai/blog/mcp-github-vulnerability (accessed 2026-11-01). Referenced in: Chapter 2: From Chat to Agents: How Large Language Models Are Used.

Cloud versus on-premises AI

Source [111]: European Commission (2026). Proposal for a Cloud and AI Development Act (CADA). COM(2026) 502, 3 June 2026. Available at: https://digital-strategy.ec.europa.eu/en/library/proposal-cloud-and-ai-development-act-cada (accessed 2026-11-01). Referenced in: Chapter 6: Geopolitics and Model Origin, Chapter 22: Future Trends, Chapter 24: Reflections on Each Part.
Source [112]: Pan, G., Chodnekar, V., Roy, A. & Wang, H. (2025). A Cost-Benefit Analysis of On-Premise Large Language Model Deployment: Breaking Even with Commercial LLM Services. arXiv:2509.18101v3 (preprint), 11 November 2025. Available at: https://arxiv.org/abs/2509.18101 (accessed 2026-11-01). Referenced in: Chapter 16A: Governing AI Economics: Cost, Value and Portfolio Decisions, Chapter 24: Reflections on Each Part.
Source [113]: OpenAI (2026). Deprecations. OpenAI API documentation, latest entry 14 October 2026. Available at: https://developers.openai.com/api/docs/deprecations (accessed 2026-11-01). Referenced in: Chapter 16A: Governing AI Economics: Cost, Value and Portfolio Decisions, Chapter 22: Future Trends, Chapter 25: Conclusions: A Consolidated Analysis.
Source [114]: Microsoft (2026). Foundry Models lifecycle and support policy. Microsoft Learn, 24 July 2026. Available at: https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirements (accessed 2026-11-01). Referenced in: Chapter 16A: Governing AI Economics: Cost, Value and Portfolio Decisions, Chapter 24: Reflections on Each Part, Chapter 25: Conclusions: A Consolidated Analysis.
Source [115]: Amazon Web Services. Generative AI Security Scoping Matrix. aws.amazon.com, undated. Available at: https://aws.amazon.com/ai/generative-ai/security/scoping-matrix/ (accessed 2026-11-01). Referenced in: Chapter 2: From Chat to Agents: How Large Language Models Are Used.
Source [116]: European Commission (2026). Frequently Asked Questions – Data Act, version 1.4. 22 January 2026. Available at: https://ec.europa.eu/newsroom/dae/redirection/document/108144 (accessed 2026-11-01). Referenced in: Chapter 25: Conclusions: A Consolidated Analysis.
Source [117]: European Commission (2025). Guidelines on the scope of the obligations for general-purpose AI models established by Regulation (EU) 2024/1689 (AI Act). Annex to C(2025) 5045 final, 18 July 2025. Available at: https://digital-strategy.ec.europa.eu/en/library/guidelines-scope-obligations-providers-general-purpose-ai-models-under-ai-act (accessed 2026-11-01). Referenced in: Chapter 2: From Chat to Agents: How Large Language Models Are Used.
Source [118]: Amazon (2026). AWS Launches AWS European Sovereign Cloud and Announces Expansion Across Europe. Amazon press release, 15 January 2026. Available at: https://press.aboutamazon.com/aws/2026/1/aws-launches-aws-european-sovereign-cloud-and-announces-expansion-across-europe (accessed 2026-11-01). Referenced in: Chapter 6: Geopolitics and Model Origin.

