Secure RAG Systems: A DeepTech Exploration with Protecto’s COO, Protik Mukhopadhyay

This interview podcast features David Yakobovitch interviewing Protik Mukhopadhyay, COO of Protecto.ai, a startup building Retrieval-Augmented Generation (RAG) solutions. The conversation explores RAG's significance in enterprise applications, focusing on its architectural patterns and addressing security concerns, particularly for sensitive data in healthcare and finance. Mukhopadhyay highlights key challenges in RAG implementation, such as data quality, handling edge cases, and managing operational costs, while emphasizing the importance of ethical considerations and responsible AI practices. Specific examples of successful RAG applications in creative writing, legal contract drafting, and customer support are provided, along with advice on choosing between on-premise and SaaS deployment models based on security needs. The interview concludes with a discussion of future trends in secure and privacy-focused AI, emphasizing the need for improved relevance and explainability in RAG systems.

Outlines

Part 1: Introduction and Background

Part 2: RAG Systems: Technical Aspects and Challenges

Part 3: RAG Applications, Ethics, and Responsible AI

Part 4: Deployment, Access Control, and Future Trends

Part 5: Conclusion

Sign in to continue reading, translating and more.

Open full episode in Podwise

HumAIn Podcast

Part 1: Introduction and Background

Introduction to the Humane Podcast and Protecto.ai

Protik Mukhopadhyay's Journey and Protecto.ai's Formation

Part 2: RAG Systems: Technical Aspects and Challenges

Introduction to Retrieval-Augmented Generation (RAG)

Technical and Architectural Patterns in RAG AI Systems and Security Concerns

Challenges in Implementing RAG Systems

Part 3: RAG Applications, Ethics, and Responsible AI

Effective Use Cases for RAG Systems

Ethics and Responsible AI in RAG Systems

Protecto.ai's Approach to Responsible AI

Part 4: Deployment, Access Control, and Future Trends

Lessons Learned in Deploying RAG AI Systems

On-Premise vs. SaaS Deployment Models for Protecto.ai

Role-Based Access Control (RBAC) in Protecto.ai's RAG Process

Future Trends in Secure and Privacy-Focused AI

Part 5: Conclusion

Conclusion and Call to Action

Secure RAG Systems: A DeepTech Exploration with Protecto’s COO, Protik Mukhopadhyay

HumAIn Podcast

Part 1: Introduction and Background

01:04Introduction to the Humane Podcast and Protecto.ai

Introduction to the Humane Podcast and Protecto.ai

02:43Protik Mukhopadhyay's Journey and Protecto.ai's Formation

Protik Mukhopadhyay's Journey and Protecto.ai's Formation

Part 2: RAG Systems: Technical Aspects and Challenges

04:16Introduction to Retrieval-Augmented Generation (RAG)

Introduction to Retrieval-Augmented Generation (RAG)

07:21Technical and Architectural Patterns in RAG AI Systems and Security Concerns

Technical and Architectural Patterns in RAG AI Systems and Security Concerns

09:53Challenges in Implementing RAG Systems

Challenges in Implementing RAG Systems

Part 3: RAG Applications, Ethics, and Responsible AI

12:18Effective Use Cases for RAG Systems

Effective Use Cases for RAG Systems

15:21Ethics and Responsible AI in RAG Systems

Ethics and Responsible AI in RAG Systems

20:55Protecto.ai's Approach to Responsible AI

Protecto.ai's Approach to Responsible AI

Part 4: Deployment, Access Control, and Future Trends

24:46Lessons Learned in Deploying RAG AI Systems

Lessons Learned in Deploying RAG AI Systems

28:20On-Premise vs. SaaS Deployment Models for Protecto.ai

On-Premise vs. SaaS Deployment Models for Protecto.ai

30:44Role-Based Access Control (RBAC) in Protecto.ai's RAG Process

Role-Based Access Control (RBAC) in Protecto.ai's RAG Process

33:44Future Trends in Secure and Privacy-Focused AI

Future Trends in Secure and Privacy-Focused AI

Part 5: Conclusion

36:36Conclusion and Call to Action

Conclusion and Call to Action