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Course Outline

Foundations of Gemini 3 Safety

  • How Gemini 3 enhances safety and reliability.
  • Understanding mechanisms for reducing vulnerabilities.
  • Overview of threat categories affecting AI systems.

Governance Principles and Policy Alignment

  • Mapping organisational policies to AI usage.
  • Configuring Gemini 3 for regulated environments.
  • Establishing governance workflows for continuous oversight.

Prompt Injection Defences

  • Types of prompt-based attacks.
  • Building resistant prompt structures.
  • Evaluating and testing vulnerability surfaces.

Responsible Data Handling

  • Managing sensitive or high-risk data.
  • Ensuring ethical usage of datasets.
  • Mitigating data leakage and confidentiality risks.

Auditing and Monitoring AI Behaviour

  • Setting up behaviour monitoring pipelines.
  • Identifying anomalous outputs.
  • Maintaining audit trails for compliance assurance.

Risk Assessment and Scenario Planning

  • Assessing risks associated with AI-assisted operations.
  • Designing effective mitigation strategies.
  • Simulating adverse scenarios to enhance preparedness.

Secure Deployment Strategies

  • Configuring deployment boundaries.
  • Integrating Gemini 3 with secure infrastructure.
  • Leveraging least-privilege architectural patterns.

Organisational Readiness and Best Practices

  • Building cross-functional AI safety processes.
  • Ensuring staff readiness and capability.
  • Strategies for long-term governance maturity.

Summary and Next Steps

Requirements

  • A solid understanding of cybersecurity fundamentals.
  • Previous experience with AI or machine learning-based systems.
  • Familiarity with governance or compliance workflows.

Audience

  • Security engineers.
  • Compliance teams.
  • AI ethics professionals.
 14 Hours

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