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General Framework for AI Security and Privacy

In: Understanding AI in Cybersecurity and Secure AI

Author

Listed:
  • Dilli Prasad Sharma

    (University of Toronto)

  • Arash Habibi Lashkari

    (York University)

  • Mahdi Daghmehchi Firoozjaei

    (MacEwan University)

  • Samaneh Mahdavifar

    (McGill University)

  • Pulei Xiong

    (National Research Council of Canada)

Abstract

This chapter presents a general framework for AI security and privacy. It begins with examining security threats and defenses across key phases of the AI system development life cycle, including data collection, preprocessing, model training, inference, and system integration. The chapter discusses NIST’s AI Risk Management Framework (AI RMF), focusing on risk identification, system trustworthiness, and the lifecycle dimensions of AI systems. It also outlines core frameworks, including Google’s Secure AI Framework, and relevant security and privacy standards, such as ISO/IEC AI security standards, EU AI Act, and OECD AI principles.

Suggested Citation

  • Dilli Prasad Sharma & Arash Habibi Lashkari & Mahdi Daghmehchi Firoozjaei & Samaneh Mahdavifar & Pulei Xiong, 2025. "General Framework for AI Security and Privacy," Progress in IS, in: Understanding AI in Cybersecurity and Secure AI, chapter 0, pages 197-219, Springer.
    • Handle: RePEc:spr:prochp:978-3-031-91524-6_10
    DOI: 10.1007/978-3-031-91524-6_10
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