AI coding agents, external models, and MCP servers now pull in dependencies and change code throughout the day. This guide explains what that means for your software supply chain and how to keep track of it.

Why this matters now

Most teams have moved past asking whether to use AI in development. The harder question is whether they can trust what it adds to production.

Many existing security tools weren’t built to inspect AI models or agent plugins, so some of what enters your codebase goes unchecked.

Four problems the guide helps you address:

• Unknown AI assets
  Developers and agents often pull models and packages from public hubs without going through your central registry. The guide covers how to see what’s actually in use.
  
  
• Models that scanners can’t read
  Traditional tools can’t inspect model weights or agent plugins, which makes them a possible route for malicious code. The guide covers how to bring these assets under review.
  
  
• Checks that happen too late
  Quarterly audits and late-stage pipeline scans miss changes that agents make every hour. The guide covers how to check continuously instead.
  
  
• Missing audit evidence
  Without traceability, provenance, and access records, compliance is hard to demonstrate. The guide covers how to build that record.
  
  If your teams are using AI to write or manage code, this is a practical starting point for keeping that work visible and verifiable.

Download the guide