Secure Chain FAQs

Question 1

What role does vulnerability data play in Secure Chain?

Accepted Answer

Secure Chain automates the seeding of containerized databases with vulnerability data. This ensures that LLMs and AI agents have up-to-date information to identify, assess, and mitigate security risks associated with software supply chain components.

Question 2

What is Secure Chain and what problem does it solve?

Accepted Answer

Secure Chain provides critical context about software supply chains to large language models (LLMs) and AI agents. It addresses the need for these AI systems to understand the complex dependencies and potential risks within software components, enhancing security and monitoring.

Question 3

What are the primary deployment requirements for Secure Chain?

Accepted Answer

Deploying Secure Chain primarily requires Docker and Docker Compose for container orchestration. It also utilizes Neo4J for graph visualization and Python 3.13+. Data dumps for graphs and vulnerabilities are downloaded from Zenodo to seed the databases.

Question 4

How does Secure Chain monitor and visualize supply chain data?

Accepted Answer

Secure Chain actively monitors and checks the status of your software supply chain. It visualizes this complex data using a Neo4J graph database, offering clear, interconnected insights into components, dependencies, and potential vulnerabilities.

Question 5

Can Secure Chain integrate with existing AI assistants and development workflows?

Accepted Answer

Yes, Secure Chain is highly configurable for seamless integration with AI assistants like Copilot, particularly via VSCode. This allows developers and AI agents to access relevant supply chain context directly within their development environment.

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