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Duration 14 hours
Course Outline
Core Foundations of Gemini 3 Safety
- Examining how Gemini 3 enhances safety and reliability.
- Analyzing mechanisms for vulnerability reduction.
- Surveying key threat categories specific to AI systems.
Governance Frameworks and Policy Integration
- Aligning corporate policies with AI utilization practices.
- Adapting Gemini 3 configurations for highly regulated sectors.
- Designing workflows for continuous governance oversight.
Defending Against Prompt Injection
- Identifying various forms of prompt-based attacks.
- Constructing prompt structures that are inherently resistant to manipulation.
- Assessing and stress-testing potential vulnerability surfaces.
Responsible Data Management
- Handling sensitive and high-risk data with care.
- Promoting the ethical use of datasets.
- Reducing risks associated with data leakage and confidentiality breaches.
AI Behavior Auditing and Monitoring
- Establishing pipelines for real-time behavior monitoring.
- Detecting and responding to anomalous outputs.
- Maintaining audit trails to support compliance verification.
Risk Assessment and Scenario Simulation
- Evaluating risks in AI-assisted operational contexts.
- Formulating effective mitigation strategies.
- Running simulations of adverse scenarios to enhance preparedness.
Strategies for Secure Deployment
- Defining clear deployment boundaries.
- Seamlessly integrating Gemini 3 into secure infrastructure.
- Applying least-privilege architectural patterns.
Organizational Preparedness and Best Practices
- Developing cross-functional processes for AI safety.
- Ensuring team readiness and technical proficiency.
- Planning for long-term governance maturity.
Conclusions and Future Actions
Requirements
- Solid grasp of cybersecurity basics.
- Practical experience with AI or machine learning systems.
- Knowledge of governance and compliance procedures.
Target Audience
- Security engineers.
- Compliance specialists.
- AI ethics professionals.
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