Identifying and Securing AI Workloads

Learn why AI workloads demand a new approach to cloud security

What happens when the most innovative part of your cloud environment also becomes the most vulnerable? If the cloud is the operating system for today’s digital world, AI has become its freestyle mode: flexible, creative and incredibly powerful, yet dangerously easy to break if you are not careful.

The same freedom that accelerates AI innovation also introduces significant risk. According to Tenable Cloud Research, 70% of cloud workloads that utilize AI services contain at least one misconfiguration or critical vulnerability. Think about that for a moment.

AI pipelines are not deployed by security teams. They are created and managed by developers, data scientists and ML engineers. In the process, these teams become the accidental administrators of entire AI ecosystems. They spin up GPU instances, attach privileged service accounts, connect data stores and configure identity permissions that often go unreviewed. In many cases, the people building your AI end up holding the keys to the kingdom, even if they never intended to.

Inside training pipelines, managed notebooks, object stores, model registries, vector databases and the identities that tie them together, hidden weaknesses accumulate. These weaknesses quietly create one of the fastest growing and least understood attack surfaces in the enterprise. Even the simplest configuration choices — such as whether a notebook has direct internet access or whether model storage is encrypted — can unintentionally open the door to exposure.

Security leaders are no longer asking: “Do we use AI in the cloud?” That question is already behind us. They are wrestling with a far more difficult reality:”How do we secure what we cannot see, cannot classify or did not even know we deployed?”

The issue is generally not carelessness. The issue is opacity. Cloud AI creates a mirage of simplicity. The surface looks calm, streamlined and beautifully abstracted, but behind that abstraction are deep layers of automation, inherited defaults, identity sprawl and unseen privilege that make it difficult to detect exposure forming in real time. These layers mask the toxic combinations of risk. A model store with public access may seem harmless until it intersects with an overprivileged service account or sensitive training data stored without encryption. It is the intersection of these issues, not any one of them on its own, that creates real exposure. Traditional scanning and policy checks were never designed for systems that behave this way. The outcome is unavoidable. AI is moving faster than the security controls built to protect it, and the gap between innovation and protection is widening every day.

What good security looks like: Closing the visibility gap

If security teams had the clarity and altitude of an eagle, the cloud would look very different. They would see AI workloads that appear and disappear in minutes, identities that quietly accumulate permissions, model artifacts shifting across storage layers and sensitive data flowing into services no one remembers configuring. They would also see the full lineage of how models are trained, where outputs are written and which cloud services interact behind the scenes. A view that reveals risks most teams never realize are present.

From that vantage point, the real risks are unmistakable. Without it, they remain hidden inside the abstraction that makes cloud AI feel simple.

Closing the gap between AI innovation and AI security begins with this kind of visibility. As models are trained, tuned and redeployed, the environment shifts underfoot. New storage connections appear. Service accounts gain privileges. Managed AI services inherit defaults that no one ever reviews. Misconfigurations accumulate quietly until they form an exposure path.

To secure AI at the speed it evolves, organizations need to rethink what “good” looks like. Good means seeing the full picture of how AI pipelines are built and how they behave. It means understanding which identities can reach which data, which workloads introduce sensitive information, where model assets travel and which combinations of vulnerabilities, privilege and exposure actually matter. It means prioritizing risk through context, not through noise.

Most importantly, it means embracing a model of continuous assessment, validation and enforcement. AI environments shift too quickly for point-in-time scanning or static configuration checks. Security must match the pace of deployment, not the other way around.

This is the foundation for securing AI in the cloud; elevated visibility, contextual understanding and continuous validation. With this in place, organizations can finally move from reacting to AI exposures to preventing them.

From challenges to actionable practices

Now that we understand the pace and complexity of AI environments, the next step is establishing the practices that bring predictability and control.

Best practices for securing AI workloads in the cloud

Once organizations understand how fluid and interconnected AI workloads truly are, the next step is putting the right practices in place to protect them. Strong AI security is not about slowing innovation. It is about giving developers, ML or deep learning engineers along with security teams a shared foundation that reduces risk without disrupting velocity.

Here are the core practices that matter most.

1. Continuously discover every AI resource across your cloud

AI workloads are scattered across services, storage, pipelines, registries, APIs and managed services. They also appear and vanish quickly. The first best practice is continuous discovery of every component in the AI lifecycle. This includes training pipelines, model repositories, AI training and inference compute services, vector databases, inference endpoints and the data stores that feed them. It also means tracking how these services connect, where models flow and which cloud services they depend on.

Visibility is not a one-time activity. AI security begins when discovery becomes continuous.

2. Classify the data that flows into and out of AI systems

AI workloads are only as safe as the data they use. Organizations need automated, granular classification that identifies sensitive training data, regulated information, proprietary IP and production datasets before they enter model pipelines. Identifying sensitive training data is the first step in discovering and securing the full AI workload and its deployment risks. It prevents accidental exposure, data leakage and unintentional model training on high-risk information. Strong AI security also benefits from visibility into examples of the data detected, not just high-level labels.

If you cannot see your data, you cannot control how it shapes your models or how it exposes your business.

