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Explores AI-driven development, rising supply chain attacks, and new regulatory requirements.

2026 Artifact Management Report

Figuring out a scalable way to secure AI continues to be a significant challenge for businesses everywhere. According to our 

 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.

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 

 feel very confident in the security of AI-generated code.

The problem: Manual validation is scaling poorly

 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 “

The solution: Automated quarantine and policy guardrails

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

 Instead of developers or AI agents pulling directly from public registries, everything flows through Cloudsmith.

 You can define rules (using OPA/Rego) that govern what you allow into your environment and what to block.

 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.

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.

Our findings show significant fragmentation in how companies handle AI models. Only 

 unify their model management with their standard artifact workflows. Meanwhile, 

 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.

 By using tools like Cosign within Cloudsmith, you can 

 your models. This ensures that the model running in production hasn't been tampered with and originated from your trusted pipeline.

 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

 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.

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.

This post only scratches the surface of the shifts we’re seeing in the industry. Download the complete 

 to explore the full demographic breakdown, competitive findings, and our deep dive into the state of software supply chain security.

Jason Myers

Technical Content Marketer

The AI speed trap: Securing the future of software supply chains

In the current landscape of software development, the primary threat to the supply chain isn’t necessarily a lack of information – it’s a lack of action. Our 

 reveals a growing "enforcement gap": the dangerous interval between identifying a vulnerability and successfully applying a security policy.

While organizations are getting better at generating security data, they are struggling to operationalize it. Visibility without automated enforcement is a front-row seat to disaster.

The manual audit trap: Visibility without velocity

 they are vulnerable, but they struggle to tell you 

 is affected. While our report shows that identification speeds are improving, legacy infrastructure and manual processes hamper teams’ ability to act on that information.

One of the first questions after discovering a malicious artifact may be: “Who downloaded this and where did it go?”

The answer for many teams is far from instantaneous. Our survey found that 

 find it difficult to identify exactly which users or systems have accessed a specific compromised artifact, often requiring manual deep-dives into logs and system data for answers.

This manual toil is more than an inconvenience; it is a security risk. In a world of automated, AI-driven attacks, relying on human-speed investigations is a losing strategy.

Fragmented scanning: The “CVE-only” blind spot

Another major challenge contributing to the enforcement gap is toolchain fragmentation. Many organizations treat different types of threats as separate problems, using separate tools that don't talk to each other.

Our data indicates that while CVE scanning is becoming standard, many organizations are still missing logic-based threats and malicious package injections. In fact, 

 have a fully automated process for verifying the chain of custody or cryptographic origin of their artifacts.

When your security stack is a patchwork of third-party scanners and manual spreadsheets, deny-by-default governance is impossible to maintain.

The 24-hour countdown: Why the CRA changes everything

The enforcement gap is no longer just a security concern, it’s becoming a legal liability. The 

 introduces strict reporting mandates for any company selling digital products in the EU. Reporting requirements for the CRA begin in September 2026.

The notification windows for CRA compliance are narrow, requiring an “early warning” within just 24 hours of discovering an active vulnerability. If your team relies on manual effort to quarantine and remediate threats, meeting these legal timelines becomes a statistical improbability. To remain compliant, organizations must move away from static, compliance-only SBOMs and toward real-time, automated gatekeeping.

Cloudsmith was built to eliminate the friction between identifying a threat and stopping it. By providing a unified, cloud-native control plane for all your artifacts, we help you transition from reactive remediation to proactive governance.

 Don't wait for a scan to finish to decide on a package. Cloudsmith lets you write policies that can automatically quarantine any new dependency at the point of ingestion, ensuring nothing reaches your developers until it clears your specific security hurdles.

 We consolidate CVE scanning, malicious package detection, and license compliance into a single source of truth. No more parsing server logs; you get granular audit trails that show exactly who, when, and where every artifact was accessed.

 Cloudsmith automates the verification of digital signatures and maintains a cryptographically secure chain of custody for every binary. This ensures that what you build is what you deploy.

The velocity of modern software development demands infrastructure that can scale security as fast as it scales code. By automating the enforcement of your security policies, you don't just close the gap; you build a more resilient foundation for everything your team creates.

Get all the data on software supply chain security.

This post highlights a few of the challenges facing modern DevOps and Security teams. Download the 

, which contains data and analysis on the state of software supply chain security.

Closing the enforcement gap: Why visibility isn’t enough for supply chain security

In a modern development environment, performance and security are inextricably linked. Our 

 reveals that software development in the age of AI is sitting on a fragile foundation of complex infrastructure and institutional inertia.

As teams become more distributed and the volume of artifacts, from standard containers to multi-gigabyte AI models, continues to grow, the “performance gap” between central hubs and remote regions is becoming a critical failure point for the global enterprise.

Maintaining artifact management systems isn't just a background task; for many, it’s a significant drain on high-value engineering resources. When your infrastructure is a legacy, self-hosted system, scaling is rarely a “set it and forget it” operation.

Our data shows that on-prem solutions remain common in otherwise cloud-centric industries. A significant portion of the industry remains stuck in a cycle of reactive maintenance – patching, upgrading, and manually provisioning storage just to keep pipelines moving.

 are now losing more than 20 hours every month to basic registry upkeep. This is time that could be spent on strategic product initiatives, rather than managing the plumbing of the software supply chain. When your infrastructure requires manual intervention to survive a release cycle, your operational overhead stifles your deployment speed.

For global organizations, the distance between a developer and their artifact registry translates directly into lost productivity. Legacy systems designed for a single data center struggle to provide parity for engineers in remote regions, leading to a two-tier developer experience.

A slow download or a registry timeout isn't just an annoyance; it's a blocker that causes CI/CD pipelines to fail. For teams distributed across continents, the performance problems at the edge result in inconsistent build times and frequent disruptions. Our report explores the depth of this performance imbalance and why centralizing your infrastructure in a single data center is no longer a viable strategy for the global stage.

Cloudsmith was built to solve the performance limitations of localized registries. By utilizing a genuinely cloud-native architecture, we move your artifacts closer to the people and machines that need them, regardless of where they are in the world.

 The user should not be concerned with provisioning storage or compute. Cloudsmith scales automatically with your usage, whether you are pushing a small patch or a massive AI model.

 By utilizing a dedicated Package Delivery Network (PDN), Cloudsmith ensures that a developer in Tokyo gets the same sub-second fetch speeds as a developer in London.

 Cloudsmith offloads your operational burden, ensuring that builds and releases remain available even during usage spikes or third-party outages.

As organizations start to deal with the increasing demands placed on development pipelines by AI, they require managed, high-performance solutions to keep up. Our data shows that 

 are already planning to migrate to managed cloud solutions in the next 12 months, while 

 want to migrate but are currently blocked..

Consolidating your infrastructure into a single, cloud-native source of truth isn’t just about speed; it’s about ensuring your delivery pipeline is resilient enough to handle the massive scale of AI-related development.

This post highlights just one part of the performance challenges facing modern DevOps teams. For a deep dive into regional latency issues, the impact of outages on release cycles, and more data on the true cost of legacy tech debt, 

Cloudsmith Blog

The AI speed trap: Securing the future of software supply chains

Closing the enforcement gap: Why visibility isn’t enough for supply chain security

Performance matters: How infrastructure impacts CI/CD