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Duration 14 hours
Course Outline
Foundations of Gemini 3 Safety
- How Gemini 3 enhances safety and reliability
- Understanding mechanisms for vulnerability reduction
- Overview of threat categories relevant to AI systems
Governance Principles and Policy Alignment
- Mapping organizational policies to AI utilization
- Configuring Gemini 3 for regulated environments
- Establishing governance workflows for continuous oversight
Defending Against Prompt Injection
- Identifying various types of prompt-based attacks
- Constructing resilient prompt architectures
- Evaluating and testing for potential vulnerability surfaces
Responsible Data Handling
- Managing sensitive or high-risk data assets
- Ensuring the ethical use of datasets
- Mitigating risks related to data leakage and confidentiality
Auditing and Monitoring AI Behavior
- Establishing pipelines for behavioral monitoring
- Detecting anomalous outputs
- Maintaining audit trails for compliance verification
Risk Assessment and Scenario Planning
- Evaluating risks in 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 competence
- Implementing long-term governance maturity strategies
Summary and Next Steps
Requirements
- A solid grasp of cybersecurity fundamentals
- Practical experience with AI or ML-based systems
- Working knowledge of governance or compliance workflows
Target Audience
- Security engineers
- Compliance teams
- AI ethics specialists
Testimonials (1)
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