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, frameworks, threat assessments and practitioner literature quickly.
Most entries were carried over from the bibliographies of the earlier volumes of The Transformed Architecture, with the access dates recorded there. Entries for MITRE ATT&CK, MITRE D3FEND, TIBER-EU, Commission Implementing Regulation (EU) 2024/2690 and Sigma were added for this book on 2026-11-02 and checked on 2026-11-10 (Sigma only partly, because its host blocks automated access; see docs/SOURCE_CHECK.md); the entries for NIST IR 8596 and NIST SP 800-61 Rev. 3 were checked against the NIST publication pages on 2026-11-02 with scripts/verify_sources.py before release.
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: Putting AI to Work in Cyber Defence, Chapter 3: NIST CSF 2.0 with an AI Focus: Functions, Focus Areas and European Obligations, Chapter 5: Organisational Context and Risk Strategy for AI in Cyber Defence, Chapter 6: Roles, Usage Modes and Autonomy Levels: People in Command of Security AI, Chapter 7: Policy, Mandate and Rules of Engagement, Chapter 8: Oversight and AI Supply Chain Risk, Chapter 9: Asset Management for an Estate That Includes AI, Chapter 10: AI-Assisted Threat Modelling and Risk Assessment, Chapter 13: Red Teaming AI Systems and Agents, Chapter 15: Awareness and Training: Resilience to AI-Generated Deception, Chapter 16: Data and Platform Security with and for AI, Chapter 18: The AI-Assisted Security Operations Centre, Chapter 19: Detection Engineering, Threat Intelligence and Deception as Code, Chapter 20: Incident Management and Analysis with AI, Chapter 21: Incident Reporting, Communication and Mitigation, Chapter 23: Recovering AI Systems and Security AI, Chapter 27: Nordvik Region and Hansa Mutual: Cyber Defence Conclusions.
- Source [2]: 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: Putting AI to Work in Cyber Defence, Chapter 3: NIST CSF 2.0 with an AI Focus: Functions, Focus Areas and European Obligations, Chapter 4: A Reference Architecture for AI-Enabled Cyber Defence, Chapter 5: Organisational Context and Risk Strategy for AI in Cyber Defence, Chapter 6: Roles, Usage Modes and Autonomy Levels: People in Command of Security AI, Chapter 7: Policy, Mandate and Rules of Engagement, Chapter 8: Oversight and AI Supply Chain Risk, Chapter 9: Asset Management for an Estate That Includes AI, Chapter 10: AI-Assisted Threat Modelling and Risk Assessment, Chapter 11: Vulnerability Discovery and Exposure Management at Machine Speed, Chapter 12: Adversary Emulation and Continuous Red Teaming with AI, Chapter 13: Red Teaming AI Systems and Agents, Chapter 14: Identity, Authentication and Access for People, Agents and Machines, Chapter 15: Awareness and Training: Resilience to AI-Generated Deception, Chapter 16: Data and Platform Security with and for AI, Chapter 17: Technology Infrastructure Resilience Against Machine-Speed Attacks, Chapter 18: The AI-Assisted Security Operations Centre, Chapter 19: Detection Engineering, Threat Intelligence and Deception as Code, Chapter 20: Incident Management and Analysis with AI, Chapter 21: Incident Reporting, Communication and Mitigation, Chapter 22: Recovery Plan Execution with AI: From Incident to Restored Operations, Chapter 23: Recovering AI Systems and Security AI, Chapter 24: Proactive Recovery: Recoverability, Rehearsal and Communication, Chapter 25: Security Chaos Engineering with AI: Verifying Security Capabilities Continuously, Chapter 26: Purple Teaming, Measurement and Continuous Improvement, Chapter 27: Nordvik Region and Hansa Mutual: Cyber Defence Conclusions, Chapter 28: Conclusions: AI as an Active Part of Cyber Defence.
