Get in Touch

Course Outline

Foundations of AI Security Governance

  • Essential principles of AI governance
  • Applying enterprise security frameworks to AI
  • Defining stakeholder roles and responsibilities

AI Risk Assessment Methodologies

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

Secure AI System Design

  • Designing for confidentiality, integrity, and availability
  • Integrating security controls into AI pipelines
  • Managing the model lifecycle securely

AI Data Protection and Privacy

  • Data governance strategies for machine learning
  • Handling sensitive and regulated data responsibly
  • Leveraging privacy-enhancing technologies

Monitoring and Securing AI Operations

  • Continuous evaluation of AI behavior
  • Detecting drift, anomalies, and potential misuse
  • Applying operational threat intelligence to AI systems

Regulatory and Compliance Alignment

  • Understanding global standards affecting AI security
  • Preparing for documentation and audits
  • Aligning governance practices with legal obligations

Incident Response for AI Systems

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

Strategic AI Security Management

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

Summary and Next Steps

Requirements

  • A solid grasp of cybersecurity risk principles
  • Practical experience with AI or data-driven systems
  • Knowledge of enterprise security governance frameworks

Intended Audience

  • Security managers overseeing AI initiatives
  • Professionals in governance and risk management
  • Technical leaders tasked with secure AI adoption
 21 Hours

Number of participants


Price per participant

Testimonials (3)

Upcoming Courses

Related Categories