A Systematic Approach to AI Security Governance: Leveraging ISO 42001


Authors : Kavya Suredranath

Volume/Issue : Volume 10 - 2025, Issue 4 - April


Google Scholar : https://tinyurl.com/4uhup54w

Scribd : https://tinyurl.com/5fum5p2s

DOI : https://doi.org/10.38124/ijisrt/25apr394

Google Scholar

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Note : Google Scholar may take 15 to 20 days to display the article.


Abstract : Security governance necessitates a comprehensive transformation as artificial intelligence (AI) continues to revolutionize industrial operations. ISO/IEC 42001 provides a standardized approach to address AI security risks, as well as compliance andethical concerns.This paper examinesthe components ofISO42001, explaining how the standard establishes a robustframework that enables bestpracticesand secure AI governance across various domains.

References :

  1. Schneier, B. (2023). Artificial Intelligence and Security Risks. Harvard University Press.
  2. Brundage, M., Avin, S., Wang, J., Belfield, H., Krueger, G., Hadfield, G., C Dafoe, (2020). Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims. arXiv preprint arXiv:2004.07213.
  3. ISO/IEC 42001. (2023). Artificial Intelligence Management System Standard. International Organization for Standardization.
  4. Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., C Schafer, B. (2018). AI4People—An Ethical Framework for a Good AI Society: Opportunities, Risks, Principles, and Recommendations. Minds and Machines, 28, 689–707.
  5. Goodman, B., C Flaxman, S. (2017). European Union regulations on algorithmic decision-making and a “right to explanation”. AI Magazine, 38(3), 50–57.
  6. Garfinkel, S. (2015). Privacy and Security in the Era of Big Data. ACM Queue, 13(6), 30–49.
  7. Doshi-Velez, F., C Kim, B. (2017). Towards A Rigorous Science of Interpretable Machine Learning. arXiv preprint arXiv:1702.08608.
  8. Binns, R. (2018). Fairness in Machine Learning: Lessons from Political Philosophy. Proceedings of the 2018 Conference on Fairness, Accountability, and Transparency.
  9. Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., C Floridi, L. (2016). The Ethics of Algorithms: Mapping the Debate. Big Data C Society.
  10. Biggio, B., C Roli, F. (2018). Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning. Pattern Recognition, 84, 317–331.
  11. McKinsey AI Report. (2021). The State of AI in 2021: A Half-Decade Review.
  12. Papernot, N., McDaniel, P., Goodfellow, I., Jha, S., Celik, Z. B., C Swami, A. (2016). Practical Black-Box Attacks against Machine Learning. Proceedings of the ACM Asia Conference on Computer and Communications Security.
  13. NIST Privacy Framework. (2020). A Tool for Improving Privacy through Enterprise Risk Management.
  14. ISO 27701. (2019). Privacy Information Management System. International Organization for Standardization.
  15. Shokri, R., Stronati, M., Song, C., C Shmatikov, V. (2017). Membership Inference Attacks Against Machine Learning Models. IEEE Symposium on Security and Privacy.
  16. Adadi, A., C Berrada, M. (2018). Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI). IEEE Access, 6, 52138–52160.
  17. Weidinger, L., Uesato, J., Michel, T., C Gabriel, I. (2022). Ethical and Social Risks of AI: Challenges and Solutions. arXiv preprint arXiv:2112.04359.
  18. ISO 27001. (2022). Information Security Management System. International Organization for Standardization.
  19. OECD AI Principles. (2019). Recommendation of the Council on Artificial Intelligence. Organization for Economic Co-operation and Development.
  20. Tschantz, M. C., Datta, A., C Wing, J. M. (2014). Formalizing and Enforcing Purpose Restrictions in Privacy Policies. IEEE Symposium on Security and Privacy.
  21. ENISA AI Threat Landscape. (2023). Artificial Intelligence Threat Landscape Report. European Union Agency for Cybersecurity.
  22. AI Risk Management Framework, NIST. (2023). Guidelines for Artificial Intelligence Risk Assessment and Management.
  23. ISO 22301. (2019). Business Continuity Management Systems. International Organization for Standardization.
  24. ISO 27040. (2015). Storage Security Standards. International Organization for Standardization.
  25. AI Security Guidelines. (2022). AI Security Recommendations for Enterprises.
  26. Huang, L., Joseph, A., C Nelson, B. (2020). Adversarial Machine Learning: Vulnerabilities and Countermeasures.
  27. IEEE AI Ethics Training. (2022). AI Ethics and Responsible AI Development Training Module.
  28. ISACA AI Governance Framework. (2021). Framework for AI Risk and Governance.
  29. CIS AI Security Controls. (2022). Cybersecurity Controls for Artificial Intelligence Systems.
  30. EU AI Act. (2023). Regulation of Artificial Intelligence in the European Union.
  31. ISO/IEC 38505-1. (2017). Governance of IT Governance of Data Part 1: Application of ISO/IEC 38500 to the Governance of Data.

Security governance necessitates a comprehensive transformation as artificial intelligence (AI) continues to revolutionize industrial operations. ISO/IEC 42001 provides a standardized approach to address AI security risks, as well as compliance andethical concerns.This paper examinesthe components ofISO42001, explaining how the standard establishes a robustframework that enables bestpracticesand secure AI governance across various domains.

Never miss an update from Papermashup

Get notified about the latest tutorials and downloads.

Subscribe by Email

Get alerts directly into your inbox after each post and stay updated.
Subscribe
OR

Subscribe by RSS

Add our RSS to your feedreader to get regular updates from us.
Subscribe