Decision forums and AI governance

Source [119]: European Central Bank (2025). ECB Banking Supervision: supervisory priorities for 2026–28. ECB Banking Supervision, November 2025. Available at: https://www.bankingsupervision.europa.eu/framework/priorities/html/ssm.supervisory_priorities202511.en.html (accessed 2026-11-01). Referenced in: Chapter 13: Decision Forums and Governance Bodies.
Source [120]: European Central Bank (2025). AI's impact on banking: use cases for credit scoring and fraud detection. Supervision Newsletter, ECB Banking Supervision, 20 November 2025. Available at: https://www.bankingsupervision.europa.eu/press/supervisory-newsletters/newsletter/2025/html/ssm.nl251120_1.en.html (accessed 2026-11-01). Referenced in: Chapter 13: Decision Forums and Governance Bodies.
Source [121]: Machado, P. (2026). Technology is neutral, governance is not: AI adoption in the banking sector. Speech at the KPMG RiskTech Conference, Frankfurt, ECB Banking Supervision, 24 February 2026. Available at: https://www.bankingsupervision.europa.eu/press/speeches/date/2026/html/ssm.sp260224~6c5b64a77a.en.html (accessed 2026-11-01). Referenced in: Chapter 13: Decision Forums and Governance Bodies, Chapter 25: Conclusions: A Consolidated Analysis.
Source [122]: European Central Bank (2026). ECB guide to internal models (version 4.1). ECB Banking Supervision, 26 June 2026. Available at: https://www.bankingsupervision.europa.eu/ecb/pub/pdf/ssm.supervisory_guide_egim_202606.en.pdf (accessed 2026-11-01). Referenced in: Chapter 13: Decision Forums and Governance Bodies.
Source [123]: European Central Bank (2026). Addressing AI-enabled cybersecurity threats (letter to the chief executive officers of significant institutions, SSM-2026-0301). ECB Banking Supervision, 7 July 2026. Available at: https://www.bankingsupervision.europa.eu/press/letterstobanks/shared/pdf/2026/ssm.2026_letter_on_AI_enabled_cybersecurity_threats.en.pdf (accessed 2026-11-01). Referenced in: Chapter 13: Decision Forums and Governance Bodies, Chapter 22: Future Trends, Chapter 24: Reflections on Each Part.
Source [124]: European Banking Authority (2025). AI Act: implications for the EU banking and payments sector. EBA, 21 November 2025. Available at: https://www.eba.europa.eu/sites/default/files/2025-11/d8b999ce-a1d9-4964-9606-971bbc2aaf89/AI%20Act%20implications%20for%20the%20EU%20banking%20sector.pdf (accessed 2026-11-01). Referenced in: Chapter 13: Decision Forums and Governance Bodies.
Source [125]: European Union Agency for Cybersecurity (2025). Technical implementation guidance on Commission Implementing Regulation (EU) 2024/2690 (version 1.0). ENISA, June 2025. Available at: https://www.enisa.europa.eu/sites/default/files/2025-06/ENISA_Technical_implementation_guidance_on_cybersecurity_risk_management_measures_version_1.0.pdf (accessed 2026-11-01). Referenced in: Chapter 13: Decision Forums and Governance Bodies.
Source [126]: Organisation for Economic Co-operation and Development (2025). Governing with Artificial Intelligence: The State of Play and Way Forward in Core Government Functions. OECD Publishing, Paris, 2025. Available at: https://doi.org/10.1787/795de142-en (accessed 2026-11-01). Referenced in: Chapter 13: Decision Forums and Governance Bodies, Chapter 16A: Governing AI Economics: Cost, Value and Portfolio Decisions.
Source [127]: Agency for Digital Government and Swedish Authority for Privacy Protection (2025). Inför en AI-policy (Introducing an AI policy), part of the national guidelines for the use of generative AI in public administration. Digg, published January 2025, updated 3 June 2026. Available at: https://www.digg.se/ai-for-offentlig-forvaltning/riktlinjer-for-generativ-ai/infor-en-ai-policy (accessed 2026-11-01). Referenced in: Chapter 2: From Chat to Agents: How Large Language Models Are Used, Chapter 13: Decision Forums and Governance Bodies.
Source [128]: Ministry of the Interior and Kingdom Relations, Netherlands (2026). Algoritmekader: governance (version 2.5). Government of the Netherlands, March 2026. Available at: https://minbzk.github.io/Algoritmekader/onderwerpen/governance/ (accessed 2026-11-01). Referenced in: Chapter 13: Decision Forums and Governance Bodies.
Source [129]: Government Digital Service (2025). Algorithmic Transparency Recording Standard Hub. GOV.UK, updated 8 May 2025. Available at: https://www.gov.uk/government/collections/algorithmic-transparency-recording-standard-hub (accessed 2026-11-01). Referenced in: Chapter 13: Decision Forums and Governance Bodies.
Source [130]: Federal Republic of Germany (2001). Betriebsverfassungsgesetz (BetrVG) (Works Constitution Act), as last amended on 19 July 2024. Federal Ministry of Justice, Gesetze im Internet. Available at: https://www.gesetze-im-internet.de/betrvg/ (accessed 2026-11-01). Referenced in: Chapter 5: Prerequisites and Readiness for Enterprise AI, Chapter 9: AI Governance as Code, Chapter 13: Decision Forums and Governance Bodies, Chapter 15: Change Management and Responsible Adoption, Chapter 25: Conclusions: A Consolidated Analysis.
Source [131]: Deloitte (2025). Governance of AI: A critical imperative for today's boards (second edition). Deloitte Global Boardroom Program, 2025. Available at: https://www.deloitte.com/global/en/issues/trust/progress-on-ai-in-the-boardroom-but-room-to-accelerate.html (accessed 2026-11-01). Referenced in: Chapter 13: Decision Forums and Governance Bodies, Chapter 24: Reflections on Each Part.
Source [132]: EY Center for Board Matters (2025). Cyber and AI oversight disclosures: what companies shared in 2025. EY, 14 October 2025. Available at: https://www.ey.com/en_us/board-matters/cyber-disclosure-trends (accessed 2026-11-01). Referenced in: Chapter 13: Decision Forums and Governance Bodies.
Source [133]: European Commission (2026). AI literacy: questions and answers. Shaping Europe's digital future, updated 20 October 2026. Available at: https://digital-strategy.ec.europa.eu/en/faqs/ai-literacy-questions-answers (accessed 2026-11-01). Referenced in: Chapter 13: Decision Forums and Governance Bodies, Chapter 14: Competence, Roles and the AI Architect, Chapter 25: Conclusions: A Consolidated Analysis.

