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Duration 14 hours
Course Outline
Gemini 3 Safety Fundamentals
- Enhancements in safety and reliability within Gemini 3
- Understanding mechanisms for reducing vulnerabilities
- An overview of threat categories specific to AI systems
Governance Principles and Policy Integration
- Aligning organizational policies with AI utilization
- Configuring Gemini 3 for highly regulated sectors
- Establishing workflows for continuous governance oversight
Defending Against Prompt Injection
- Identifying various forms of prompt-based attacks
- Constructing prompt structures that resist manipulation
- Testing and evaluating potential vulnerability points
Responsible Data Management
- Handling sensitive or high-risk information securely
- Safeguarding the ethical use of datasets
- Mitigating risks associated with data leakage and confidentiality
Monitoring and Auditing AI Performance
- Implementing pipelines for behavioral monitoring
- Detecting anomalous or unexpected outputs
- Maintaining audit trails to support compliance
Risk Analysis and Scenario Simulation
- Evaluating risks in AI-assisted workflows
- Developing targeted mitigation strategies
- Simulating adverse scenarios to ensure operational readiness
Strategies for Secure Deployment
- Defining clear deployment boundaries
- Integrating Gemini 3 into secure infrastructure ecosystems
- Applying least-privilege principles in architecture
Organizational Preparedness and Best Practices
- Cultivating cross-functional processes for AI safety
- Ensuring team readiness and skill development
- Long-term strategies for advancing governance maturity
Conclusions and Path Forward
Requirements
- Core knowledge of cybersecurity principles
- Professional experience with AI or machine learning systems
- Familiarity with governance and compliance processes
Target Audience
- Security Engineers
- Compliance Professionals
- AI Ethics Specialists
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