- Source [3]: 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: Putting AI to Work in Cyber Defence, Chapter 3: NIST CSF 2.0 with an AI Focus: Functions, Focus Areas and European Obligations, Chapter 4: A Reference Architecture for AI-Enabled Cyber Defence, Chapter 5: Organisational Context and Risk Strategy for AI in Cyber Defence, Chapter 6: Roles, Usage Modes and Autonomy Levels: People in Command of Security AI, Chapter 7: Policy, Mandate and Rules of Engagement, Chapter 8: Oversight and AI Supply Chain Risk, Chapter 9: Asset Management for an Estate That Includes AI, Chapter 10: AI-Assisted Threat Modelling and Risk Assessment, Chapter 11: Vulnerability Discovery and Exposure Management at Machine Speed, Chapter 12: Adversary Emulation and Continuous Red Teaming with AI, Chapter 14: Identity, Authentication and Access for People, Agents and Machines, Chapter 15: Awareness and Training: Resilience to AI-Generated Deception, Chapter 16: Data and Platform Security with and for AI, Chapter 17: Technology Infrastructure Resilience Against Machine-Speed Attacks, Chapter 18: The AI-Assisted Security Operations Centre, Chapter 19: Detection Engineering, Threat Intelligence and Deception as Code, Chapter 20: Incident Management and Analysis with AI, Chapter 21: Incident Reporting, Communication and Mitigation, Chapter 22: Recovery Plan Execution with AI: From Incident to Restored Operations, Chapter 23: Recovering AI Systems and Security AI, Chapter 24: Proactive Recovery: Recoverability, Rehearsal and Communication, Chapter 25: Security Chaos Engineering with AI: Verifying Security Capabilities Continuously, Chapter 26: Purple Teaming, Measurement and Continuous Improvement, Chapter 27: Nordvik Region and Hansa Mutual: Cyber Defence Conclusions, Chapter 28: Conclusions: AI as an Active Part of Cyber Defence.
- Source [4]: European Commission. Commission Implementing Regulation (EU) 2024/2690 laying down rules for the application of Directive (EU) 2022/2555 as regards technical and methodological requirements of cybersecurity risk-management measures. Official Journal of the European Union, 18 October 2024. Available at: https://eur-lex.europa.eu/eli/reg_impl/2024/2690/oj (accessed 2026-11-02). Referenced in: Chapter 1: Introduction: Putting AI to Work in Cyber Defence, Chapter 3: NIST CSF 2.0 with an AI Focus: Functions, Focus Areas and European Obligations, Chapter 7: Policy, Mandate and Rules of Engagement, Chapter 8: Oversight and AI Supply Chain Risk, Chapter 9: Asset Management for an Estate That Includes AI, Chapter 14: Identity, Authentication and Access for People, Agents and Machines, Chapter 15: Awareness and Training: Resilience to AI-Generated Deception, Chapter 19: Detection Engineering, Threat Intelligence and Deception as Code, Chapter 24: Proactive Recovery: Recoverability, Rehearsal and Communication.
- Source [5]: 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 1: Introduction: Putting AI to Work in Cyber Defence, Chapter 3: NIST CSF 2.0 with an AI Focus: Functions, Focus Areas and European Obligations, Chapter 7: Policy, Mandate and Rules of Engagement, Chapter 8: Oversight and AI Supply Chain Risk, Chapter 11: Vulnerability Discovery and Exposure Management at Machine Speed, Chapter 16: Data and Platform Security with and for AI.
- Source [6]: 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 7: Policy, Mandate and Rules of Engagement, Chapter 8: Oversight and AI Supply Chain Risk.
- Source [7]: 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 5: Organisational Context and Risk Strategy for AI in Cyber Defence, Chapter 26: Purple Teaming, Measurement and Continuous Improvement.
- Source [8]: 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 3: NIST CSF 2.0 with an AI Focus: Functions, Focus Areas and European Obligations, Chapter 7: Policy, Mandate and Rules of Engagement, Chapter 8: Oversight and AI Supply Chain Risk, Chapter 9: Asset Management for an Estate That Includes AI, Chapter 14: Identity, Authentication and Access for People, Agents and Machines, Chapter 24: Proactive Recovery: Recoverability, Rehearsal and Communication.
- Source [9]: 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 and on the free movement of such 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 2026-11-06). Referenced in: Chapter 1: Introduction: Putting AI to Work in Cyber Defence, Chapter 4: A Reference Architecture for AI-Enabled Cyber Defence, Chapter 7: Policy, Mandate and Rules of Engagement, Chapter 9: Asset Management for an Estate That Includes AI, Chapter 15: Awareness and Training: Resilience to AI-Generated Deception, Chapter 19: Detection Engineering, Threat Intelligence and Deception as Code, Chapter 21: Incident Reporting, Communication and Mitigation.
