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Duration 14 hours
Course Outline
Foundations of Gemini 3 Safety
- How Gemini 3 enhances safety and operational reliability
- Understanding mechanisms for vulnerability reduction
- Overview of threat categories specific to AI systems
Governance Principles and Policy Alignment
- Aligning organizational policies with AI usage standards
- Configuring Gemini 3 for regulated industry environments
- Establishing governance workflows for continuous oversight
Prompt Injection Defense
- Identifying various types of prompt-based attacks
- Constructing prompt structures that resist manipulation
- Evaluating and testing potential vulnerability surfaces
Responsible Data Handling
- Managing sensitive and high-risk data securely
- Ensuring ethical usage of datasets
- Mitigating risks of data leakage and confidentiality breaches
Auditing and Monitoring AI Behavior
- Implementing pipelines for behavior monitoring
- Detecting anomalous or unexpected outputs
- Maintaining audit trails for compliance verification
Risk Assessment and Scenario Planning
- Evaluating risks associated with AI-assisted operations
- Formulating effective mitigation strategies
- Simulating adverse scenarios to ensure preparedness
Secure Deployment Strategies
- Defining and configuring 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 competency
- Implementing strategies for long-term governance maturity
Summary and Next Steps
Requirements
- A foundational understanding of cybersecurity principles
- Professional experience with AI or Machine Learning systems
- Knowledge of governance structures and compliance workflows
Target Audience
- Security Engineers
- Compliance Teams
- AI Ethics Professionals
Testimonials (1)
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