What makes up the AI supply chain?
The AI supply chain describes everything that turns raw data into a live, production AI system. It starts long before a model is deployed and continues through training, packaging, and inference. Each stage depends on the integrity of the one before it, which is why weaknesses can cascade across the entire system.
At the foundation is the data layer. This includes the datasets used to train and fine-tune models, the pipelines that move data between systems, and the labeling or enrichment services that shape how models learn. These datasets often contain sensitive or proprietary information, making them a frequent target for tampering or exfiltration.
Next is the development layer, where models are designed and trained. This includes model architectures, training frameworks like PyTorch or TensorFlow, experiment tracking systems, and the environments where data scientists work. Security issues here don’t always look like traditional vulnerabilities—misused credentials, unsafe serialization, or untrusted dependencies can introduce risk before a model ever reaches production.
The model artifact layer manages the outputs of training. Trained models, weights, configuration files, and metadata are stored in registries and versioning systems so they can be reviewed, approved, and deployed. If these artifacts aren’t protected with signing, integrity checks, and access controls, attackers can replace or modify models without obvious signs of compromise.
The deployment and inference layer is where models are packaged and exposed. This includes container images, serving runtimes, orchestration platforms like Kubernetes, API gateways, and inference endpoints. Controls such as authentication, authorization, rate limiting, and encrypted transport all determine who can call a model and what it can access once it’s running. Visibility across endpoints, identities, and network paths is critical here, because exposure is rarely obvious from configuration alone.
Finally, most AI systems depend on third-party components. These include pre-trained models and weights from public hubs, open-source libraries, tokenizers, configuration files, commercial AI APIs, vector databases, and managed AI services. Because these components are deeply integrated into training and inference workflows, a compromise upstream can silently propagate into production.
What makes the AI supply chain especially challenging is how tightly these layers are connected. A poisoned dataset can corrupt a model, a tampered model can leak data during inference, and an over-permissive deployment can expose everything downstream. Securing the AI supply chain means understanding these dependencies – not just protecting each component in isolation.
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Why AI Supply Chain Security Is Getting Harder to Ignore
AI supply chain security is getting attention because AI is being built and deployed in ways most organizations haven’t dealt with before. Teams are pulling in models, data, and tooling from many places, wiring them together quickly, and exposing them through APIs – often without a clear picture of what they’re trusting along the way.
A few years ago, most AI lived in research environments. Today, those same models are running in production, connected to customer data, internal systems, and automated decision-making. That shift changes the stakes. A compromised model isn’t just a technical issue – it can directly affect users, outcomes, and business operations.
The way AI is assembled also makes supply chain risk easier to miss. Modern AI systems depend on pre-trained models, open-source libraries, training datasets, vector databases, and managed cloud services. Much of this arrives as a black box. Teams may know what they’re using, but not where it came from, how it was trained, or what else it can access once deployed.
Exposure has increased as well. Models are now commonly accessed through inference endpoints, chat interfaces, and APIs. That means attackers don’t need source code or file access to interact with AI systems – they can probe them directly. In some cases, that’s enough to extract behavior, infer training data, or trigger unsafe actions.
What makes this harder is that problems don’t always look like failures. A poisoned dataset or backdoored model can behave normally most of the time. Issues may only appear under specific inputs or conditions, which makes them easy to miss during testing and hard to diagnose after deployment.
Finally, expectations are changing. Regulators, customers, and partners increasingly want clear answers about where AI systems came from and how they’re controlled. Questions about provenance, integrity, and accountability are no longer theoretical – they’re showing up in audits, procurement reviews, and customer conversations.
Put simply, AI supply chain security matters more now because AI systems are more connected, more exposed, and more consequential than they were even a short time ago. As organizations rely on AI to do real work, understanding and securing how it’s built becomes unavoidable.
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How AI supply chain attacks can occur
Most AI supply chain attacks don’t begin with a dramatic breach. They start with small compromises in trusted components—training environments, dependencies, or model artifacts – that quietly move downstream into production.
