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Course Outline

Foundations of AI Security Governance

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

Methodologies for AI Risk Assessment

  • Identification and categorization of AI security risks
  • Threat modeling for AI-enabled systems
  • Impact analysis and risk prioritization

Designing Secure AI Systems

  • Engineering for confidentiality, integrity, and availability
  • Deploying security controls within AI workflows
  • Key considerations for model lifecycle management

Data Protection and Privacy in AI

  • Data governance for machine learning processes
  • Handling sensitive and regulated data
  • Utilizing privacy-enhancing technologies

Monitoring and Securing AI Operations

  • Continuous evaluation of AI system behavior
  • Identifying drift, anomalies, and potential misuse
  • Leveraging operational threat intelligence for AI

Aligning with Regulatory and Compliance Standards

  • Global standards influencing AI security
  • Documentation and readiness for audits
  • Ensuring governance aligns with legal obligations

Incident Response for AI Systems

  • AI-specific attack vectors and indicators of compromise
  • Response procedures for compromised models
  • Post-incident review and remediation strategies

Strategic AI Security Management

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

Summary and Next Steps

Requirements

  • Familiarity with cybersecurity risk principles
  • Hands-on experience with AI or data-centric systems
  • Knowledge of enterprise security governance

Target Audience

  • Security managers leading AI initiatives
  • Governance and risk management professionals
  • Technical leaders tasked with ensuring secure AI adoption
 21 Hours

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