Design a Graph-Based Risk Scoring Engine
Last updated: December 9, 2025
Quick Overview
Design a system that computes dynamic risk scores for cloud resources by analyzing their relationships, exposure levels, and connected vulnerabilities in a security graph.
Wiz
December 9, 202515
11
1,856 solved
Design a system that computes dynamic risk scores for cloud resources by analyzing their relationships, exposure levels, and connected vulnerabilities in a security graph.
Risk scoring at Wiz is fundamentally a graph problem. A public-facing VM with a critical vulnerability is risky, but a public-facing VM with a critical vulnerability that has a network path to a database containing PII data is catastrophically risky. The system must score risk based on the full graph context, not just individual resource attributes.
What the Interviewer Expects
- Model cloud resources and their relationships as a graph with typed edges
- Design a scoring algorithm that considers path-based risk amplification
- Handle incremental score updates when the graph changes
- Address scale: millions of nodes, billions of edges, scores updated in near real-time
- Discuss how to make risk scores explainable to security teams
Key Topics to Cover
How to Approach This
- Start by clarifying functional and non-functional requirements with the interviewer.
- Estimate the scale: QPS, storage, bandwidth. This drives your design decisions.
- Draw a high-level architecture first, then deep dive into 1-2 critical components.
- Discuss trade-offs explicitly (e.g., consistency vs availability, SQL vs NoSQL).
- Address failure scenarios, monitoring, and how the system handles 10x traffic spikes.
Possible Follow-up Questions
- How would you handle a graph update that affects risk scores for thousands of downstream resources?
- How do you make the scoring algorithm tunable by customers without re-engineering?
- What graph database would you choose and why?
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Practice System Design ProblemsSample Answer
Requirements
- Functional Requirements:
- Model cloud resources (VMs, databases, etc.) and their relationships (network paths, security groups) as a directed graph with typed edges.
- Compute dynamic...
Capacity Estimation
- Expected Scale:
- Assume 10 million cloud resources (nodes) at Wiz, including VMs, databases, and storage accounts.
- Each resource can have an average of 100 relationships (edges).
...
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