Securing non-deterministic systems: A practical guide for AI artifacts and LLMOps
In this practical guide, Cloudsmith breaks down the real security risks introduced by AI-generated code, machine learning models, and agentic workflows. It shows how centralized artifact management gives teams the control, visibility, and trust required to ship AI safely.
About this guide
AI systems are non-deterministic by design – but most security tooling still assumes static code, predictable builds, and trusted dependencies.
Today, developers are pulling auto-generated code, executable models, and orchestration logic from public ecosystems at machine speed. Hallucinated dependencies, unsafe model formats, and poisoned registries create supply chain risks that traditional DevOps and AppSec tools were never built to detect.
Securing Non-Deterministic Systems is a practical roadmap for security, platform, and engineering leaders navigating this new reality.
The guide explores the shift from DevOps to LLMOps and explains why we must treat AI artifacts – code, models, weights, and prompts – with the same rigor as production binaries. It outlines how centralizing AI artifacts inside a secure registry enables auditing, scanning, signing, and provenance across the entire AI development lifecycle.
What you'll learn:
- How AI-generated code and hallucinated dependencies introduce new supply chain risks - and how to prevent slopsquatting and poisoned artifacts from reaching production.
- The emerging AI threat landscape, from metadata poisoning and model steganography to exploitation of AI frameworks and orchestration layers.
- How LLMOps differs from traditional DevOps – and what this means for evaluation, monitoring, and operational security.
- What a secure AI development lifecycle looks like, and how centralized artifact management enables visibility, provenance, and trust across every AI component.
