Intuit & Neo4j: Graph-Powered Intelligence Securing Data & Enhancing GenAI
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🎉 Let’s talk tech, graphs, GenAI—and everything in between!
Join us for an in-person tech meetup focused on real-world applications of graph technology and AI for an evening of fun, networking and food!
Expect sharp talks, technical insights, and open discussion with peers who are building, scaling, and experimenting with graph databases, AI, and modern data systems. Come for the content, stay for the solid conversations, good food, and a chance to connect with other.
RSVP SECURITY NOTE: For building security, we will provide your First Name & Last Name for security. Your email address will also be provided to facility to send you a QR code.
Title: How Intuit Safeguards the Data of 100 Million Customers with Graph
Speaker: Zach Probst, Software Engineer, Intuit
In today's AI age, security threats are becoming more sophisticated and relentless, putting businesses at risk. At Intuit, safeguarding the financial information of over 100 million customers is paramount. To this end, Knowledge Graphs (KGs) have emerged as powerful weapons in our arsenal against these growing threats. Are you curious how this technology revolutionized cybersecurity at Intuit? We'll unravel the complexities of securing cloud infrastructure by harnessing the power of KGs to improve our incident response times. We'll also expand our graph to explore how we use Software Bill of Materials (SBOM) and KGs to seek out, prioritize and remediate vulnerabilities on our developer platform. By imparting you with the opportunities we see, we hope you join us in our effort to graph our way to a secure digital frontier.
Title: Vector and GraphRAG: Accuracy and Explainability in GenAI Applications
Speaker: Jennifer Reif, Developer Advocate at Neo4j
Accuracy and explainability are critical in GenAI applications. When information from AI-integrated solutions is inaccurate, it can impact business, people’s health, financial decisions, and even legal policies, which causes cascading repercussions. Having the best data at the right time is vital.
LLMs are not able to handle this on their own, but retrieval augmented generation (RAG) can help by providing curated data as context to an LLM, guiding it to an appropriate answer. This session will explore how vector and graph RAG address the shortcomings of LLMs, explaining their shared functionality as well as some ways they handle it differently. Finally, we will see how to build a GenAI application with RAG to see these concepts in action.
Speaker Bios
Zach Probst is a Staff Software Engineer at Intuit with a strong focus on graph databases, generative AI, and cybersecurity. He is a prolific architect and developer on the Security Attribution and SSDLC teams, where he established scalable development patterns adopted by over a dozen engineers. Zach also led the design of a Generative AI Query Experience, leveraging RAG, prompt engineering, and a large graph knowledge base to enable intelligent, natural-language access to complex security data. He holds dual degrees in Computing Security and Computer Science from Rochester Institute of Technology.
Jennifer Reif is a Developer Advocate at Neo4j, speaker, and blogger with an MS in CMIS. An avid developer and problem-solver, she has worked with many businesses and projects to organize and make sense of widespread data assets and leverage them for maximum business value. She has expertise in a variety of commercial and open source tools, and she enjoys learning new technologies, sometimes on a daily basis! Her passion is finding ways to organize chaos and deliver software more effectively.