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Course Outline

Foundations of Gemini 3 Safety

  • How Gemini 3 enhances safety and reliability
  • Understanding mechanisms for reducing vulnerabilities
  • Overview of threat categories specific to AI systems

Governance Principles and Policy Alignment

  • Aligning organizational policies with AI usage guidelines
  • Configuring Gemini 3 for regulated operating environments
  • Implementing governance workflows for continuous oversight

Prompt Injection Defense

  • Identification of prompt-based attack vectors
  • Developing resilient prompt structures
  • Evaluating and testing potential vulnerability surfaces

Responsible Data Handling

  • Managing sensitive or high-risk data sets
  • Ensuring ethical use of datasets
  • Mitigating risks of data leakage and confidentiality breaches

Auditing and Monitoring AI Behavior

  • Establishing pipelines for behavior monitoring
  • Identifying anomalous outputs
  • Utilizing audit trails for compliance assurance

Risk Assessment and Scenario Planning

  • Evaluating risks associated with AI-assisted operations
  • Designing effective mitigation strategies
  • Simulating adverse scenarios to enhance preparedness

Secure Deployment Strategies

  • Defining deployment boundaries
  • Integrating Gemini 3 with secure infrastructure
  • Applying least-privilege architectural patterns

Organizational Readiness and Best Practices

  • Developing cross-functional AI safety processes
  • Ensuring staff readiness and capability
  • Strategies for long-term governance maturity

Summary and Next Steps

Requirements

  • Foundational knowledge of cybersecurity principles
  • Practical experience with AI or machine learning systems
  • Familiarity with governance or compliance procedures

Target Audience

  • Security engineers
  • Compliance officers and teams
  • AI ethics professionals
 14 Hours

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