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 Duration 21 hours

Course Outline

Foundations of AI Security Governance

  • Essential principles of AI governance
  • Enterprise security frameworks applicable to AI
  • Roles and responsibilities of key stakeholders

Methodologies for AI Risk Assessment

  • Recognizing and classifying AI security risks
  • Threat modeling for systems enabled by AI
  • Assessing impact and prioritizing actions

Secure Design of AI Systems

  • Designing for confidentiality, integrity, and availability
  • Integrating security controls within AI pipelines
  • Considerations for managing the model lifecycle

AI Data Protection and Privacy

  • Data governance specific to machine learning
  • Handling sensitive and regulated data
  • Utilization of privacy-enhancing technologies

Monitoring and Securing AI Operations

  • Ongoing evaluation of AI behavior
  • Identification of drift, anomalies, and misuse
  • Operational threat intelligence for AI systems

Regulatory and Compliance Alignment

  • Global standards influencing AI security
  • Preparing documentation and audits
  • Aligning governance with legal obligations

Incident Response for AI Systems

  • AI-specific attack vectors and indicators
  • Response protocols for compromised models
  • Post-incident analysis and remediation

Strategic AI Security Management

  • Developing long-term AI security capabilities
  • Integrating AI risk into enterprise strategy
  • Conducting maturity assessments and continuous improvement

Summary and Next Steps

Requirements

  • A solid grasp of cybersecurity risk fundamentals
  • Hands-on experience with AI or data-centric systems
  • Knowledge of enterprise security governance practices

Target Audience

  • Security managers overseeing AI initiatives
  • Governance and risk professionals
  • Technical leaders accountable for secure AI adoption

Testimonials (3)

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