Frameworks, standards and taxonomies
- Source [10]: MITRE Corporation. MITRE ATT&CK: Adversarial Tactics, Techniques, and Common Knowledge. Available at: https://attack.mitre.org/ (accessed 2026-11-02). Referenced in: Chapter 1: Introduction: Putting AI to Work in Cyber Defence, Chapter 2: The Threat Landscape When Attackers Use AI, Chapter 4: A Reference Architecture for AI-Enabled Cyber Defence, Chapter 7: Policy, Mandate and Rules of Engagement, Chapter 8: Oversight and AI Supply Chain Risk, Chapter 9: Asset Management for an Estate That Includes AI, Chapter 10: AI-Assisted Threat Modelling and Risk Assessment, Chapter 12: Adversary Emulation and Continuous Red Teaming with AI, Chapter 14: Identity, Authentication and Access for People, Agents and Machines, Chapter 15: Awareness and Training: Resilience to AI-Generated Deception, Chapter 17: Technology Infrastructure Resilience Against Machine-Speed Attacks, Chapter 18: The AI-Assisted Security Operations Centre, Chapter 19: Detection Engineering, Threat Intelligence and Deception as Code, Chapter 20: Incident Management and Analysis with AI, Chapter 25: Security Chaos Engineering with AI: Verifying Security Capabilities Continuously, Chapter 26: Purple Teaming, Measurement and Continuous Improvement.
- Source [11]: MITRE Corporation. MITRE ATLAS: Adversarial Threat Landscape for AI Systems. Available at: https://atlas.mitre.org/ (accessed 2026-10-31). Referenced in: Chapter 1: Introduction: Putting AI to Work in Cyber Defence, Chapter 2: The Threat Landscape When Attackers Use AI, Chapter 4: A Reference Architecture for AI-Enabled Cyber Defence, Chapter 7: Policy, Mandate and Rules of Engagement, Chapter 8: Oversight and AI Supply Chain Risk, Chapter 9: Asset Management for an Estate That Includes AI, Chapter 10: AI-Assisted Threat Modelling and Risk Assessment, Chapter 11: Vulnerability Discovery and Exposure Management at Machine Speed, Chapter 12: Adversary Emulation and Continuous Red Teaming with AI, Chapter 13: Red Teaming AI Systems and Agents, Chapter 16: Data and Platform Security with and for AI, Chapter 18: The AI-Assisted Security Operations Centre, Chapter 19: Detection Engineering, Threat Intelligence and Deception as Code, Chapter 20: Incident Management and Analysis with AI, Chapter 26: Purple Teaming, Measurement and Continuous Improvement.
- Source [12]: MITRE Corporation. MITRE D3FEND: A knowledge graph of cybersecurity countermeasures. Available at: https://d3fend.mitre.org/ (accessed 2026-11-02). Referenced in: Chapter 4: A Reference Architecture for AI-Enabled Cyber Defence, Chapter 10: AI-Assisted Threat Modelling and Risk Assessment, Chapter 19: Detection Engineering, Threat Intelligence and Deception as Code, Chapter 21: Incident Reporting, Communication and Mitigation.
- Source [13]: European Central Bank. TIBER-EU: Threat Intelligence-Based Ethical Red Teaming framework. ECB, framework updated February 2025. Available at: https://www.ecb.europa.eu/paym/cyber-resilience/tiber-eu/html/index.en.html (accessed 2026-11-02). Referenced in: Chapter 1: Introduction: Putting AI to Work in Cyber Defence, Chapter 7: Policy, Mandate and Rules of Engagement, Chapter 12: Adversary Emulation and Continuous Red Teaming with AI, Chapter 26: Purple Teaming, Measurement and Continuous Improvement, Chapter 27: Nordvik Region and Hansa Mutual: Cyber Defence Conclusions.
- Source [14]: 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 4: A Reference Architecture for AI-Enabled Cyber Defence, Chapter 5: Organisational Context and Risk Strategy for AI in Cyber Defence, Chapter 7: Policy, Mandate and Rules of Engagement, Chapter 8: Oversight and AI Supply Chain Risk, Chapter 10: AI-Assisted Threat Modelling and Risk Assessment, Chapter 11: Vulnerability Discovery and Exposure Management at Machine Speed, Chapter 13: Red Teaming AI Systems and Agents, Chapter 14: Identity, Authentication and Access for People, Agents and Machines, Chapter 15: Awareness and Training: Resilience to AI-Generated Deception, Chapter 16: Data and Platform Security with and for AI, Chapter 17: Technology Infrastructure Resilience Against Machine-Speed Attacks, Chapter 18: The AI-Assisted Security Operations Centre, Chapter 21: Incident Reporting, Communication and Mitigation, Chapter 23: Recovering AI Systems and Security AI, Chapter 24: Proactive Recovery: Recoverability, Rehearsal and Communication.
- Source [15]: 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 4: A Reference Architecture for AI-Enabled Cyber Defence, Chapter 6: Roles, Usage Modes and Autonomy Levels: People in Command of Security AI, Chapter 9: Asset Management for an Estate That Includes AI, Chapter 10: AI-Assisted Threat Modelling and Risk Assessment, Chapter 13: Red Teaming AI Systems and Agents, Chapter 14: Identity, Authentication and Access for People, Agents and Machines, Chapter 23: Recovering AI Systems and Security AI.