One common entry point is the training and runtime environment itself. AI workloads often rely on highly privileged GPU drivers, container runtimes, and low-level libraries. Wiz Research has shown how vulnerabilities in AI-related infrastructure can expose entire environments, allowing attackers to escalate privileges or access sensitive workloads running on shared systems
Another frequent attack path runs through open-source AI libraries and repositories. Many AI teams depend on popular frameworks and tooling pulled directly from GitHub or package registries. In real-world incidents investigated by Wiz Research, attackers compromised widely used AI libraries and injected malicious code that performed cryptomining once deployed—spreading silently into training and inference environments
Model artifacts and serialized files are another high-risk area. Trained models are often treated as static assets, but unsafe serialization formats can execute code when models are loaded. Wiz Research has documented supply chain attacks where malicious payloads were embedded into trusted artifacts, turning the model loading process itself into an execution vector
Supply chain risk also appears through third-party integrations and dependencies. AI pipelines frequently pull in external models, weights, tokenizers, and configuration files. When attackers compromise an upstream dependency, that malicious code can propagate into AI workflows without exploiting traditional application vulnerabilities
Finally, exposed inference endpoints give attackers direct interaction with models. Even without access to infrastructure or source code, adversaries can probe models through repeated queries, enabling extraction of model behavior or inference of training data. These attacks blend easily into normal traffic, making them difficult to detect without contextual monitoring.
What makes these attacks especially dangerous is how well they hide. A compromised dependency still looks like a legitimate library. A poisoned model behaves normally most of the time. A vulnerable runtime component operates as expected until it’s exploited. Attackers don’t need to compromise the entire AI pipeline – just one trusted link and the supply chain does the rest.
Challenges securing the AI supply chain
One of the biggest challenges in securing the AI supply chain is simply knowing what exists. AI assets are often created outside of traditional software workflows – by data science teams, experimentation notebooks, or ad-hoc pipelines. Models, datasets, and endpoints can appear quickly across cloud accounts without ever passing through centralized security review, leading to shadow AI that’s difficult to inventory or govern.
Even when assets are known, dependency complexity makes risk hard to reason about. An AI system may rely on specific versions of open-source libraries, GPU drivers, training frameworks, pre-trained weights, and external data sources. A weakness in any layer – code, data, or infrastructure – can undermine the entire system, yet these dependencies are rarely tracked together in one place.
Ephemeral training environments add another layer of difficulty. Large training jobs may spin up thousands of GPU-backed instances for a short period of time and then disappear. Traditional security tools that rely on long-running agents or manual review often miss these environments entirely, leaving gaps in visibility during some of the most sensitive stages of the AI lifecycle.
Validating third-party models and artifacts is also challenging. Public model hubs and repositories make it easy to reuse existing work, but they provide limited insight into how models were trained, what data was used, or whether artifacts have been altered. Without strong provenance and integrity controls, teams are forced to trust opaque components in critical systems.
Detection is complicated by the probabilistic nature of AI behavior. A compromised model may continue to produce plausible outputs, making it difficult to distinguish between natural drift, performance issues, and intentional manipulation. Unlike traditional software, there is often no single “correct” output to validate against.
Organizational friction plays a role as well. AI systems cut across data science, engineering, security, and platform teams, each with different priorities and tooling. Without shared visibility and clear ownership, supply chain risks can fall into the gaps between teams rather than being addressed end to end.
Finally, securing AI supply chains at scale is harder in multi-cloud environments. AI workloads are often spread across providers to access specific services or hardware, making it difficult to enforce consistent policies and controls. Without a unified view, teams may secure individual environments while missing cross-cloud attack paths.
How AI supply chain security fits into cloud security
AI supply chain security isn’t a separate discipline from cloud security – but it also isn’t limited to the cloud alone. While some organizations run on-prem training clusters, edge inference, or hybrid deployments to control cost, latency, or data residency, cloud infrastructure is where most modern AI supply chains ultimately intersect.
