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Duration 14 hours
Course Outline
Foundations of Gemini 3 Safety
- Improvements in safety and reliability within Gemini 3
- Mechanisms for reducing system vulnerabilities
- Key threat categories facing AI systems
Governance Principles and Policy Alignment
- Aligning organizational policies with AI usage
- Configuring Gemini 3 for regulated sectors
- Workflow design for continuous governance oversight
Defending Against Prompt Injection
- Classification of prompt-based attack vectors
- Constructing prompts with inherent resistance
- Testing and evaluating potential vulnerability surfaces
Responsible Data Management
- Handling sensitive or high-risk data effectively
- Promoting ethical use of datasets
- Mitigating risks associated with data leakage and confidentiality
Auditing and Monitoring AI Conduct
- 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
- Formulating effective mitigation strategies
- Simulating adverse scenarios to enhance preparedness
Secure Deployment Strategies
- Defining deployment boundaries and constraints
- Integrating Gemini 3 with secure infrastructure
- Adopting least-privilege architectural patterns
Organizational Readiness and Best Practices
- Developing cross-functional AI safety processes
- Ensuring staff competency and readiness
- Strategies for long-term governance maturity
Conclusion and Next Steps
Requirements
- A solid grasp of cybersecurity fundamentals
- Working experience with AI or machine learning-based systems
- Knowledge of governance or compliance workflows
Intended Audience
- Security engineers
- Compliance teams
- AI ethics specialists
Testimonials (1)
Flow , vibe and topic on presentation