- Source [16]: 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 3: NIST CSF 2.0 with an AI Focus: Functions, Focus Areas and European Obligations, Chapter 5: Organisational Context and Risk Strategy for AI in Cyber Defence, Chapter 26: Purple Teaming, Measurement and Continuous Improvement.
- Source [17]: 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 13: Red Teaming AI Systems and Agents.
- Source [18]: Vassilev, A., Oprea, A., Fordyce, A., Anderson, H., Davies, X. & Hamin, M. (2025). Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations. NIST AI 100-2 E2025. National Institute of Standards and Technology, 24 March 2025. Available at: https://doi.org/10.6028/NIST.AI.100-2e2025 (accessed 2026-11-01). Referenced in: Chapter 4: A Reference Architecture for AI-Enabled Cyber Defence, Chapter 8: Oversight and AI Supply Chain Risk, Chapter 13: Red Teaming AI Systems and Agents, Chapter 16: Data and Platform Security with and for AI, Chapter 23: Recovering AI Systems and Security AI.
- Source [19]: NIST. The NIST Cybersecurity Framework (CSF) 2.0. NIST CSWP 29, 26 February 2024. Available at: https://doi.org/10.6028/NIST.CSWP.29 (accessed 2026-10-30). Referenced in: Chapter 1: Introduction: Putting AI to Work in Cyber Defence, Chapter 2: The Threat Landscape When Attackers Use AI, Chapter 3: NIST CSF 2.0 with an AI Focus: Functions, Focus Areas and European Obligations, Chapter 4: A Reference Architecture for AI-Enabled Cyber Defence, Chapter 5: Organisational Context and Risk Strategy for AI in Cyber Defence, Chapter 6: Roles, Usage Modes and Autonomy Levels: People in Command of Security AI, Chapter 7: Policy, Mandate and Rules of Engagement, Chapter 8: Oversight and AI Supply Chain Risk, Chapter 9: Asset Management for an Estate That Includes AI, Chapter 10: AI-Assisted Threat Modelling and Risk Assessment, Chapter 11: Vulnerability Discovery and Exposure Management at Machine Speed, Chapter 12: Adversary Emulation and Continuous Red Teaming with AI, Chapter 13: Red Teaming AI Systems and Agents, Chapter 14: Identity, Authentication and Access for People, Agents and Machines, Chapter 15: Awareness and Training: Resilience to AI-Generated Deception, Chapter 16: Data and Platform Security with and for AI, Chapter 17: Technology Infrastructure Resilience Against Machine-Speed Attacks, Chapter 18: The AI-Assisted Security Operations Centre, Chapter 19: Detection Engineering, Threat Intelligence and Deception as Code, Chapter 20: Incident Management and Analysis with AI, Chapter 21: Incident Reporting, Communication and Mitigation, Chapter 22: Recovery Plan Execution with AI: From Incident to Restored Operations, Chapter 23: Recovering AI Systems and Security AI, Chapter 24: Proactive Recovery: Recoverability, Rehearsal and Communication, Chapter 25: Security Chaos Engineering with AI: Verifying Security Capabilities Continuously, Chapter 26: Purple Teaming, Measurement and Continuous Improvement, Chapter 28: Conclusions: AI as an Active Part of Cyber Defence.
- Source [20]: National Institute of Standards and Technology. Cybersecurity Framework Profile for Artificial Intelligence (Cyber AI Profile). NIST IR 8596, initial preliminary draft, 16 December 2025. Available at: https://csrc.nist.gov/pubs/ir/8596/iprd (accessed 2026-11-02). Referenced in: Chapter 1: Introduction: Putting AI to Work in Cyber Defence, Chapter 2: The Threat Landscape When Attackers Use AI, Chapter 3: NIST CSF 2.0 with an AI Focus: Functions, Focus Areas and European Obligations, Chapter 4: A Reference Architecture for AI-Enabled Cyber Defence, Chapter 5: Organisational Context and Risk Strategy for AI in Cyber Defence, Chapter 6: Roles, Usage Modes and Autonomy Levels: People in Command of Security AI, Chapter 7: Policy, Mandate and Rules of Engagement, Chapter 8: Oversight and AI Supply Chain Risk, Chapter 9: Asset Management for an Estate That Includes AI, Chapter 10: AI-Assisted Threat Modelling and Risk Assessment, Chapter 11: Vulnerability Discovery and Exposure Management at Machine Speed, Chapter 12: Adversary Emulation and Continuous Red Teaming with AI, Chapter 13: Red Teaming AI Systems and Agents, Chapter 14: Identity, Authentication and Access for People, Agents and Machines, Chapter 15: Awareness and Training: Resilience to AI-Generated Deception, Chapter 16: Data and Platform Security with and for AI, Chapter 17: Technology Infrastructure Resilience Against Machine-Speed Attacks, Chapter 18: The AI-Assisted Security Operations Centre, Chapter 19: Detection Engineering, Threat Intelligence and Deception as Code, Chapter 20: Incident Management and Analysis with AI, Chapter 21: Incident Reporting, Communication and Mitigation, Chapter 22: Recovery Plan Execution with AI: From Incident to Restored Operations, Chapter 23: Recovering AI Systems and Security AI, Chapter 24: Proactive Recovery: Recoverability, Rehearsal and Communication, Chapter 25: Security Chaos Engineering with AI: Verifying Security Capabilities Continuously, Chapter 26: Purple Teaming, Measurement and Continuous Improvement, Chapter 28: Conclusions: AI as an Active Part of Cyber Defence.
