Mapping Entities and Assessing Risk in Graph‑Backed Storage Protocols

Session Track: Data Intelligence

Session Time: 05:30 am - 06:00 am November 6

Session description

In modern enterprises, data flows across teams, tools, and territories—often without clear boundaries. Understanding who has access to what kind of data, under what authority, and with what risk is critical for compliance, governance, and security. This session dives into building a one-on-one entity mapping framework using Neo4j to represent storage protocols, user access, departmental ownership, data types, and governance authorities as interconnected graph nodes. We’ll demonstrate how to map relationships such as :OWNS, :STORES, :ACCESS_GRANTED, and :GOVERNS to model real-world storage systems, including file servers, databases, cloud buckets, and more. But we won’t stop at visualization. We'll layer in a risk calculation engine that scores each data access path based on multiple weighted factors: Data sensitivity (PII, PHI, IP) Access privilege level (read, write, admin) Department-to-data alignment Compliance impact (GDPR, HIPAA, etc.) Orphaned or stale data risks Centrality and graph traversal exposure Attendees will learn how to implement this architecture using Cypher and APOC, derive actionable insights like "high-risk access patterns" and "authority mismatches," and plug the model into alerting or reporting systems. Whether you're working in cybersecurity, compliance, data governance, or enterprise IT, this session will help you rethink access control and risk in a structured, graph-native way.