Geopolitics and model origin

Source [134]: United States Congress (2018). 18 U.S.C. § 2713, Required preservation and disclosure of communications and records (CLOUD Act, enacted 23 March 2018 as Division V of the Consolidated Appropriations Act 2018). Legal Information Institute, Cornell Law School. Available at: https://www.law.cornell.edu/uscode/text/18/2713 (accessed 2026-11-01). Referenced in: Chapter 6: Geopolitics and Model Origin.
Source [135]: Brennan Center for Justice (2026). Section 702 Surveillance Authority Will Not Lapse if Reauthorized by March 2027. Brennan Center, 22 September 2026. Available at: https://www.brennancenter.org/media/16217/download/bcj-702-march-2027-deadline-one-pager-9.22.26.pdf (accessed 2026-11-01). Referenced in: Chapter 6: Geopolitics and Model Origin.
Source [136]: House Permanent Select Committee on Intelligence (2026). Chairman Crawford Statement on FISA 702 Reauthorization. US House of Representatives, 24 April 2026. Available at: https://intelligence.house.gov/2026/04/24/chairman-crawford-statement-on-fisa-702-reauthorization/ (accessed 2026-11-01). Referenced in: Chapter 6: Geopolitics and Model Origin.
Source [137]: Abu-Ltaif, S. & Bullwinkel, J. (2026). One year on: Progress on our European digital commitments. Microsoft On the Issues, 29 April 2026. Available at: https://blogs.microsoft.com/on-the-issues/2026/04/29/one-year-on-progress-on-our-european-digital-commitments/ (accessed 2026-11-01). Referenced in: Chapter 6: Geopolitics and Model Origin.
Source [138]: Privacy and Civil Liberties Oversight Board (2026). Congressional Budget Justification Fiscal Year 2027. PCLOB, 13 February 2026. Available at: https://documents.pclob.gov/prod/Documents/FinancialReport/1084/FY27%20PCLOB%20CBJ%20-%20Final%20-%202.13.26.pdf (accessed 2026-11-01). Referenced in: Chapter 6: Geopolitics and Model Origin.
Source [139]: Court of Justice of the European Union (2025). Appeal brought on 31 October 2025 against the judgment of the General Court in Case T-553/23, Case C-703/25 P. Notice in OJ C/2025/6610, 22 December 2025. Available at: https://curia.europa.eu/juris/liste.jsf?num=C-703/25 (accessed 2026-11-01). Referenced in: Chapter 6: Geopolitics and Model Origin, Chapter 22: Future Trends, Chapter 25: Conclusions: A Consolidated Analysis.
Source [140]: Court of Justice of the European Union (2025). Press release No 106/25: Judgment of the General Court in Case T-553/23, Latombe v Commission. 3 September 2025. Available at: https://curia.europa.eu/jcms/upload/docs/application/pdf/2025-09/cp250106en.pdf (accessed 2026-11-01). Referenced in: Chapter 6: Geopolitics and Model Origin, Chapter 25: Conclusions: A Consolidated Analysis.
Source [141]: Sénat (2025). Commission d'enquête sur la commande publique: compte rendu de l'audition de MM. Anton Carniaux, directeur des affaires publiques et juridiques, et Pierre Lagarde, directeur technique du secteur public, de Microsoft France. Sénat, 10 June 2025. Available at: https://senat.fr/compte-rendu-commissions/20250609/ce_commande_publique.html (accessed 2026-11-01). Referenced in: Chapter 6: Geopolitics and Model Origin.
Source [142]: Bureau of Industry and Security (2026). Revision to License Review Policy for Advanced Computing Commodities (Final rule, 91 FR 1684). Federal Register, 15 January 2026. Available at: https://www.federalregister.gov/documents/2026/01/15/2026-00789/revision-to-license-review-policy-for-advanced-computing-commodities (accessed 2026-11-01). Referenced in: Chapter 6: Geopolitics and Model Origin, Chapter 22: Future Trends.
Source [143]: Bureau of Industry and Security (2025). Department of Commerce Announces Rescission of Biden-Era Artificial Intelligence Diffusion Rule, Strengthens Chip-Related Export Controls. US Department of Commerce, 13 May 2025. Available at: https://www.bis.gov/press-release/department-commerce-announces-rescission-biden-era-artificial-intelligence-diffusion-rule-strengthens (accessed 2026-11-01). Referenced in: Chapter 6: Geopolitics and Model Origin, Chapter 22: Future Trends.
Source [144]: The Register (2026). Microsoft throws spox under the bus after Parliament testimony on ICC email kerfuffle. The Register, 18 February 2026. Available at: https://www.theregister.com/off-prem/2026/02/18/microsoft-throws-spox-under-the-bus-in-icc-email-flap/4890338 (accessed 2026-11-01). Referenced in: Chapter 6: Geopolitics and Model Origin.
Source [145]: Tridgell, J. (2025). Justice Recoded? Why It Matters that the International Criminal Court Embraced Open-Source Software and Ditched Microsoft. EJIL:Talk!, 20 November 2025. Available at: https://www.ejiltalk.org/justice-recoded-why-it-matters-that-the-international-criminal-court-embraced-open-source-software-and-ditched-microsoft/ (accessed 2026-11-01). Referenced in: Chapter 6: Geopolitics and Model Origin.
Source [146]: Bruckner, T. (2026). One Token Is Enough: Fingerprinting and Verifying Large Language Models from Single-Token Output Distributions. arXiv:2607.10252 (preprint), 11 July 2026. Available at: https://arxiv.org/abs/2607.10252 (accessed 2026-11-01). Referenced in: Chapter 6: Geopolitics and Model Origin.
Source [147]: National Institute of Standards and Technology, Center for AI Standards and Innovation (2026). CAISI Evaluation of DeepSeek V4 Pro. NIST, 1 May 2026, updated 2 May 2026. Available at: https://www.nist.gov/news-events/news/2026/05/caisi-evaluation-deepseek-v4-pro (accessed 2026-11-01). Referenced in: Chapter 6: Geopolitics and Model Origin.
Source [148]: Pan, J. & Xu, X. (2026). Political censorship in large language models originating from China. PNAS Nexus, 17 February 2026. Available at: https://doi.org/10.1093/pnasnexus/pgag013 (accessed 2026-11-01). Referenced in: Chapter 6: Geopolitics and Model Origin.
Source [149]: National Institute of Standards and Technology, Center for AI Standards and Innovation (2025). CAISI Evaluation of Kimi K2 Thinking. NIST, 12 December 2025. Available at: https://www.nist.gov/news-events/news/2025/12/caisi-evaluation-kimi-k2-thinking (accessed 2026-11-01). Referenced in: Chapter 6: Geopolitics and Model Origin.
Source [150]: Souly, A., Rando, J., Chapman, E. et al. (2025). Poisoning Attacks on LLMs Require a Near-constant Number of Poison Samples. arXiv:2510.07192 (preprint), 8 October 2025. Available at: https://arxiv.org/abs/2510.07192 (accessed 2026-11-01). Referenced in: Chapter 6: Geopolitics and Model Origin.