- Source [21]: National Institute of Standards and Technology. Incident Response Recommendations and Considerations for Cybersecurity Risk Management: A CSF 2.0 Community Profile. NIST SP 800-61 Rev. 3, April 2025. Available at: https://csrc.nist.gov/pubs/sp/800/61/r3/final (accessed 2026-11-02). Referenced in: Chapter 20: Incident Management and Analysis with AI, Chapter 21: Incident Reporting, Communication and Mitigation, Chapter 22: Recovery Plan Execution with AI: From Incident to Restored Operations, Chapter 24: Proactive Recovery: Recoverability, Rehearsal and Communication.
- Source [22]: Bartock, M., Cichonski, J., Souppaya, M., Smith, M., Witte, G. & Scarfone, K. (2016). Guide for Cybersecurity Event Recovery. NIST SP 800-184. National Institute of Standards and Technology, December 2016. Available at: https://doi.org/10.6028/NIST.SP.800-184 (accessed 2026-11-02). Referenced in: Chapter 22: Recovery Plan Execution with AI: From Incident to Restored Operations, Chapter 24: Proactive Recovery: Recoverability, Rehearsal and Communication, Chapter 28: Conclusions: AI as an Active Part of Cyber Defence.
- Source [23]: Rose, S., Borchert, O., Mitchell, S. & Connelly, S. Zero Trust Architecture. NIST Special Publication 800-207, August 2020. Available at: https://doi.org/10.6028/NIST.SP.800-207 (accessed 2026-10-30). Referenced in: Chapter 4: A Reference Architecture for AI-Enabled Cyber Defence, Chapter 14: Identity, Authentication and Access for People, Agents and Machines, Chapter 17: Technology Infrastructure Resilience Against Machine-Speed Attacks.
- Source [24]: OASIS Cyber Threat Intelligence (CTI) TC. STIX Version 2.1 and TAXII Version 2.1. OASIS Standards, approved 10 June 2021. Available at: https://docs.oasis-open.org/cti/stix/v2.1/os/stix-v2.1-os.html and https://docs.oasis-open.org/cti/taxii/v2.1/os/taxii-v2.1-os.html (accessed 2026-10-30). Referenced in: Chapter 10: AI-Assisted Threat Modelling and Risk Assessment, Chapter 19: Detection Engineering, Threat Intelligence and Deception as Code.
- Source [25]: Threat Modeling Manifesto Working Group. Threat Modeling Manifesto. 2020. Available at: https://www.threatmodelingmanifesto.org/ (accessed 2026-10-30). Referenced in: Chapter 10: AI-Assisted Threat Modelling and Risk Assessment.
- Source [26]: Forum of Incident Response and Security Teams. Exploit Prediction Scoring System (EPSS). FIRST; academic basis Jacobs, J. et al. (2021), Digital Threats: Research and Practice 2(3), https://doi.org/10.1145/3436242. Available at: https://www.first.org/epss/ (accessed 2026-11-01). Referenced in: Chapter 5: Organisational Context and Risk Strategy for AI in Cyber Defence, Chapter 11: Vulnerability Discovery and Exposure Management at Machine Speed.
- Source [27]: Cybersecurity and Infrastructure Security Agency. Known Exploited Vulnerabilities Catalog. CISA. Available at: https://www.cisa.gov/known-exploited-vulnerabilities-catalog (accessed 2026-11-01). Referenced in: Chapter 5: Organisational Context and Risk Strategy for AI in Cyber Defence, Chapter 11: Vulnerability Discovery and Exposure Management at Machine Speed.
- Source [28]: European Union Agency for Cybersecurity (2025). European Vulnerability Database (EUVD). Launched 13 May 2025 under the NIS 2 Directive. Available at: https://euvd.enisa.europa.eu/ (accessed 2026-11-01). Referenced in: Chapter 5: Organisational Context and Risk Strategy for AI in Cyber Defence, Chapter 11: Vulnerability Discovery and Exposure Management at Machine Speed.
