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Duration 14 hours
Course Outline
Foundations of Gemini 3 Safety
- Exploring how Gemini 3 enhances safety and reliability
- Analyzing mechanisms for reducing system vulnerabilities
- Reviewing key threat categories relevant to AI systems
Governance Principles and Policy Alignment
- Aligning organizational policies with AI utilization
- Tuning Gemini 3 settings for regulated environments
- Establishing workflows for ongoing governance oversight
Defending Against Prompt Injection
- Identifying various forms of prompt-based attacks
- Constructing prompt structures that resist manipulation
- Testing and evaluating potential vulnerability surfaces
Responsible Data Handling
- Handling sensitive or high-risk data responsibly
- Promoting ethical usage of datasets
- Reducing risks associated with data leakage and confidentiality
Auditing and Monitoring AI Behavior
- Implementing pipelines for behavioral monitoring
- Detecting anomalous outputs in real-time
- Maintaining audit trails to ensure compliance
Risk Assessment and Scenario Planning
- Evaluating risks within AI-assisted operations
- Formulating effective mitigation strategies
- Simulating adverse scenarios to enhance preparedness
Secure Deployment Strategies
- Defining clear boundaries for deployment
- Integrating Gemini 3 with secure infrastructure
- Applying least-privilege architectural principles
Organizational Readiness and Best Practices
- Developing cross-functional AI safety processes
- Ensuring staff competence and readiness
- Planning for long-term governance maturity
Summary and Next Steps
Requirements
- Foundational knowledge of cybersecurity principles
- Practical experience with AI or ML-based systems
- Acquaintance with governance or compliance processes
Target Audience
- Security engineers
- Compliance teams
- AI ethics specialists
Testimonials (1)
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