Source [151]: National Institute of Standards and Technology, Center for AI Standards and Innovation (2025). CAISI Evaluation of DeepSeek AI Models Finds Shortcomings and Risks. NIST, 30 September 2025, updated 20 November 2025. Available at: https://www.nist.gov/news-events/news/2025/09/caisi-evaluation-deepseek-ai-models-finds-shortcomings-and-risks (accessed 2026-11-01). Referenced in: Chapter 6: Geopolitics and Model Origin.
Source [152]: Kirchner, M. (2025). Berlin data protection authority wants DeepSeek to be blocked in app stores. heise online, 27 June 2025. Available at: https://heise.de/-10462419 (accessed 2026-11-01). Referenced in: Chapter 6: Geopolitics and Model Origin.
Source [153]: Garante per la protezione dei dati personali (2025). Intelligenza artificiale: il Garante privacy blocca DeepSeek (press release; order doc. web 10098477). Garante, 30 January 2025. Available at: https://www.garanteprivacy.it/home/docweb/-/docweb-display/docweb/10097450 (accessed 2026-11-01). Referenced in: Chapter 6: Geopolitics and Model Origin, Chapter 22: Future Trends.
Source [154]: Hubinger, E. et al. (2024). Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training. arXiv:2401.05566, 10 January 2024 (v3, 17 January 2024). Available at: https://arxiv.org/abs/2401.05566 (accessed 2026-11-01). Referenced in: Chapter 6: Geopolitics and Model Origin.
Source [155]: Cyberspace Administration of China and six other bodies (2023). 生成式人工智能服务管理暂行办法 (Interim Measures for the Administration of Generative AI Services), Order No. 15. CAC, 13 July 2023. Available at: https://www.cac.gov.cn/2023-07/13/c_1690898327029107.htm (accessed 2026-11-01). Referenced in: Chapter 6: Geopolitics and Model Origin.
Source [156]: Dezan Shira & Associates (2021). A Close Reading of China's Data Security Law, in Effect Sept. 1, 2021. China Briefing. Available at: https://www.china-briefing.com/news/a-close-reading-of-chinas-data-security-law-in-effect-sept-1-2021/ (accessed 2026-11-01). Referenced in: Chapter 6: Geopolitics and Model Origin.
Source [157]: National People's Congress Standing Committee (2017). National Intelligence Law of the People's Republic of China (adopted 27 June 2017; unofficial English translation). Available at: https://cs.brown.edu/courses/cs180/sources/2017_PRC_NationalIntelligenceLaw.pdf (accessed 2026-11-01). Referenced in: Chapter 6: Geopolitics and Model Origin.
Source [158]: Office of Management and Budget (2025). M-26-04, Increasing Public Trust in Artificial Intelligence Through Unbiased AI Principles. The White House, 11 December 2025. Available at: https://www.whitehouse.gov/wp-content/uploads/2025/12/M-26-04-Increasing-Public-Trust-in-Artificial-Intelligence-Through-Unbiased-AI-Principles-1.pdf (accessed 2026-11-01). Referenced in: Chapter 6: Geopolitics and Model Origin.
Source [159]: The White House (2025). Winning the Race: America's AI Action Plan. The White House, July 2025. Available at: https://www.whitehouse.gov/wp-content/uploads/2025/07/Americas-AI-Action-Plan.pdf (accessed 2026-11-01). Referenced in: Chapter 6: Geopolitics and Model Origin.
Source [160]: Cohere and Aleph Alpha (2026). Cohere and Aleph Alpha sign agreement to become the first transatlantic sovereign AI solution. PR Newswire, 16 September 2026. Available at: https://www.prnewswire.com/news-releases/cohere-and-aleph-alpha-sign-agreement-to-become-the-first-transatlantic-sovereign-ai-solution-302880758.html (accessed 2026-11-01). Referenced in: Chapter 6: Geopolitics and Model Origin, Chapter 22: Future Trends.
Source [161]: TechCrunch (2026). Mistral raises €3B as sovereign AI becomes big business. TechCrunch, 8 September 2026. Available at: https://techcrunch.com/2026/09/08/mistral-raises-e3b-as-sovereign-ai-becomes-big-business/ (accessed 2026-11-01). Referenced in: Chapter 6: Geopolitics and Model Origin, Chapter 22: Future Trends.
Source [162]: EuroHPC Joint Undertaking (2026). Call for tenders for the selection of Artificial Intelligence Gigafactory Consortia and the establishment of AI Gigafactories (EUROHPC-2026-CEI-AIGF-01). EuroHPC JU, opened 30 July 2026. Available at: https://www.eurohpc-ju.europa.eu/call-tenders-selection-artificial-intelligence-gigafactory-consortia-and-establishment-ai_en (accessed 2026-11-01). Referenced in: Chapter 6: Geopolitics and Model Origin.
Source [163]: EPFL AI Center (2026). Apertus 1.5: Building the next generation of open AI infrastructure. EPFL, 24 July 2026. Available at: https://ai.epfl.ch/?p=18627 (accessed 2026-11-01). Referenced in: Chapter 6: Geopolitics and Model Origin.
Source [164]: EPFL AI Center (2025). Apertus: a fully open, transparent, multilingual language model. EPFL, 2 September 2025. Available at: https://ai.epfl.ch/apertus-a-fully-open-transparent-multilingual-language-model/ (accessed 2026-11-01). Referenced in: Chapter 6: Geopolitics and Model Origin, Chapter 22: Future Trends.
Source [165]: Security Management (2026). Kabinet blokkeert Amerikaanse overname van DigiD-leverancier Solvinity. Securitymanagement.nl, 27 May 2026. Available at: https://www.securitymanagement.nl/kabinet-blokkeert-amerikaanse-overname-van-digid-leverancier-solvinity/ (accessed 2026-11-01). Referenced in: Chapter 6: Geopolitics and Model Origin.
Source [166]: University of Edinburgh School of Informatics (2025). Europe takes another step toward AI sovereignty with the launch of EuroLLM-22B. University of Edinburgh, 16 December 2025. Available at: https://informatics.ed.ac.uk/news/latest-news/europe-takes-another-step-toward-ai-sovereignty-with-the-launch-of-eurollm-22b (accessed 2026-11-01). Referenced in: Chapter 6: Geopolitics and Model Origin.
Source [167]: OpenEuroLLM consortium. Deliverables. OpenEuroLLM, undated web page. Available at: https://www.openeurollm.eu/deliverables (accessed 2026-11-01). Referenced in: Chapter 6: Geopolitics and Model Origin.
Source [168]: Algemene Inlichtingen- en Veiligheidsdienst (2026). AIVD Jaarverslag 2025. Presented to the Dutch Parliament on 23 April 2026. Available at: https://www.eerstekamer.nl/nonav/overig/20260423/openbaar_jaarverslag_aivd_2025/document (accessed 2026-11-01). Referenced in: Chapter 6: Geopolitics and Model Origin.
Source [169]: ANSSI and CERT-FR (2026). L'IA générative face aux attaques informatiques: synthèse de la menace en 2025 (CERTFR-2026-CTI-001). ANSSI, 4 February 2026. Available at: https://cert.cyber.gouv.fr/uploads/CERTFR-2026-CTI-001.pdf (accessed 2026-11-01). Referenced in: Chapter 6: Geopolitics and Model Origin.

