Cloud computing is moving businesses forward, and it's also always evolving. The first wave of the cloud involved platforms as a service. Then came the phase of infrastructure as a service, followed by how the cloud could collect, store and analyze data. The next wave is about leveraging artificial intelligence (AI) to deliver efficiencies. As “AI in the cloud” gains momentum, it’s critical to break through the growing hype.
What Is AI in the Cloud?
Cloud-based AI refers to any type of cloud service designed to assist developers in integrating AI or machine learning into their application. All major clouds offer an array of AI tools to help developers do just about anything from image recognition to analyzing big data for insights.
Cloud AI delivers some of the same advantages of other cloud services. You don't need to manage your own infrastructure for hosting AI applications, making it much easier to deploy. Configurations and models are already available for developers to use and leverage, and using the public cloud AI also means your team doesn't have to be experts in the field.
But is AI in the cloud sustainable? Some experts might argue that it's a fad. All the major public cloud owners eagerly offer cloud-based AI services, and there are many benefits to using them. The structure is already developed, access is easy, and most options are fairly affordable. How could this model possibly have any shortcomings?
What About Data Privacy?
Many companies across the world, big and small, use public cloud services every day with no concern. It allows them to make sense of data and find opportunities to be more efficient and profitable. It may even help them develop new products or expand into a new vertical.
But nothing's perfect. Using AI in the public cloud requires IT managers to ask: "What about data privacy?"
AI feeds off of data and it needs a large amount of it. When AI runs in a public cloud, it must be fed lots of data, often from different places, especially for companies using multiple public and private clouds. However, in the quest to harness the power of an AI engine, you might begin to hesitate when that data is highly sensitive. It could be protected health information (PHI), financial, or other data that's subject to heavy regulation while also being extremely desirable to cybercriminals. On top of this, you have to meet compliance mandates on how you collect, store and transfer data. This can get tricky.
For example, in July 2019, a software engineer was arrested for hacking a server containing customer information from financial giant Capital One. The hacker was a former employee of Amazon Web Services (AWS), the host of Capital One's data. The company didn't just use AWS for storing data; they built their own web applications on top of the cloud to be able to analyze and work with the data. So how did the hacker access the servers?
The FBI reported that she gained access through a misconfiguration of a firewall on the web app. AWS quickly responded that Capital One controlled the apps and AWS was not at fault. Capital One resolved the vulnerability, but the breach had already occurred.
The Challenge with Public Clouds and AI
The main challenge around data privacy is that the old approach of securing internal data servers doesn't correlate with the cloud. Cloud security measures are still considered by many to be immature. Much of data security has revolved around securing the perimeter, which is based on building walls and assuming that threats will attempt to come through the front door. With all the attention on the perimeter, the assets that live in the cloud are overlooked.
So while the cloud provides significant opportunities and benefits, the way in which it is secured has to evolve, and that may be more attainable in a hybrid cloud.
Hybrid Cloud, New Frontier of AI
You do have an alternative beyond using only a public cloud. A hybrid cloud is the new frontier of cloud AI. Along with offering a better approach to data privacy and security, you can also maximize performance and speed.
The enterprise is hungry for more AI insights that can drive intelligent actions. This desire is felt across many industries, from retail to manufacturing to healthcare. And these industries need the right infrastructure. That infrastructure must be robust enough to handle large amounts of data with high bandwidth that isn't complicated. It must be simple, scalable, and secure.
Nutanix has partnered with NVIDIA and Mellanox Technologies to develop such a solution that's turnkey for applying AI in the cloud. By creating a hybrid cloud for AI applications, the eternal question of "Is it safe?" is more consistently answered with yes.
Protecting your data matters, and AI can only be a tool for innovation with data. You shouldn't have to pause or hesitate in using the cloud because of data security fears. The Nutanix solution includes all the aspects you need and is built with a security-first design.
Cloud computing and AI have already had a huge impact on the business world. Now is the time to use cloud AI to gain a competitive advantage and drive better decision-making. See how our solutions give you freedom and flexibility while also ensuring data privacy. Contact Nutanix today to discover how it works.
Images 1, 2 and 3 by Pixabay.
Michael Brenner is a keynote speaker, author and CEO of Marketing Insider Group. Michael has written hundreds of articles on sites such as Forbes, Entrepreneur Magazine, and The Guardian and he speaks at dozens of leadership conferences each year covering topics such as marketing, leadership, technology and business strategy. Follow him @BrennerMichael.
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