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Course Outline

Foundations of AI and Security

  • What makes AI systems unique from a security perspective.
  • Overview of the AI lifecycle: data, training, inference, and deployment.
  • Basic taxonomy of AI risks: technical, ethical, legal, and organizational.

AI-Specific Threat Vectors

  • Adversarial examples and model manipulation.
  • Model inversion and data leakage risks.
  • Data poisoning during training phases.
  • Risks in generative AI (e.g., LLM misuse, prompt injection).

Security Risk Management Frameworks

  • NIST AI Risk Management Framework (NIST AI RMF).
  • ISO/IEC 42001 and other AI-specific standards.
  • Mapping AI risk to existing enterprise GRC frameworks.

AI Governance and Compliance Principles

  • AI accountability and auditability.
  • Transparency, explainability, and fairness as security-relevant properties.
  • Bias, discrimination, and downstream harms.

Enterprise Readiness and AI Security Policies

  • Defining roles and responsibilities in AI security programs.
  • Policy elements: development, procurement, use, and retirement.
  • Third-party risk and supplier AI tool usage.

Regulatory Landscape and Global Trends

  • Overview of the EU AI Act and international regulation.
  • U.S. Executive Order on Safe, Secure, and Trustworthy AI.
  • Emerging national frameworks and sector-specific guidance.

Optional Workshop: Risk Mapping and Self-Assessment

  • Mapping real-world AI use cases to NIST AI RMF functions.
  • Performing a basic AI risk self-assessment.
  • Identifying internal gaps in AI security readiness.

Summary and Next Steps

Requirements

  • A basic understanding of cybersecurity principles.
  • Experience with IT governance or risk management frameworks.
  • Familiarity with general AI concepts is beneficial but not mandatory.

Audience

  • IT security teams.
  • Risk managers.
  • Compliance professionals.
 14 Hours

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