- Source [29]: Souppaya, M. & Scarfone, K. (2022). Guide to Enterprise Patch Management Planning: Preventive Maintenance for Technology. NIST SP 800-40 Rev. 4. Available at: https://doi.org/10.6028/NIST.SP.800-40r4 (accessed 2026-11-01). Referenced in: Chapter 11: Vulnerability Discovery and Exposure Management at Machine Speed, Chapter 16: Data and Platform Security with and for AI.
- Source [30]: 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 3: NIST CSF 2.0 with an AI Focus: Functions, Focus Areas and European Obligations.
- Source [31]: International Organization for Standardization and International Electrotechnical Commission (2022). ISO/IEC 27002:2022 Information security, cybersecurity and privacy protection — Information security controls. Geneva: ISO, February 2022. Available at: https://www.iso.org/standard/75652.html (accessed 2026-11-01). Referenced in: Chapter 3: NIST CSF 2.0 with an AI Focus: Functions, Focus Areas and European Obligations, Chapter 9: Asset Management for an Estate That Includes AI, Chapter 24: Proactive Recovery: Recoverability, Rehearsal and Communication.
- Source [32]: SigmaHQ. Sigma: Generic Signature Format for SIEM Systems. Open-source detection rule specification and rule repository. Available at: https://github.com/SigmaHQ/sigma (accessed 2026-11-02). Referenced in: Chapter 19: Detection Engineering, Threat Intelligence and Deception as Code, Chapter 26: Purple Teaming, Measurement and Continuous Improvement.
Threat assessments and agency guidance
- Source [33]: European Union Agency for Cybersecurity (2025). ENISA Threat Landscape 2025. October 2025. Available at: https://www.enisa.europa.eu/publications/enisa-threat-landscape-2025 (accessed 2026-11-01). Referenced in: Chapter 2: The Threat Landscape When Attackers Use AI, Chapter 14: Identity, Authentication and Access for People, Agents and Machines.
- Source [34]: European Union Agency for Cybersecurity (2026). ENISA Threat Landscape 2026. September 2026. Available at: https://www.enisa.europa.eu/sites/default/files/2026-09/ENISA%20Threat%20Landscape%202026_Final.pdf (accessed 2026-11-01). Referenced in: Chapter 1: Introduction: Putting AI to Work in Cyber Defence, Chapter 2: The Threat Landscape When Attackers Use AI, Chapter 7: Policy, Mandate and Rules of Engagement, Chapter 10: AI-Assisted Threat Modelling and Risk Assessment, Chapter 14: Identity, Authentication and Access for People, Agents and Machines, Chapter 19: Detection Engineering, Threat Intelligence and Deception as Code, Chapter 28: Conclusions: AI as an Active Part of Cyber Defence.
- Source [35]: Europol (2026). The evolving threat landscape. How encryption, proxies and AI are expanding cybercrime – Internet Organised Crime Threat Assessment (IOCTA) 2026. Luxembourg: Publications Office of the European Union. ISBN 978-92-9414-096-8. Available at: https://doi.org/10.2813/5737847 (accessed 2026-11-01). Referenced in: Chapter 2: The Threat Landscape When Attackers Use AI, Chapter 15: Awareness and Training: Resilience to AI-Generated Deception.
- Source [36]: 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 2: The Threat Landscape When Attackers Use AI, Chapter 7: Policy, Mandate and Rules of Engagement.
- Source [37]: National Cyber Security Centre (2025). Impact of AI on cyber threat from now to 2027. NCSC Assessment, 7 May 2025. Available at: https://www.ncsc.gov.uk/report/impact-ai-cyber-threat-now-2027 (accessed 2026-11-01). Referenced in: Chapter 1: Introduction: Putting AI to Work in Cyber Defence, Chapter 2: The Threat Landscape When Attackers Use AI, Chapter 5: Organisational Context and Risk Strategy for AI in Cyber Defence, Chapter 9: Asset Management for an Estate That Includes AI, Chapter 10: AI-Assisted Threat Modelling and Risk Assessment, Chapter 14: Identity, Authentication and Access for People, Agents and Machines.
- Source [38]: 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 2: The Threat Landscape When Attackers Use AI, Chapter 5: Organisational Context and Risk Strategy for AI in Cyber Defence, Chapter 11: Vulnerability Discovery and Exposure Management at Machine Speed.