Usage modes of large language models

Source [170]: OpenAI (2026). What is the ChatGPT Enterprise and Business data policy? OpenAI Help Center, living page read 1 November 2026. Available at: https://help.openai.com/en/articles/8983130-what-is-the-chatgpt-enterprise-and-team-data-policy (accessed 2026-11-01). Referenced in: Chapter 2: From Chat to Agents: How Large Language Models Are Used.
Source [171]: Anthropic (2025). Updates to Consumer Terms and Privacy Policy. Anthropic, 28 August 2025. Available at: https://www.anthropic.com/news/updates-to-our-consumer-terms (accessed 2026-11-01). Referenced in: Chapter 2: From Chat to Agents: How Large Language Models Are Used.
Source [172]: Microsoft (2026). Enterprise data protection in Microsoft Copilot and Microsoft Copilot Chat. Microsoft Learn, updated 31 October 2026. Available at: https://learn.microsoft.com/en-us/copilot/microsoft-365/enterprise-data-protection (accessed 2026-11-01). Referenced in: Chapter 2: From Chat to Agents: How Large Language Models Are Used.
Source [173]: Microsoft (2026). Data, Privacy, and Security for Microsoft Copilot. Microsoft Learn, updated 31 October 2026. Available at: https://learn.microsoft.com/en-us/copilot/microsoft-365/microsoft-365-copilot-privacy (accessed 2026-11-01). Referenced in: Chapter 2: From Chat to Agents: How Large Language Models Are Used.
Source [174]: Chauhan, S. (2025). Vibe working: Introducing Agent Mode and Office Agent in Microsoft 365 Copilot. Microsoft 365 Blog, 29 September 2025. Available at: https://www.microsoft.com/en-us/microsoft-365/blog/2025/09/29/vibe-working-introducing-agent-mode-and-office-agent-in-microsoft-365-copilot/ (accessed 2026-11-01). Referenced in: Chapter 2: From Chat to Agents: How Large Language Models Are Used.
Source [175]: Anthropic. Claude Cowork. Claude product page, living document. Available at: https://claude.com/blog/cowork-research-preview (accessed 2026-11-01). Referenced in: Chapter 2: From Chat to Agents: How Large Language Models Are Used.
Source [176]: Reddy, P. & Gujral, A. S. (2025). EchoLeak: The First Real-World Zero-Click Prompt Injection Exploit in a Production LLM System. arXiv:2509.10540, AAAI Fall Symposium Series 2025. Available at: https://arxiv.org/abs/2509.10540 (accessed 2026-11-01). Referenced in: Chapter 2: From Chat to Agents: How Large Language Models Are Used.
Source [177]: Levi, S. (2025). ForcedLeak: AI agent risks exposed in Salesforce Agentforce. Noma Security blog, 25 September 2025. Available at: https://noma.security/blog/forcedleak-agent-risks-exposed-in-salesforce-agentforce/ (accessed 2026-11-01). Referenced in: Chapter 2: From Chat to Agents: How Large Language Models Are Used.
Source [178]: Anthropic. Configure permissions and Configure the sandboxed Bash tool. Claude Code documentation, living page. Available at: https://code.claude.com/docs/en/permissions; https://code.claude.com/docs/en/sandboxing (accessed 2026-11-01). Referenced in: Chapter 2: From Chat to Agents: How Large Language Models Are Used.
Source [179]: GitHub. Risks and mitigations for GitHub Copilot cloud agent. GitHub Docs, living page. Available at: https://docs.github.com/en/copilot/concepts/agents/cloud-agent/risks-and-mitigations (accessed 2026-11-01). Referenced in: Chapter 2: From Chat to Agents: How Large Language Models Are Used.
Source [180]: Microsoft (2026). Event triggers overview – Microsoft Copilot Studio. Microsoft Learn, updated 10 September 2026. Available at: https://learn.microsoft.com/en-us/microsoft-copilot-studio/authoring-triggers-about (accessed 2026-11-01). Referenced in: Chapter 2: From Chat to Agents: How Large Language Models Are Used, Chapter 22: Future Trends.
Source [181]: European Data Protection Supervisor (2025). Generative AI and the EUDPR. Orientations for ensuring data protection compliance when using generative AI systems, version 2. EDPS, 28 October 2025. Available at: https://www.edps.europa.eu/system/files/2025-10/25-10_28_revised_genai_orientations_en.pdf (accessed 2026-11-01). Referenced in: Chapter 2: From Chat to Agents: How Large Language Models Are Used.
Source [182]: Chapekis, A. & Lieb, A. (2025). Google users are less likely to click on links when an AI summary appears in the results. Pew Research Center, 22 July 2025. Available at: https://www.pewresearch.org/short-reads/2025/07/22/google-users-are-less-likely-to-click-on-links-when-an-ai-summary-appears-in-the-results/ (accessed 2026-11-01). Referenced in: Chapter 2: From Chat to Agents: How Large Language Models Are Used.
Source [183]: OpenCode (2026). OpenCode documentation: Intro, with the linked Providers and Agents pages. Last updated 1 November 2026; repository licensed under MIT. Available at: https://opencode.ai/docs (accessed 2026-11-01). Referenced in: Chapter 2: From Chat to Agents: How Large Language Models Are Used.

