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Duration 14 hours
Course Outline
Core Principles of Gemini 3 Safety
- Enhancements in safety and reliability within Gemini 3
- Mechanisms for reducing system vulnerabilities
- Overview of threat vectors specific to AI systems
Governance Strategies and Policy Integration
- Aligning organizational policies with AI usage
- Tailoring Gemini 3 for regulated industry environments
- Implementing governance workflows for ongoing oversight
Defending Against Prompt Injection
- Identifying various types of prompt-based threats
- Constructing prompts that are resistant to manipulation
- Assessing and testing potential vulnerability points
Ethical Data Management
- Handling sensitive or high-risk data effectively
- Safeguarding ethical practices in dataset utilization
- Reducing risks associated with data leakage and confidentiality
Monitoring and Auditing AI Conduct
- Establishing pipelines for behavioral monitoring
- Detecting anomalies in AI outputs
- Maintaining audit trails to support compliance
Risk Analysis and Scenario Modeling
- Evaluating risks in AI-assisted workflows
- Formulating effective mitigation plans
- Simulating adverse events to ensure preparedness
Strategies for Secure Deployment
- Defining secure deployment boundaries
- Integrating Gemini 3 with secure infrastructure components
- Applying least-privilege principles in architectural design
Organizational Preparedness and Best Practices
- Cultivating cross-functional AI safety processes
- Ensuring team readiness and competency
- Advancing long-term governance maturity
Conclusion and Future Steps
Requirements
- A solid grasp of cybersecurity fundamentals
- Experience working with AI or ML-based systems
- Knowledge of governance and compliance processes
Target Audience
- Security engineers
- Compliance specialists
- AI ethics professionals
Testimonials (1)
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