Neo4j's Risk Register: Maximise Your Data Insights
summary
Legacy spreadsheet risk register becomes a Neo4j graph via Apache Hop and GitHub Actions running a 24‑hour ETL, producing Bloom/Neo Dash dashboards and Graph Data Science pipelines. Graph model encodes risks, assets, people, reviews and mitigation plans; algorithms identify crown assets and compute attacker blast radius paths by summing weighted edges. Vector embeddings and similarity search collapse duplicate risks; a severity prediction model flags mismatched scores where mitigation wasn’t recorded. Proof‑of‑concept runs end‑to‑end and surfaces new, actionable questions for prioritising mitigations.
AI-written summary · about
chapters (9)
- 0:00Introduction to project
- 2:57Project background and goals
- 7:13Data modeling principles
- 10:19Graph model walkthrough
- 13:46Data analytics and insights
- 18:36Applications of graph model
- 22:29Business impact and future work
- 24:56Implementation approach
- 26:40Next steps and conclusion
related talks
31mSix Degrees of Infiltration
29mCartography: using graphs to improve and scale security decision-making
16mGraph-Powered Compliance: Building Intelligent Regulatory Mapping with AI and Neo4j
20mAttack of the Graph: Visual Tools for Cyber Analysis
39mUsing GraphDB Technology to Resolve Transitive Vulnerabilities at Scale
31mTransforming Vulnerability Management with Advanced Dependency Knowledge Graphs
35mGraphRag in Action: Revolutionizing Fraud Detection Techniques
48mHunting Stealth Adversaries with Graphs & AI