What are prompt injection attacks and how do you stop them? How do you avoid deceptive responses? Can AI traffic be end-to-end encrypted? We'll answer these questions and more with technical demonstrations to make it real.

Mark Russinovich will show you how to develop and deploy AI applications that prioritize safety, privacy, and integrity. Leverage real-time safety guardrails to filter harmful content and proactively prevent misuse, ensuring AI outputs are trustworthy. The integration of confidential inferencing enables users to maintain data privacy by encrypting information during processing, safeguarding sensitive data from exposure. Enhance AI solutions with advanced features like Groundedness detection, which provides real-time corrections to inaccurate outputs, and the Confidential Computing initiative that extends verifiable privacy across all services.

Mark Russinovich, Azure CTO, joins Jeremy Chapman to share how to build secure AI applications, monitor and manage potential risks, and ensure compliance with privacy regulations.

 

► QUICK LINKS: 00:00 - Keep data safe and private 01:19 - Azure AI Content Safety capability set 02:17 - Direct jailbreak attack 03:47 - Put controls in place 04:54 - Indirect prompt injection attack 05:57 - Options to monitor attacks over time 06:22 - Groundedness detection 07:45 - Privacy—Confidential Computing 09:40 - Confidential inferencing Model-as-a-service 11:31 - Ensure services and APIs are trustworthy 11:50 - Security 12:51 - Web Query Transparency 13:51 - Microsoft Defender for Cloud Apps 15:16 - Wrap up

 

► Link References

Check out https://aka.ms/MicrosoftTrustworthyAI 

For verifiable privacy, go to our blog at https://aka.ms/ConfidentialInferencing 

 

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