Microsoft Mechanics Podcast

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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