For most of us, the cloud represents convenience. Today, companies can train, optimize, and deploy AI models without the need for their own data centers. However, the rising demand for data to train these models has raised serious concerns about data privacy, especially for sensitive industries like healthcare and the government sector. With that in mind, what are the risks of using the cloud when training your AI?
Companies that don’t prioritize data security are at risk of compromising user privacy and significant financial losses. Why? Consider three vulnerabilities that make the cloud riskier than on-premise infrastructure:
- Shared resources. In the cloud, multiple organizations use the same computing resources, creating a potential for data leakage or exposure. Even though cloud providers use robust security mechanisms, vulnerabilities still exist.
- Data transfers. Data that’s being moved from on-premise to the cloud, or vice versa, is vulnerable to interception, particularly when not adequately encrypted.
- Compliance risks: Certain industries are subject to strict regulations. For example, healthcare companies must be HIPAA (Health Insurance Portability and Accountability Act) compliant, and military organizations have strict rules regarding data handling. Using cloud services can inadvertently cause non-compliance issues, leading to heavy penalties.
While cloud infrastructure is prized for its convenience and scalability, on-premise solutions have emerged as a safer option when it comes to protecting sensitive data.
On-Premise AI Training: The Safe Haven
Rich data and comprehensive AI training are key to creating usable AI models. However, training AI requires data collection, data processing, model development, and validation — all of which can place your data at risk.
On-premise AI training gives you control over your AI infrastructure, allowing you to keep data isolated from external users. Because it offers better control over data and computational resources, it’s the preferred choice when dealing with sensitive data. Here are a few reasons why:
- Data security. On-premise solutions keep data within your organization’s network, eliminating the risk associated with data transfer over the Internet.
- Compliance. For industries with strict data protection regulations, on-premise training ensures that data handling adheres to those standards. It’s also easier to audit and control data access.
- Customization: On-premise solutions can be customized to meet the specific needs of an organization, enhancing efficiency.
Suppose a healthcare organization wants to develop and deploy a deep learning model to detect anomalies in medical images (e.g., X-rays, MRIs, CT scans). With on-premise hardware, they’d be able to optimize their network and data storage capabilities to handle medical images, which are large and data-intensive. To do so, they may deploy high-speed SSDs and optimize storage protocols (such as NFS or iSCSI) to quickly access and store large volumes of medical image data. Because the organization owns the infrastructure, they have the freedom to tailor it to their unique workloads.
On-premise solutions have their challenges. They require a significant upfront investment in hardware and may not scale as easily as cloud solutions. They may also require dedicated IT personnel to maintain the infrastructure and ensure security. With these added costs, it’s essential to evaluate your data’s sensitivity, security needs, and compliance requirements before deciding on your AI training environment.
In your analysis, you may conclude that the secure environment provided by on-premise infrastructure is non-negotiable. One solution is to leverage lab-as-a-service (LaaS), which can provide dedicated computing services without the upfront cost. Equus’s LaaS service allows you to experiment, test, and optimize your AI solutions on the fly in a clean, isolated lab. And if your AI workload requires a full on-premise solution, we’ve got you covered.
AI computing needs vary significantly based on the type of AI task, the size and complexity of the dataset, and the architecture of the AI model. For example, when training a language model for natural language processing, you might prioritize memory bandwidth and computational power. Whereas when deploying a computer vision model for real-time object detection, you’d prioritize latency and edge computing resources.
These examples show that AI covers a broad spectrum of use cases with different infrastructure needs. Our team at Equus can help you design, deploy, and manage AI solutions tailored to your use case. Additionally, as an Equus partner, you’ll get access to our deep supply chain expertise and economy of scale — ensuring the best value for your AI infrastructure. Contact us to learn more.