What Microsoft learnt from building Ai applications

Lessons you will find valuable from Microsoft

Microsoft's AI Red Team has released a groundbreaking paper titled "Lessons From Red Teaming 100 Generative AI Products" (https://lnkd.in/dGxsydwF)

🌎 Drawing from their extensive experience, they've distilled eight pivotal lessons for enhancing the safety and security of Gen AI systems:-

1. Understand what the system can do and where it is applied.
2. You don’t have to compute gradients to break an AI system. 3. AI red teaming is not safety benchmarking.
3. Automation can help cover more of the risk landscape.
4. The human element of AI red teaming is crucial.
5. Responsible AI harms are pervasive but difficult to measure.
6. LLMs amplify existing security risks and introduce new ones.
7. The work of securing AI systems will never be complete.

📌 Distinguish between Red teaming and safety Benchmarking - Red teaming involves simulating real-world attacks to uncover vulnerabilities, whereas safety benchmarking assesses performance against predefined standards.

🤖 Leverage automation - Utilizing tools like PyRIT can help cover a broader risk landscape more efficiently.

👭 Human judgment is irreplaceable - While automation aids the process, human expertise is essential for nuanced assessments and decision-making.

💭 Responsible AI harms are complex - Identifying and measuring harms require careful consideration, as they can be pervasive yet subtle.

👉 LLMs introduce new security challenges - Large Language Models can amplify existing risks and present novel ones, necessitating continuous vigilance.

👉 Security is an Ongoing Process - Ensuring the safety of AI systems is a continuous effort, demanding regular updates and assessments.

📜 This paper is a must-read for AI practitioners aiming to fortify their systems against emerging threats.

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