Conclusions for the two example organisations

Source [184]: European Parliament and Council of the European Union (2009). Directive 2009/138/EC on the taking-up and pursuit of the business of Insurance and Reinsurance (Solvency II) (recast). Official Journal of the European Union, L 335, 17 December 2009. Available at: https://eur-lex.europa.eu/eli/dir/2009/138/oj (accessed 2026-11-01). Referenced in: Chapter 7: Procurement, Vendor Management and Open Models, Chapter 11: Evidence, Assurance and Audit, Chapter 12: Organisational Design for AI, Chapter 14: Competence, Roles and the AI Architect, Chapter 16: Management as Code for AI Programmes, Chapter 23: Nordvik Region and Hansa Mutual: Conclusions for Two Organisations.
Source [185]: European Insurance and Occupational Pensions Authority (2025). Opinion on Artificial Intelligence governance and risk management. EIOPA, 6 August 2025. Available at: https://www.eiopa.europa.eu/publications/opinion-artificial-intelligence-governance-and-risk-management_en (accessed 2026-11-01). Referenced in: Chapter 10: Trustworthy AI: Transparency, Fairness and Human Oversight, Chapter 12: Organisational Design for AI, Chapter 22: Future Trends, Chapter 23: Nordvik Region and Hansa Mutual: Conclusions for Two Organisations, Chapter 25: Conclusions: A Consolidated Analysis.
Source [186]: European Free Trade Association. Q&A about the EEA Agreement. EFTA. Available at: https://www.efta.int/eea/eea-agreement/eea-basic-features (accessed 2026-11-01). Referenced in: Chapter 23: Nordvik Region and Hansa Mutual: Conclusions for Two Organisations.
Source [187]: European Free Trade Association. EEA-Lex factsheet 32024R1689: the Artificial Intelligence Act. EFTA. Available at: https://www.efta.int/eea-lex/32024r1689 (accessed 2026-11-01). Referenced in: Chapter 1: Introduction to AI Governance, Chapter 3: The EU AI Act and the European Regulatory Landscape, Chapter 4: Fundamental Principles, Chapter 12: Organisational Design for AI, Chapter 14: Competence, Roles and the AI Architect, Chapter 15: Change Management and Responsible Adoption, Chapter 22: Future Trends, Chapter 23: Nordvik Region and Hansa Mutual: Conclusions for Two Organisations, Chapter 25: Conclusions: A Consolidated Analysis.
Source [188]: Department for Science, Innovation and Technology and Office for Artificial Intelligence (2023). A pro-innovation approach to AI regulation (CP 815). UK Government, 29 March 2023. Available at: https://www.gov.uk/government/publications/ai-regulation-a-pro-innovation-approach/white-paper (accessed 2026-11-01). Referenced in: Chapter 1: Introduction to AI Governance, Chapter 3: The EU AI Act and the European Regulatory Landscape, Chapter 12: Organisational Design for AI, Chapter 22: Future Trends, Chapter 23: Nordvik Region and Hansa Mutual: Conclusions for Two Organisations.
Source [189]: European Commission (2025). Commission Implementing Decision (EU) 2025/2574 amending Implementing Decision (EU) 2021/1772 on the adequate protection of personal data by the United Kingdom. Official Journal of the European Union, 23 December 2025. Available at: https://eur-lex.europa.eu/eli/dec_impl/2025/2574/oj (accessed 2026-11-01). Referenced in: Chapter 5: Prerequisites and Readiness for Enterprise AI, Chapter 23: Nordvik Region and Hansa Mutual: Conclusions for Two Organisations, Chapter 25: Conclusions: A Consolidated Analysis.
Source [190]: Swiss Federal Assembly (2020). Federal Act of 25 September 2020 on Data Protection (Data Protection Act, FADP), SR 235.1. Fedlex, in force 1 September 2023. Available at: https://www.fedlex.admin.ch/eli/cc/2022/491/en (accessed 2026-11-01). Referenced in: Chapter 1: Introduction to AI Governance, Chapter 3: The EU AI Act and the European Regulatory Landscape, Chapter 8: AI Risk Management and Classification as Code, Chapter 23: Nordvik Region and Hansa Mutual: Conclusions for Two Organisations.
Source [191]: European Commission (2000). Commission Decision 2000/518/EC on the adequate protection of personal data provided in Switzerland. Official Journal of the European Communities, L 215, 25 August 2000. Available at: https://eur-lex.europa.eu/eli/dec/2000/518/oj (accessed 2026-11-01). Referenced in: Chapter 5: Prerequisites and Readiness for Enterprise AI, Chapter 23: Nordvik Region and Hansa Mutual: Conclusions for Two Organisations, Chapter 25: Conclusions: A Consolidated Analysis.
Source [192]: Swiss Financial Market Supervisory Authority (2024). FINMA Guidance 08/2024: Governance and risk management when using artificial intelligence. FINMA, 18 December 2024. Available at: https://www.finma.ch/news/2024/12/20241218-mm-finma-am-08-24/ (accessed 2026-11-01). Referenced in: Chapter 4: Fundamental Principles, Chapter 12: Organisational Design for AI, Chapter 22: Future Trends, Chapter 23: Nordvik Region and Hansa Mutual: Conclusions for Two Organisations, Chapter 25: Conclusions: A Consolidated Analysis.

