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Evaluating RAG and the Future of LLM Security: Insights with LlamaIndex

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Manage episode 414620480 series 3461851
محتوای ارائه شده توسط MLSecOps.com. تمام محتوای پادکست شامل قسمت‌ها، گرافیک‌ها و توضیحات پادکست مستقیماً توسط MLSecOps.com یا شریک پلتفرم پادکست آن‌ها آپلود و ارائه می‌شوند. اگر فکر می‌کنید شخصی بدون اجازه شما از اثر دارای حق نسخه‌برداری شما استفاده می‌کند، می‌توانید روندی که در اینجا شرح داده شده است را دنبال کنید.https://fa.player.fm/legal

Send us a text

In this episode of the MLSecOps Podcast, host Neal Swaelens, along with co-host Oleksandr Yaremchuk, sit down with special guest Simon Suo, co-founder and CTO of LlamaIndex. Simon shares insights into the development of LlamaIndex, a leading data framework for orchestrating data in large language models (LLMs). Drawing from his background in the self-driving industry, Simon discusses the challenges and considerations of integrating LLMs into various applications, emphasizing the importance of contextualizing LLMs within specific environments.
The conversation delves into the evolution of retrieval-augmented generation (RAG) techniques and the future trajectory of LLM-based applications. Simon comments on the significance of balancing performance with cost and latency in leveraging LLM capabilities, envisioning a continued focus on data orchestration and enrichment.
Addressing LLM security concerns, Simon emphasizes the critical need for robust input and output evaluation to mitigate potential risks. He discusses the potential vulnerabilities associated with LLMs, including prompt injection attacks and data leakage, underscoring the importance of implementing strong access controls and data privacy measures. Simon also highlights the ongoing efforts within the LLM community to address security challenges and foster a culture of education and awareness.
As the discussion progresses, Simon introduces LlamaCloud, an enterprise data platform designed to streamline data processing and storage for LLM applications. He emphasizes the platform's tight integration with the open-source LlamaIndex framework, offering users a seamless transition from experimentation to production-grade deployments. Listeners will also learn about LlamaIndex's parsing solution, LlamaParse.
Join us to learn more about the ongoing journey of innovation in large language model-based applications, while remaining vigilant about LLM security considerations.

Thanks for checking out the MLSecOps Podcast! Get involved with the MLSecOps Community and find more resources at https://community.mlsecops.com.
Additional tools and resources to check out:
Protect AI Guardian: Zero Trust for ML Models

Recon: Automated Red Teaming for GenAI

Protect AI’s ML Security-Focused Open Source Tools

LLM Guard Open Source Security Toolkit for LLM Interactions

Huntr - The World's First AI/Machine Learning Bug Bounty Platform

  continue reading

41 قسمت

Artwork
iconاشتراک گذاری
 
Manage episode 414620480 series 3461851
محتوای ارائه شده توسط MLSecOps.com. تمام محتوای پادکست شامل قسمت‌ها، گرافیک‌ها و توضیحات پادکست مستقیماً توسط MLSecOps.com یا شریک پلتفرم پادکست آن‌ها آپلود و ارائه می‌شوند. اگر فکر می‌کنید شخصی بدون اجازه شما از اثر دارای حق نسخه‌برداری شما استفاده می‌کند، می‌توانید روندی که در اینجا شرح داده شده است را دنبال کنید.https://fa.player.fm/legal

Send us a text

In this episode of the MLSecOps Podcast, host Neal Swaelens, along with co-host Oleksandr Yaremchuk, sit down with special guest Simon Suo, co-founder and CTO of LlamaIndex. Simon shares insights into the development of LlamaIndex, a leading data framework for orchestrating data in large language models (LLMs). Drawing from his background in the self-driving industry, Simon discusses the challenges and considerations of integrating LLMs into various applications, emphasizing the importance of contextualizing LLMs within specific environments.
The conversation delves into the evolution of retrieval-augmented generation (RAG) techniques and the future trajectory of LLM-based applications. Simon comments on the significance of balancing performance with cost and latency in leveraging LLM capabilities, envisioning a continued focus on data orchestration and enrichment.
Addressing LLM security concerns, Simon emphasizes the critical need for robust input and output evaluation to mitigate potential risks. He discusses the potential vulnerabilities associated with LLMs, including prompt injection attacks and data leakage, underscoring the importance of implementing strong access controls and data privacy measures. Simon also highlights the ongoing efforts within the LLM community to address security challenges and foster a culture of education and awareness.
As the discussion progresses, Simon introduces LlamaCloud, an enterprise data platform designed to streamline data processing and storage for LLM applications. He emphasizes the platform's tight integration with the open-source LlamaIndex framework, offering users a seamless transition from experimentation to production-grade deployments. Listeners will also learn about LlamaIndex's parsing solution, LlamaParse.
Join us to learn more about the ongoing journey of innovation in large language model-based applications, while remaining vigilant about LLM security considerations.

Thanks for checking out the MLSecOps Podcast! Get involved with the MLSecOps Community and find more resources at https://community.mlsecops.com.
Additional tools and resources to check out:
Protect AI Guardian: Zero Trust for ML Models

Recon: Automated Red Teaming for GenAI

Protect AI’s ML Security-Focused Open Source Tools

LLM Guard Open Source Security Toolkit for LLM Interactions

Huntr - The World's First AI/Machine Learning Bug Bounty Platform

  continue reading

41 قسمت

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