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Duration 14 hours
Course Outline
Foundations of Gemini 3 Safety
- Enhancements in Gemini 3’s safety and reliability standards
- Mechanisms for reducing system vulnerabilities
- Categorization of threats specific to AI systems
Governance Principles and Policy Alignment
- Aligning organizational policies with AI usage protocols
- Tailoring Gemini 3 for regulated industries and environments
- Establishing continuous oversight through governance workflows
Prompt Injection Defense
- Identifying various types of prompt-based attacks
- Constructing prompt structures resistant to manipulation
- Testing and evaluating potential vulnerability surfaces
Responsible Data Handling
- Protocols for managing sensitive or high-risk data
- Ensuring ethical usage of datasets
- Mitigating risks related to data leakage and confidentiality
Auditing and Monitoring AI Behavior
- Implementing pipelines for behavioral monitoring
- Detecting anomalous AI 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 enhance preparedness
Secure Deployment Strategies
- Defining clear deployment boundaries
- Integrating Gemini 3 with secure infrastructure components
- Applying least-privilege architectural patterns
Organizational Readiness and Best Practices
- Developing cross-functional AI safety processes
- Ensuring staff competency and readiness
- Planning for long-term governance maturity
Summary and Next Steps
Requirements
- A solid grasp of cybersecurity fundamentals
- Practical experience with AI or ML-based systems
- Familiarity with governance or compliance workflows
Target Audience
- Security engineers
- Compliance teams
- AI ethics specialists
Testimonials (1)
Flow , vibe and topic on presentation