- Source [39]: 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 1: Introduction: Putting AI to Work in Cyber Defence, Chapter 2: The Threat Landscape When Attackers Use AI, Chapter 5: Organisational Context and Risk Strategy for AI in Cyber Defence, Chapter 21: Incident Reporting, Communication and Mitigation, Chapter 22: Recovery Plan Execution with AI: From Incident to Restored Operations.
- Source [40]: 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 4: A Reference Architecture for AI-Enabled Cyber Defence, Chapter 6: Roles, Usage Modes and Autonomy Levels: People in Command of Security AI, Chapter 13: Red Teaming AI Systems and Agents, Chapter 14: Identity, Authentication and Access for People, Agents and Machines, Chapter 18: The AI-Assisted Security Operations Centre.
- Source [41]: 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 12: Adversary Emulation and Continuous Red Teaming with AI.
- Source [42]: 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 12: Adversary Emulation and Continuous Red Teaming with AI.
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AI-enabled threats and exploitation timelines
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- Source [47]: Google Threat Intelligence Group (2026). GTIG AI Threat Tracker: Adversaries Leverage AI for Vulnerability Exploitation, Augmented Operations, and Initial Access. Google Cloud Blog, 11 May 2026. Available at: https://cloud.google.com/blog/topics/threat-intelligence/ai-vulnerability-exploitation-initial-access (accessed 2026-11-01). Referenced in: Chapter 2: The Threat Landscape When Attackers Use AI.
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- Source [50]: 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 2: The Threat Landscape When Attackers Use AI, Chapter 4: A Reference Architecture for AI-Enabled Cyber Defence, Chapter 12: Adversary Emulation and Continuous Red Teaming with AI, Chapter 19: Detection Engineering, Threat Intelligence and Deception as Code.
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AI-assisted defence and vulnerability discovery
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- Source [61]: 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 1: Introduction: Putting AI to Work in Cyber Defence, Chapter 2: The Threat Landscape When Attackers Use AI, Chapter 11: Vulnerability Discovery and Exposure Management at Machine Speed, Chapter 28: Conclusions: AI as an Active Part of Cyber Defence.
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Agents, containment and AI incidents
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- Source [65]: 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 6: Roles, Usage Modes and Autonomy Levels: People in Command of Security AI, Chapter 12: Adversary Emulation and Continuous Red Teaming with AI, Chapter 20: Incident Management and Analysis with AI.
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Books and practitioner literature
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- Source [68]: Basiri, A., Behnam, N., de Rooij, R., Hochstein, L., Kosewski, L., Reynolds, J. & Rosenthal, C. (2016). Chaos Engineering. IEEE Software 33(3), 35–41. Available at: https://doi.org/10.1109/MS.2016.60 (accessed 2026-11-02). Referenced in: Chapter 25: Security Chaos Engineering with AI: Verifying Security Capabilities Continuously.
- Source [69]: Netflix. Chaos Monkey. Open-source resiliency tool that randomly terminates instances in production. Available at: https://netflix.github.io/chaosmonkey/ (accessed 2026-11-02). Referenced in: Chapter 25: Security Chaos Engineering with AI: Verifying Security Capabilities Continuously.
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The Transformed Architecture and the author
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- Source [72]: Nordqvist, G. (2026). AI Governance: Governing Artificial Intelligence in European Enterprises and Public Authorities. The Transformed Architecture, Book 2, published at aig.geon.se. Available at: https://aig.geon.se/ (accessed 2026-11-01). Conceived and directed by Nordqvist; text written by AI agents. Referenced in: Chapter 1: Introduction: Putting AI to Work in Cyber Defence, Chapter 2: The Threat Landscape When Attackers Use AI, Chapter 3: NIST CSF 2.0 with an AI Focus: Functions, Focus Areas and European Obligations, Chapter 5: Organisational Context and Risk Strategy for AI in Cyber Defence, Chapter 6: Roles, Usage Modes and Autonomy Levels: People in Command of Security AI, Chapter 7: Policy, Mandate and Rules of Engagement, Chapter 8: Oversight and AI Supply Chain Risk, Chapter 9: Asset Management for an Estate That Includes AI, Chapter 11: Vulnerability Discovery and Exposure Management at Machine Speed, Chapter 12: Adversary Emulation and Continuous Red Teaming with AI, Chapter 13: Red Teaming AI Systems and Agents, Chapter 14: Identity, Authentication and Access for People, Agents and Machines, Chapter 15: Awareness and Training: Resilience to AI-Generated Deception, Chapter 16: Data and Platform Security with and for AI, Chapter 17: Technology Infrastructure Resilience Against Machine-Speed Attacks, Chapter 18: The AI-Assisted Security Operations Centre, Chapter 19: Detection Engineering, Threat Intelligence and Deception as Code, Chapter 20: Incident Management and Analysis with AI, Chapter 21: Incident Reporting, Communication and Mitigation, Chapter 23: Recovering AI Systems and Security AI, Chapter 24: Proactive Recovery: Recoverability, Rehearsal and Communication, Chapter 25: Security Chaos Engineering with AI: Verifying Security Capabilities Continuously, Chapter 26: Purple Teaming, Measurement and Continuous Improvement, Chapter 27: Nordvik Region and Hansa Mutual: Cyber Defence Conclusions, Chapter 28: Conclusions: AI as an Active Part of Cyber Defence, About the Author.
