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Course Outline
Foundations of Gemini 3 Safety
- How Gemini 3 improves safety and reliability
- Understanding vulnerability reduction mechanisms
- Overview of threat categories for AI systems
Governance Principles and Policy Alignment
- Mapping organizational policies to AI usage
- Configuring Gemini 3 for regulated environments
- Governance workflows for continuous oversight
Prompt Injection Defense
- Types of prompt-based attacks
- Building resistant prompt structures
- Evaluating and testing vulnerability surfaces
Responsible Data Handling
- Managing sensitive or high-risk data
- Ensuring ethical dataset usage
- Mitigating leakage and confidentiality risks
Auditing and Monitoring AI Behavior
- Setting up behavior monitoring pipelines
- Identifying anomalous outputs
- Audit trails for compliance assurance
Risk Assessment and Scenario Planning
- Assessing risks for AI-assisted operations
- Designing mitigation strategies
- Simulating adverse scenarios for preparedness
Secure Deployment Strategies
- Configuring deployment boundaries
- Integrating Gemini 3 with secure infrastructure
- Leveraging least-privilege architectural patterns
Organizational Readiness and Best Practices
- Building cross-functional AI safety processes
- Ensuring staff readiness and capability
- Long-term governance maturity strategies
Summary and Next Steps
Requirements
- An understanding of cybersecurity fundamentals
- Experience with AI or ML-based systems
- Familiarity with governance or compliance workflows
Audience
- Security engineers
- Compliance teams
- AI ethics professionals
14 Hours
Testimonials (1)
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