Example organisations and multinational settings

Source [193]: United Kingdom (2018). Data Protection Act 2018 (c. 12), with the UK General Data Protection Regulation. Available at: https://www.legislation.gov.uk/ukpga/2018/12/contents (accessed 2026-11-01). Referenced in: Chapter 12: Organisational Design for AI.

Future trends: 2025 and 2026 developments

Source [194]: CEN-CENELEC (2026). First ever European standard on AI published in the OJEU. CEN-CENELEC news, 15 October 2026. Available at: https://www.cencenelec.eu/news-events/news/2026/en-in-the-spotlight/2026-10-15-en-18286-2026-published-in-ojeu/ (accessed 2026-11-01). Referenced in: Chapter 22: Future Trends.
Source [195]: European Commission (2026). Commission publishes guidelines on transparency obligations for providers and deployers of certain AI systems. Shaping Europe's digital future, 20 July 2026. Available at: https://digital-strategy.ec.europa.eu/en/news/commission-publishes-guidelines-transparency-obligations-providers-and-deployers-certain-ai-systems (accessed 2026-11-01). Referenced in: Chapter 22: Future Trends.
Source [196]: European Commission (2026). Draft Commission guidelines on the classification of high-risk AI systems. Shaping Europe's digital future, 19 May 2026, last updated 23 July 2026. Available at: https://digital-strategy.ec.europa.eu/en/library/draft-commission-guidelines-classification-high-risk-ai-systems (accessed 2026-11-01). Referenced in: Chapter 22: Future Trends, Chapter 24: Reflections on Each Part.
Source [197]: Bengio, Y. et al. (2026). International AI Safety Report 2026. International AI Safety Report, 3 February 2026. Available at: https://internationalaisafetyreport.org/publication/international-ai-safety-report-2026 (accessed 2026-11-01). Referenced in: Chapter 22: Future Trends, Chapter 24: Reflections on Each Part.
Source [198]: Stanford Institute for Human-Centered Artificial Intelligence (2026). The 2026 AI Index Report. Stanford University. Available at: https://hai.stanford.edu/ai-index/2026-ai-index-report (accessed 2026-11-01). Referenced in: Chapter 22: Future Trends.
Source [199]: Swiss Federal Council (2025). AI regulation: Federal Council to ratify Council of Europe Convention. Press release, 12 February 2025. Available at: https://www.admin.ch/en/nsb?id=104110 (accessed 2026-11-01). Referenced in: Chapter 22: Future Trends, Chapter 24: Reflections on Each Part.
Source [200]: International Energy Agency (2025). Energy and AI. IEA, April 2025. Available at: https://www.iea.org/reports/energy-and-ai (accessed 2026-11-01). Referenced in: Chapter 22: Future Trends.
Source [201]: Gartner (2025). Gartner Predicts Over 40% of Agentic AI Projects Will Be Canceled by End of 2027. Press release, 25 June 2025. Available at: https://www.gartner.com/en/newsroom/press-releases/2025-06-25-gartner-predicts-over-40-percent-of-agentic-ai-projects-will-be-canceled-by-end-of-2027 (accessed 2026-11-01). Referenced in: Chapter 22: Future Trends, Chapter 24: Reflections on Each Part.