- Source [73]: Nordqvist, G. (2026). AI Platform and Security: Building and Securing AI Platforms in European Enterprises and Public Authorities. The Transformed Architecture, Book 3. Available at: https://aips.geon.se (accessed 2026-11-01). Conceived and directed by Nordqvist; text written by AI agents. Referenced in: Chapter 1: Introduction: Putting AI to Work in Cyber Defence, Chapter 2: The Threat Landscape When Attackers Use AI, Chapter 3: NIST CSF 2.0 with an AI Focus: Functions, Focus Areas and European Obligations, Chapter 4: A Reference Architecture for AI-Enabled Cyber Defence, Chapter 5: Organisational Context and Risk Strategy for AI in Cyber Defence, Chapter 6: Roles, Usage Modes and Autonomy Levels: People in Command of Security AI, Chapter 7: Policy, Mandate and Rules of Engagement, Chapter 8: Oversight and AI Supply Chain Risk, Chapter 9: Asset Management for an Estate That Includes AI, Chapter 10: AI-Assisted Threat Modelling and Risk Assessment, Chapter 11: Vulnerability Discovery and Exposure Management at Machine Speed, Chapter 12: Adversary Emulation and Continuous Red Teaming with AI, Chapter 13: Red Teaming AI Systems and Agents, Chapter 14: Identity, Authentication and Access for People, Agents and Machines, Chapter 15: Awareness and Training: Resilience to AI-Generated Deception, Chapter 16: Data and Platform Security with and for AI, Chapter 17: Technology Infrastructure Resilience Against Machine-Speed Attacks, Chapter 18: The AI-Assisted Security Operations Centre, Chapter 19: Detection Engineering, Threat Intelligence and Deception as Code, Chapter 20: Incident Management and Analysis with AI, Chapter 21: Incident Reporting, Communication and Mitigation, Chapter 22: Recovery Plan Execution with AI: From Incident to Restored Operations, Chapter 23: Recovering AI Systems and Security AI, Chapter 24: Proactive Recovery: Recoverability, Rehearsal and Communication, Chapter 25: Security Chaos Engineering with AI: Verifying Security Capabilities Continuously, Chapter 26: Purple Teaming, Measurement and Continuous Improvement, Chapter 27: Nordvik Region and Hansa Mutual: Cyber Defence Conclusions, Chapter 28: Conclusions: AI as an Active Part of Cyber Defence, About the Author.
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European legislation and case law added after review
- Source [75]: 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 2026-11-14). Referenced in: Chapter 3: NIST CSF 2.0 with an AI Focus: Functions, Focus Areas and European Obligations, Chapter 7: Policy, Mandate and Rules of Engagement, Chapter 15: Awareness and Training: Resilience to AI-Generated Deception.
- Source [76]: Federal Republic of Germany. Betriebsverfassungsgesetz (BetrVG), § 87 Mitbestimmungsrechte (Works Constitution Act, Section 87, rights of co-determination). Gesetze im Internet. Available at: https://www.gesetze-im-internet.de/betrvg/__87.html (accessed 2026-11-14). Referenced in: Chapter 1: Introduction: Putting AI to Work in Cyber Defence, Chapter 7: Policy, Mandate and Rules of Engagement, Chapter 15: Awareness and Training: Resilience to AI-Generated Deception, Chapter 26: Purple Teaming, Measurement and Continuous Improvement.
- Source [77]: Bundesarbeitsgericht (2024). Beschluss vom 16. Juli 2024, 1 ABR 16/23 (Headset-System; technische Einrichtung zur Überwachung, § 87 Abs. 1 Nr. 6 BetrVG). Federal Labour Court, First Senate, ECLI:DE:BAG:2024:160724.B.1ABR16.23.0. Available at: https://www.bundesarbeitsgericht.de/entscheidung/1-abr-16-23/ (accessed 2026-11-14). Referenced in: Chapter 1: Introduction: Putting AI to Work in Cyber Defence, Chapter 7: Policy, Mandate and Rules of Engagement, Chapter 15: Awareness and Training: Resilience to AI-Generated Deception, Chapter 26: Purple Teaming, Measurement and Continuous Improvement.
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