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Duration 14 hours
Course Outline
Foundations of Gemini 3 Safety
- How Gemini 3 enhances safety and reliability standards
- Understanding mechanisms for reducing vulnerabilities
- An overview of threat categories relevant to AI systems
Governance Principles and Policy Alignment
- Aligning organizational policies with AI usage standards
- Configuring Gemini 3 for regulated operational environments
- Establishing governance workflows for continuous oversight
Prompt Injection Defense
- Identifying various types of prompt-based attacks
- Constructing prompt structures resistant to manipulation
- Evaluating and testing potential vulnerability surfaces
Responsible Data Handling
- Managing sensitive or high-risk data assets
- Safeguarding ethical dataset usage
- Mitigating risks related to data leakage and confidentiality
Auditing and Monitoring AI Behavior
- Setting up pipelines for behavioral monitoring
- Detecting anomalous outputs
- Maintaining audit trails for compliance assurance
Risk Assessment and Scenario Planning
- Evaluating risks in AI-assisted operations
- Formulating effective mitigation strategies
- Simulating adverse scenarios to improve preparedness
Secure Deployment Strategies
- Defining and configuring deployment boundaries
- Integrating Gemini 3 with secure infrastructure
- Utilizing least-privilege architectural patterns
Organizational Readiness and Best Practices
- Developing cross-functional AI safety processes
- Ensuring staff readiness and technical capability
- 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
- Familiarity with governance and compliance workflows
Target Audience
- Security engineers
- Compliance teams
- AI ethics specialists
Testimonials (1)
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