Figuring out a scalable way to secure AI continues to be a significant challenge for businesses everywhere. According to our 2026 Artifact Management Report, 93% of organizations use AI to accelerate their software development cycles. We are building faster than ever, yet the infrastructure supporting this speed was largely designed for a pre-AI world.

As AI agents dictate the pace of production, they are also expanding software attack surfaces. Security practices that worked at human speed are buckling. At Cloudsmith, we see this as a two-sided challenge: securing the code AI writes (the output) and securing the models AI uses (the input).

Here is how the landscape is shifting and how a cloud-native approach to artifact management can provide the control necessary to future-proof your development.

The dependency explosion

AI agents prioritize functional results over security provenance. When an AI suggests a block of code, it can pull in new dependencies. When you factor in transitive dependencies, the situation gets even more complex. Our survey data shows that only 17% of organizations feel very confident in the security of AI-generated code.

The problem: Manual validation is scaling poorly

The survey reveals that over 50% of teams are spending between 11 and 40+ hours per month manually validating the dependencies introduced by AI. This “operational tax” chips away at the speed gains promised by AI adoption. Furthermore, AI is prone to hallucinating packages, suggesting names that don't exist, which attackers then register in public repositories to facilitate “slopsquatting” attacks.

The solution: Automated quarantine and policy guardrails

Cloudsmith addresses this by acting as a sophisticated gatekeeper between your AI agents and the public internet.

• Smart upstreams: Instead of developers or AI agents pulling directly from public registries, everything flows through Cloudsmith.
• Policy-as-code: You can define rules (using OPA/Rego) that govern what you allow into your environment and what to block.
• Package quarantine: Create policies that automatically quarantine packages that meet specific criteria. When an AI agent calls for a new, unknown package, you can inspect quarantined packages before they reach your development environment.

Model governance challenges

While AI generates code at scale, the models themselves don’t get the same scrutiny that regular binaries receive. Companies frequently store AI models in ad-hoc silos like local drives or unmanaged cloud buckets, detached from the standard software development life cycle.

The problem: Fragmented control

Our findings show significant fragmentation in how companies handle AI models. Only 12% of organizations unify their model management with their standard artifact workflows. Meanwhile, 41% of teams only perform basic integrity checks (like checksums), leaving them vulnerable to model tampering or malicious weight injections.

The solution: Models as first-class artifacts

To future-proof your AI initiatives, you must treat models exactly like other artifacts, like container images or npm packages.

• Provenance and signatures: By using tools like Cosign within Cloudsmith, you can digitally sign your models. This ensures that the model running in production hasn't been tampered with and originated from your trusted pipeline.
• Vulnerability insight: Cloudsmith scans the metadata and layers of these artifacts, ensuring that your “inputs” are as secure as your “outputs.”

Future-proofing: Building for machine scale

The shift to AI-driven development isn't a temporary trend; it’s a fundamental change in how we build software. Traditional artifact managers – often on-prem and slow to update – cannot provide the visibility required for this new development velocity.

Cloudsmith helps you move from a reactive “trust then verify” posture to an automated, proactive “verify then trust” one. By centralizing code, containers, and AI models in a single, cloud-native platform, you gain the visibility needed to comply with international compliance standards and frameworks, like the EU’s Cyber Resilience Act, while giving your developers the freedom to move at machine speed.

The bottom line

Security should be the foundation for AI. By implementing automated quarantine workflows and a unified registry for models, you can reclaim the hours lost to manual validation and protect your organization from the unique risks of the AI era.

Read the full report

This post only scratches the surface of the shifts we’re seeing in the industry. Download the complete 2026 Artifact Management Report to explore the full demographic breakdown, competitive findings, and our deep dive into the state of software supply chain security.