Introduction
Building resilient guardrails for a generative future
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1. Foundations of AI Trust and Safety
Historical context: From content moderation to AI governance
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Why AI trust and safety matters
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Core principles: SAFER framework
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Stakeholders and roles in AI trust and safety
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2. The GenAI Era
GenAI basics
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GenAI risks and challenges
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Learning from major GenAI platforms
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3. AI Safety Risk Assessment and Mapping
Purpose and steps of risk mapping
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Motivations: Why attackers target AI
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Abuse vectors across the AI lifecycle
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Emerging exploits and next-generation tactics
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4. Regulatory and Governance Essentials
Global regulatory landscape
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Corporate governance and internal policy
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Documentation and reporting: Building transparency and trust
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5. Technical Tools and Design Strategies
The importance of high-quality training data
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Techniques for safety evaluations and red teaming
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User-centric design for trust and safety
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Integrating explainable AI and privacy techniques
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6. Ongoing Monitoring and Mitigation
Real-time monitoring for AI systems
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Developing feedback loops for continuous improvement
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Incident response and management for AI systems
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7. Strategic Outlook
Future directions and enterprise adoption
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Agentic AI and oversight
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Differentiating through trust
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Scaling across the enterprise
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Thought leadership in AI trust and safety
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Navigating global regulatory trends
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Conclusion
Championing AI trust and safety
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