Graph Database - Pacific Northwest

Building a network intrusion detector with Neo4j Graph Analytics for Snowflake. We use KNN to connect similar attack events, then train GraphSAGE on those relationships to classify malicious traffic - all natively in Snowpark Container Services. We'll match the academic benchmark (macro-F1 0.811) using nothing but SQL.

Guests: Olga Razvenskaia ( https://www.linkedin.com/in/olga-razvenskaya/ ) & Stu Moore ( https://www.linkedin.com/in/stujamesmoore/ )

Blog: https://stu-moore.medium.com/9e202e56d55c

#neo4j #graphdatabase #agenticai #knowledgelayer #knowledgegraph #graphrag #AIAgents #snowflake #cybersecurity

Fixing automotive supply chain risk with graphs, vectors, and agents A chip shortage hits and one part goes dark. Vector search finds parts that look alike. It can't tell you which vehicles are affected, whether a substitute clears compliance, or where supplier concentration creates risk. In this stream, we build a dual-store architecture - Neo4j for graph reasoning, Qdrant for semantic search - orchestrated by a LangGraph agent that runs impact analysis, validates alternatives against compliance and BOM constraints, and flags supplier risk, end to end. If you build with graphs, vectors, or agentic pipelines, come see how they hold up under pressure.

Guest: Pavan Vemuri ( https://www.linkedin.com/in/pavan-vemuri-77419723/ )

#neo4j #graphdatabase #agenticai #knowledgelayer #knowledgegraph #graphrag #AIAgents #automotive

You gave your AI agents access to the knowledge graph. Now they can answer anything, grounded in real data, no hallucinations. Problem solved, right? Not quite. What happens when someone asks the agent to summarize confidential HR files? Or pull the CEO's salary? What if an agent deletes a critical asset and you have no idea why? And how does any of this comply with GDPR or CCPA? Security is the part most teams skip when building knowledge-based AI. And when they do think about it, they reach for role-based access control, which quietly turns into a mess of role explosion and over-permissioning. That's a bad combination with AI, where behavior is dynamic and hard to predict. In this session, you'll learn how to turn your own graph into a policy engine that your agents can actually trust. We cover: Why connected data raises the security stakes for agentic AI Why RBAC breaks down and what to use instead How to apply your graph as security context Real standards you can use today: MCP, A2A, OAuth2, and the new AuthZEN authorization standard

Guest: Alex Babeanu ( https://www.linkedin.com/in/ababeanu/ )

#neo4j #graphdatabase #agenticai #knowledgelayer #knowledgegraph #graphrag #AIAgents