Documented European case studies

Source [202]: District Court of The Hague (2020). Judgment of 5 February 2020, NJCM et al. v. the State of the Netherlands (SyRI), case C/09/550982 / HA ZA 18-388, ECLI:NL:RBDHA:2020:1878 (English translation of ECLI:NL:RBDHA:2020:865). Rechtspraak.nl. Available at: https://uitspraken.rechtspraak.nl/details?id=ECLI:NL:RBDHA:2020:1878 (accessed 2026-11-01). Referenced in: Chapter 18: Case Studies, Chapter 23: Nordvik Region and Hansa Mutual: Conclusions for Two Organisations, Chapter 24: Reflections on Each Part.
Source [203]: UK Government Digital Service (2025). Microsoft 365 Copilot Experiment: Cross-Government Findings Report. GOV.UK, 2 June 2025. Available at: https://www.gov.uk/government/publications/microsoft-365-copilot-experiment-cross-government-findings-report (accessed 2026-11-01). Referenced in: Chapter 16A: Governing AI Economics: Cost, Value and Portfolio Decisions, Chapter 18: Case Studies, Chapter 23: Nordvik Region and Hansa Mutual: Conclusions for Two Organisations.
Source [204]: Department for Business and Trade (2025). Evaluation of the M365 Copilot pilot in the Department for Business and Trade (corporate report "Microsoft 365 Copilot pilot: DBT evaluation report"). GOV.UK, 28 August 2025. Available at: https://www.gov.uk/government/publications/microsoft-365-copilot-pilot-dbt-evaluation-report (accessed 2026-11-01). Referenced in: Chapter 16A: Governing AI Economics: Cost, Value and Portfolio Decisions, Chapter 18: Case Studies.
Source [205]: Garante per la protezione dei dati personali (2024). ChatGPT, the Italian Data Protection Authority closes the preliminary investigation. OpenAI will have to carry out a six-month information campaign and pay a fine of EUR 15 million (press release of 20 December 2024 on Decision No. 755 of 2 November 2024, with the Garante's note on its removal following judgment No. 4153/2026 of the Court of Rome, published 18 March 2026). Garante, docweb 10085432. Available at: https://www.garanteprivacy.it/home/docweb/-/docweb-display/docweb/10085432 (accessed 2026-11-01). Referenced in: Chapter 18: Case Studies.
Source [206]: Christakis, T. (2026). Generative AI and GDPR Enforcement in Europe: A Lot of Noise, One Fine, Zero Survivors. Cross-Border Data Forum, 19 March 2026. Available at: https://www.crossborderdataforum.org/generative-ai-and-gdpr-enforcement-in-europe-a-lot-of-noise-one-fine-zero-survivors/ (accessed 2026-11-01). Referenced in: Chapter 18: Case Studies.

AI economics, cost and value

Source [207]: FinOps Foundation (2026). FinOps for AI Overview. FinOps Foundation working group paper, last updated 28 October 2026. Available at: https://www.finops.org/wg/finops-for-ai-overview/ (accessed 2026-11-01). Referenced in: Chapter 16A: Governing AI Economics: Cost, Value and Portfolio Decisions.
Source [208]: FinOps Foundation. FinOps Framework Overview. FinOps Foundation, living page. Available at: https://www.finops.org/framework/ (accessed 2026-11-01). Referenced in: Chapter 16A: Governing AI Economics: Cost, Value and Portfolio Decisions.
Source [209]: Storment, J. R. (2026). Token Economics: The Atomic Unit of AI Value. FinOps Foundation Insights, 10 May 2026. Available at: https://finops.org/insights/token-economics-the-atomic-unit-of-ai-value/ (accessed 2026-11-01). Referenced in: Chapter 16A: Governing AI Economics: Cost, Value and Portfolio Decisions.
Source [210]: Linux Foundation (2026). Linux Foundation Announces the Intent to Launch the Tokenomics Foundation to Establish Open Standards for AI Cost Management. Press release, San Francisco, 3 June 2026. Available at: https://www.linuxfoundation.org/press/linux-foundation-announces-the-intent-to-launch-the-tokenomics-foundation-to-establish-open-standards-for-ai-cost-management (accessed 2026-11-01). Referenced in: Chapter 16A: Governing AI Economics: Cost, Value and Portfolio Decisions.
Source [211]: FinOps Foundation (2026). State of FinOps 2026 Report. FinOps Foundation, sixth annual survey, 1,192 respondents. Available at: https://data.finops.org/ (accessed 2026-11-01). Referenced in: Chapter 16A: Governing AI Economics: Cost, Value and Portfolio Decisions.
Source [212]: Challapally, A., Pease, C., Raskar, R. & Chari, P. (2025). The GenAI Divide: State of AI in Business 2025. MIT NANDA, Massachusetts Institute of Technology, July 2025 (preliminary findings). Available at: https://mlq.ai/media/quarterly_decks/v0.1_State_of_AI_in_Business_2025_Report.pdf (accessed 2026-11-01). Referenced in: Chapter 16A: Governing AI Economics: Cost, Value and Portfolio Decisions.
Source [213]: HM Treasury (2026). The Green Book (2026): UK Government Guidance on Appraisal. London: HM Treasury, published 5 February 2026, updated 28 October 2026. ISBN 978-1-918417-12-8. Available at: https://www.gov.uk/government/publications/the-green-book--2/the-green-book-2026 (accessed 2026-11-01). Referenced in: Chapter 16A: Governing AI Economics: Cost, Value and Portfolio Decisions.
Source [214]: European Commission (2021). Economic Appraisal Vademecum 2021–2027: General Principles and Sector Applications. Directorate-General for Regional and Urban Policy, with the support of JASPERS. Luxembourg: Publications Office of the European Union, September 2021. ISBN 978-92-76-40462-0, doi:10.2776/182302. Available at: https://jaspers.eib.org/LibraryNP/EC%20Reports/Economic%20Appraisal%20Vademecum%202021-2027%20-%20General%20Principles%20and%20Sector%20Applications.pdf (accessed 2026-11-01). Referenced in: Chapter 16A: Governing AI Economics: Cost, Value and Portfolio Decisions.

How to cite

Inline citations give the source number, the author or organisation, and the year, for example (69, Skelton & Pais, 2019), and link to the entry here. Where an author is named in the sentence, the citation carries the year. Each chapter closes with a ## Sources section that lists the works it cites, using the same numbering as this bibliography. New sources are appended to the appropriate category with the next free number; existing numbers are never reused.