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Duration 14 hours
Course Outline
Foundations of Gemini 3 Safety
- Ways Gemini 3 enhances safety and reliability
- Comprehending mechanisms that reduce vulnerabilities
- An overview of threat categories relevant to AI systems
Governance Principles and Policy Alignment
- Aligning organizational policies with AI usage
- Setting up Gemini 3 for regulated environments
- Implementing governance workflows for ongoing oversight
Defending Against Prompt Injection
- Identifying various types of prompt-based attacks
- Constructing prompt structures that resist manipulation
- Assessing and testing potential vulnerability areas
Responsible Data Management
- Handling sensitive or high-risk data securely
- Ensuring the ethical use of datasets
- Reducing risks associated with data leakage and confidentiality
Auditing and Monitoring AI Behavior
- Establishing pipelines for behavior monitoring
- Detecting anomalous outputs
- Maintaining audit trails to ensure compliance
Risk Assessment and Scenario Planning
- Evaluating risks in AI-assisted operations
- Developing effective mitigation strategies
- Simulating adverse scenarios to ensure preparedness
Secure Deployment Strategies
- Defining deployment boundaries
- Integrating Gemini 3 with secure infrastructure
- Applying least-privilege architectural principles
Organizational Readiness and Best Practices
- Creating cross-functional processes for AI safety
- Ensuring staff are prepared and capable
- Adopting strategies for long-term governance maturity
Summary and Next Steps
Requirements
- A foundational grasp of cybersecurity principles
- Practical experience with AI or ML-based systems
- Knowledge of governance and compliance processes
Target Audience
- Security engineers
- Compliance teams
- AI ethics specialists
Testimonials (1)
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