3. Understand identity and privilege relationships within AI workflows

AI services rely on service accounts, tokens and entitlements that quickly grow beyond their original purpose. Every GPU job, notebook, pipeline or scheduled task introduces new permissions. Security teams must understand who and what has access to each AI resource and which privileges are inherited silently from elsewhere. This visibility includes not only access to models but also the training and production data that flow through these systems, as well as the identities your AI workloads themselves rely on.

Strong AI security is grounded in identity hygiene. Weak identity controls create the fastest path to model compromise or data theft.

4. Prioritize AI risks through context, not volume

AI workloads generate large amounts of noise. Not every vulnerability or misconfiguration is equally important. The real danger comes when exposures intersect: sensitive data combined with overprivileged roles, or vulnerable workloads accessible from public networks. Contextual prioritization recognizes that vulnerabilities impacting AI frameworks or tooling must be evaluated through the lens of who can exploit them and how they connect to critical assets.

Organizations need prioritization that reflects real attack paths, not static lists of issues. Security improves when teams know which exposures matter and why.

5. Shift from static checks to continuous validation

AI environments evolve at a speed that legacy security tools cannot match. New models, new datasets and new pipeline steps can introduce exposure overnight. Organizations must adopt continuous validation that monitors posture changes, enforces guardrails and ensures that risky conditions do not return after remediation. This includes monitoring for misconfigurations like public model storage, missing encryption or notebooks with direct internet access.

Static security creates blind spots. Continuous validation and controls, closes them.

6. Implement guardrails that prevent risk before it is created

Good security does not slow AI innovation. It accelerates it by reducing rework. Organizations should enforce least privilege, data protection and configuration guardrails as part of the AI development and deployment process. This includes preventing public exposure of model assets, restricting sensitive data from entering training pipelines, and blocking entitlements that grant unnecessary access to model registries and vector databases and even specifying where sensitive data can and cannot reside.

To implement these access control guardrails effectively, the safest approach is to limit elevated permissions to the exact moment they are needed. Short-lived, on-demand access helps ensure AI teams can move quickly without leaving long-standing privileges behind in the environment.

Best practices outline the destination, but only matter when they can be put into practice. AI environments move too quickly for theory alone. Organizations need a way to turn these principles into something real. This is exactly where Tenable delivers: the visibility, context, continuous validation and control that transform best practices from talking points into day-to-day reality.

How Tenable Cloud Security secures AI workloads across the cloud

Tenable delivers the clarity that modern AI environments demand. AI workloads span compute, storage, identity, data and managed services and every part of that ecosystem plays a role in shaping risk. Exposure management is the strategy that enables organizations to see, prioritize and fix these risks across interconnected cloud assets. The Tenable Cloud Security CNAPP helps you implement this strategy by unifying cloud configurations, identity pathways and data sensitivity into a single contextual understanding of where exposures truly matter.

Cloud and AI have rewritten the learning curve overnight. Cloud-native AI services, such as Amazon Bedrock foundation models and agents, Azure AI Studio agents, Azure OpenAI models and Google Vertex AI endpoints, can be instantly adopted . They also introduce new forms of risk that most teams are only beginning to understand.

Modern AI deployment is increasingly shaped by these managed services. Tenable discovers and monitors the cloud resources supporting them, evaluates their configurations, and maps identities and permissions that interact with them. This helps organizations secure the AI they consume just as effectively as the AI they create.

It starts with visibility. Tenable inventories the cloud services involved in AI workloads across AWS, Azure, GCP and Oracle Cloud. This includes compute environments, storage services, networking layers, identities, APIs and access controls. It also identifies where sensitive data lives within these environments and which identities or services can reach it, including when AI resources have access.

From there, Tenable adds context. AI workloads rarely become dangerous because of a single issue. Risk materializes when misconfigurations, sensitive data and excessive privilege collide across cloud services. Tenable correlates these signals and surfaces the exposures most likely to result in data leakage, unintended access or privilege misuse. For instance, it can uncover when a managed AI model has access to unencrypted training data.

This context allows organizations to focus on what matters instead of reacting to noise. Tenable’s Vulnerability Priority Rating (VPR) applies real time threat intelligence to determine which issues represent genuine exploitability and which are unlikely to be targeted.

Data exposure is often where AI risk becomes tangible. Tenable’s data security posture capabilities connect sensitive datasets to the AI services interacting with them. One example is the built-in analysis that detects AWS Bedrock custom models trained on sensitive data, delivering actionable insight rather than abstract alerts.

Finally, Tenable provides continuous validation. Cloud resources underpinning AI workloads change constantly as new services are deployed, permissions evolve and fresh data flows into the environment. Tenable monitors these changes in real time, applying policy guardrails that enforce least privilege, protect sensitive data and prevent risky conditions before they enter production.

Together, these capabilities create an environment where AI innovation can move quickly without leaving security behind. Organizations gain visibility into the cloud services supporting AI workloads, confidence in where risk accumulates and assurance that their posture remains protected even as the environment shifts over time.

See it in action

The short demo below walks you through a real cloud AI environment and shows how Tenable identifies workloads, reveals hidden risks and highlights the issues that matter most.

It is a quick, straightforward look at what AI security visibility actually feels like in practice.

Click here to get more information about identifying and securing AI workloads.