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- Introduction
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- App architecture
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- Normal security considerations
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- Creative nature of generative AI
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- The model security
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- Fine-tuning
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- Protecting your IP
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- Restrict API access to models
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- Prompt injection
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- Data leakage
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- Plug-ins and agents
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- Prompt injection
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- Indirect attack
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- Content filters
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- Perform your own testing
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- Responsibility
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- AI for good
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- Summary
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- Close
Description:
Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it Learn essential security considerations and risk mitigation strategies for generative AI applications in this 44-minute technical video. Explore key topics including application architecture, model security, IP protection, and API access restrictions. Dive deep into critical security challenges like prompt injection, data leakage, and indirect attacks while understanding the importance of content filtering and thorough testing. Gain insights into the shared responsibility model and ethical AI implementation, complete with practical examples and best practices. Access supplementary resources including whiteboards, Azure AI landing zone documentation, and Microsoft's responsible AI principles to enhance your understanding of secure generative AI deployment.

Generative AI Security: Top Considerations and Risk Mitigation Strategies

John Savill's Technical Training
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