Even when models are trained or deployed outside the cloud, critical parts of the workflow often pass through it. Training data may be staged in cloud storage, models pulled from cloud-hosted registries, pipelines orchestrated through managed services, or inference systems authenticated against cloud identities. As a result, AI supply chain risk frequently shows up through cloud permissions, network exposure, and data access paths.
At the same time, not all AI supply chain risks are cloud-native. Compromised open-source model weights, poisoned third-party datasets, or vulnerabilities in ML frameworks can introduce risk before cloud infrastructure is involved at all. These issues originate earlier in the lifecycle and can follow models wherever they run.
The cloud becomes critical because it is where these risks converge and compound. A tampered model is most dangerous when deployed behind a public endpoint. A poisoned dataset has the greatest impact when it retrains models that interact with sensitive production data. A vulnerable ML framework becomes a serious incident when it runs under an over-privileged cloud identity.
This is why AI supply chain security intersects so closely with core cloud security practices. Cloud posture management governs how training data and model artifacts are stored. Identity and access management controls who and what can interact with models and pipelines. Data security protects sensitive datasets, embeddings, and inference logs. Vulnerability management secures the containers, images, and dependencies used to train and serve models.
What changes with AI is how tightly coupled these layers are. A secure cloud environment can still be undermined by an unsafe model artifact. A high-quality model can become a liability if it runs under excessive permissions. AI supply chain risk rarely appears as a single failure – it emerges from the interaction between data, models, dependencies, identities, and exposure paths.
For most organizations, effective AI supply chain security means extending existing cloud security workflows to account for AI-specific assets and dependencies, while still recognizing risks that originate outside the cloud. Continuous visibility into how models, data, infrastructure, and identities connect is what allows teams to catch supply chain risk early – before it propagates into production systems and business-critical decisions.
How Wiz helps secure the AI supply chain
Wiz helps organizations secure the AI security posture across the AI supply chain by making AI assets, infrastructure, and data exposure visible within the same platform they already use to manage cloud risk. Wiz doesn't replace model governance, fairness reviews, or regulatory conformity assessments – but it closes a critical gap by showing how AI systems can actually be exposed, abused, or exploited in real cloud environments.
Using an agentless approach, Wiz continuously discovers AI services, training environments, model endpoints, storage locations, and supporting infrastructure across AWS, Azure, and GCP. This includes managed platforms such as Amazon SageMaker and Bedrock, Azure OpenAI Service and Azure Machine Learning, Google Vertex AI, as well as custom AI workloads running on Kubernetes or virtual machines. This discovery effectively creates an AI Bill of Materials (AI BOM), helping teams inventory models, datasets, pipelines, identities, and services – and surface shadow AI that falls outside standard controls.
Wiz correlates these AI assets with cloud context using its Security Graph, connecting identities, network exposure, vulnerabilities, and sensitive data. Instead of flagging individual misconfigurations, Wiz reveals attack paths – the combinations of exposure, permissions, and data access that turn small issues into real supply chain risk. This allows teams to see which AI components could be abused to exfiltrate data, move laterally, or enable unauthorized access to AI-powered systems.
Wiz also supports AI supply chain security across the lifecycle. Code-to-cloud visibility links exposed AI services back to the pipelines or infrastructure-as-code that introduced them, enabling durable fixes. At runtime, Wiz Defend monitors AI infrastructure for abuse patterns such as misuse of service identities, data exfiltration, or cryptomining focusing on how AI expands the cloud attack surface, not on judging model outputs.
Request a demo to see how Wiz helps security teams identify and reduce AI supply chain risk across models, data, and cloud infrastructure.
Accelerate AI Innovation, Securely
Learn why CISOs at the fastest growing companies choose Wiz to secure their organization's AI infrastructure.