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Course Outline

AI Security Governance Foundations

  • Core principles of AI governance.
  • Enterprise security frameworks for AI.
  • Stakeholder roles and responsibilities.

AI Risk Assessment Methodologies

  • Identifying and categorizing AI security risks.
  • Threat modeling for AI-enabled systems.
  • Impact assessment and prioritization.

Secure AI System Design

  • Designing for confidentiality, integrity, and availability.
  • Implementing security controls in AI pipelines.
  • Considerations for model lifecycle management.

AI Data Protection and Privacy

  • Data governance for machine learning.
  • Managing sensitive and regulated data.
  • Privacy-enhancing technologies.

Monitoring and Securing AI Operations

  • Continuous evaluation of AI behavior.
  • Detecting drift, anomalies, and misuse.
  • Operational threat intelligence for AI systems.

Regulatory and Compliance Alignment

  • Global standards impacting AI security.
  • Documentation and audit readiness.
  • Aligning governance with legal obligations.

Incident Response for AI Systems

  • AI-specific attack vectors and indicators.
  • Response workflows for compromised models.
  • Post-incident review and remediation.

Strategic AI Security Management

  • Building long-term AI security capability.
  • Integrating AI risk into enterprise strategy.
  • Maturity assessments and continuous improvement.

Summary and Next Steps

Requirements

  • A solid understanding of cybersecurity risk principles.
  • Experience with AI or data-driven systems.
  • Familiarity with enterprise security governance.

Target Audience

  • Security managers overseeing AI initiatives.
  • Governance and risk professionals.
  • Technical leaders responsible for the secure adoption of AI.
 21 Hours

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