7 Cloud Security Tips Businesses Can Implement Before Scaling AI Projects

Scaling an AI project signals intent. It means a business is ready to move from experimentation to an infrastructure that others will depend on. But there is a sequence that organizations consistently get wrong: they scale the AI, and then they think about security.

Cloud environments that were adequate for a pilot project look very different when processing millions of inference requests, handling sensitive training data, connecting to production systems, and being accessed by expanded teams across multiple regions. The attack surface grows. The compliance obligations deepen. Retrofitting security after the fact is substantially more expensive than building it in beforehand, measured in engineering time, incident response effort, and disruption to live systems.

The steps businesses need to take before scaling an AI project are not particularly exotic. They apply established cloud security fundamentals to the specific characteristics of AI workloads. Seven of the most consequential are covered below.

1. Audit your cloud environment before you expand it

After you increase your cloud’s footprint by 2X, the first time you discover either a misconfigured storage bucket or an over-permissioned service account will be the best time for you personally to go through the pain of performing a security audit of your current environment. Before scaling any AI workload, ensure you have performed sufficient due diligence on your cloud: who has access, what resources exist, how they are configured, and where you deviate from security best practices.

You should be aware that cloud environments have the potential to “drift” significantly over time. For instance, a resource that was properly configured six months ago could have had its configuration changed since then without your knowledge. In addition, because teams that continue to develop AI applications typically value speed and meeting deadlines, they will occasionally fail to manage account and resource access and configuration until something goes wrong.

Having a structured pre-scaling audit provides an organization with the opportunity to establish a defined baseline. Establishing this defined baseline allows organizations to take ownership of their responsibility to secure the environment as it continues to grow. Failing to establish either this defined baseline or to secure your environment at the time that you built your baseline will result in expanding into a previously unknown security threat landscape.

2. Apply least-privilege access to every AI workload

AI systems require substantial data access, including datasets for training, databases for inference, and storage for model management. Granting broad access permissions for quick testing may seem convenient, but these permissions are rarely restricted later.

As AI workloads expand, regularly review all associated identities, service accounts, API access, and IAM roles. Limit permissions to only those necessary for each task, and apply this principle consistently across training infrastructure, model endpoints, data pipelines, and management accounts.

3. Classify and manage the information your AI systems touch

AI is inherently data-driven, and the amount of data flowing through an AI pipeline is generally much higher than the team initially anticipates. Customer records from training a recommendation engine, transaction histories used to feed a fraud detection application, and communication between employees analyzed for productivity applications all have privacy obligations, compliance issues, and data-handling requirements that must be in place before they can be put into production.

Data classification, the tagging of data according to sensitivity, with the application of access and data control measures accordingly, is a foundational requirement. Without data classification, you cannot accurately determine that sensitive data is used appropriately, flows only to approved systems for processing, or is retained/ destroyed in compliance with regulations.

For businesses subject to regulations such as GDPR and HIPAA, this is not negotiable. Even for businesses operating in less prescriptive regulatory environments, implementation of ungoverned AI training data creates long-term liability that generally emerges at inopportune times.

4. Incorporate AI-driven security operations prior to scaling threats

As workloads increase, the amount of work produced by AI workloads will grow as well. The volume of work generated by AI instances (API calls, data access events, configuration changes, authentication events) will scale in lockstep with the growth of AI workloads. Therefore, those monitoring solutions that may work for pilot-scale monitoring will not be capable of monitoring production AI workloads processing thousands of requests per second.

This is where AI-powered SecOps becomes critically important. Traditional security detection technologies struggle to handle the large, varied signals generated by large AI infrastructures. AI-based Security Operations employ machine learning to establish a baseline of normal behavior, identify abnormal behavior relative to it, and prioritize alert responses based on contextual risk. Therefore, security personnel can focus on real threats rather than process large overall alert volumes.

Having this capability in place prior to scaling gives your security operation a significant advantage. You will have already established what is ‘standard’ before the operational environment becomes so complex as to make it difficult to differentiate between normal and abnormal at scale.

Why does the timing matter?

Establishing a behavioral baseline for an environment before scaling it is much easier than doing so retrospectively. The volume of false alerts generated during post-implementation development of the operational environment will be much greater than when establishing the baseline for a smaller operational environment and developing its operating patterns.

5. Safeguard your AI supply chain

Many modern AI initiatives employ a variety of resources rather than developing everything from the ground up. These resources may include third-party libraries, open-source templates/models, pre-trained foundation models, and commercially available models, APIs, and other reference code. All represent unique dependencies that increase the number of vulnerabilities within your organization’s security perimeter beyond your company’s infrastructure alone.

For example, an unpatched vulnerability found within a machine learning library, compromised model repositories, or poorly secured third-party APIs can all serve as entry points into an organization’s environment. The risks in the AI software supply chain echo those that security teams have managed in the traditional SDLC. However, they are often more complex because many AI resources are still evolving and may not be updated or maintained as reliably as traditional software.

6. Implement network segmentation for AI Infrastructure

AI workloads, especially the training infrastructure, can be computationally intensive, and, given broad access to data, expose themselves to risk. In the case of a breach of an AI workload, the potential loss due to other resources being compromised is significant if proper resource segmentation is not implemented.

Implementing network segmentation will create physical barriers to limit lateral movement by malicious actors if malware is used to gain access to a resource in a production environment. There should be no connectivity between the AI training environments and the production systems.

Model serving infrastructure should have a different network address space than that used in internal development environments. Data pipelines should not be able to access all devices on the network. Instead, they should communicate only with the storage devices and services needed to fulfill the data needs of the data pipelines.

As AI projects scale, the number of components supporting those projects and the connections between them grow rapidly and are sometimes not tracked for their physical connections. Therefore, establishing network boundaries prior to scaling will enable better monitoring and enforcement of AI system boundaries as the systems evolve.

7. Create and test your incident response plan with AI-specific scenarios in mind

Copying another organization’s incident response plan won’t cover all the ways AI systems can fail. For example, you might see an AI model endpoint act in unexpected ways, or a training pipeline could be tampered with to add adversarial patterns to the model. 

There is also the risk of your training data being exfiltrated through the data pipeline. These situations need response procedures designed specifically for AI systems, since they work and fail differently from traditional application infrastructure.

Before you scale, figure out which scenarios are the biggest risks for your business and create response procedures for each one. Decide how you will detect problems, who will handle them, what steps are needed to contain the issue, and how you will confirm your AI system is fixed before putting it back into production.

After you have your procedures and test plan, run tabletop exercises to make sure your plan works and to spot any gaps before a real incident happens. Many teams only focus on incident response planning right before launch, thinking it is only needed if something goes wrong.

Companies that build a solid security strategy before scaling up usually handle incidents more effectively than those that do not. It is important to create your playbook before you face the pressure of a major scaling event.

Wrapping up

The structure of the seven-step process is that all seven steps are based on the same fundamental rule: implementing security will happen in cheaper, quicker, and less disruptive ways before scaling systems, rather than after. In addition, the cloud-based proof-of-concept AI project environment provides a much more forgiving security environment than the cloud-based end-user or customer AI production environment or their respective downstream services.

Organizations that are building lasting AI capabilities approach security as an enabler rather than a barrier to their development. By addressing the fundamentals of cloud security before scaling production AI capabilities, these companies lay the groundwork for the future sustainability of their capabilities.

To understand how AI impacts hybrid cloud security environments through the examples above, this section will also provide the necessary context for understanding the importance and relevance of the sequencing argument beyond comparing individual workloads.