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Course Outline

Foundations of AI Security Governance

  • Core principles of AI governance
  • Enterprise security frameworks for AI
  • Roles and responsibilities of stakeholders

AI Risk Assessment Methodologies

  • Identifying and categorizing AI security risks
  • Threat modeling for AI-enabled systems
  • Conducting impact assessments and prioritizing risks

Designing Secure AI Systems

  • Ensuring confidentiality, integrity, and availability
  • Implementing security controls within AI pipelines
  • Considering model lifecycle management

Data Protection and Privacy in AI

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

Monitoring and Securing AI Operations

  • Continuously evaluating AI behavior
  • Detecting drift, anomalies, and potential misuse
  • Gathering operational threat intelligence for AI systems

Regulatory and Compliance Alignment

  • Global standards influencing AI security
  • Preparing documentation and ensuring audit readiness
  • Aligning governance with legal obligations

Incident Response for AI Systems

  • Identifying AI-specific attack vectors and indicators
  • Establishing response workflows for compromised models
  • Conducting post-incident reviews and remediation

Strategic AI Security Management

  • Building long-term AI security capabilities
  • Integrating AI risk into enterprise strategy
  • Performing maturity assessments and driving continuous improvement

Summary and Next Steps

Requirements

  • Understanding of cybersecurity risk principles
  • Hands-on 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 secure AI adoption
 21 Hours

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