Introduction

If you think it's due to AI, then you're spot on!

Ever since 2021, businesses have been rapidly deploying generative AI across a wide range of use cases.

Many of which involve sensitive data. This has made organizations realize the need for highly secure and tightly controlled cloud infrastructure.

But there is more to it. The shift is also driven by issues associated with the public cloud.

The next section is about that.

Challenges with Public Cloud

These reasons are based on our cloud market study, discussions with business leaders about their perspectives, and active listening to social media conversations on the topic:

1. Possibility of Resource Sharing Issues

   In a public cloud, a third-party cloud service provider owns and operates the cloud infrastructure. Companies using this type of cloud model only have access to a portion of these resources and are considered tenants. And multiple tenants share the same cloud resources, which can lead to:

   1. Contention issues

      We believe explaining this term with an example will give you a clearer perspective.

      So, here it is —> If one tenant consumes more cloud resources due to their AI experiments, other tenants may have to wait longer to access their own share of the resources—causing delays in output and potential reputational damage for them.

   2. Increased threat surface

      Let's understand this with an example —> Let's say one tenant's AI test fails and somehow compromises cloud resources. This can eventually leave other tenants vulnerable, as they share those resources, thus expanding the threat landscape.

2. Lack of Data Transition Visibility

   Data in the public cloud is constantly moving, shifting from one location to another.

   And organizations using this type of cloud infrastructure often have no visibility into these data movements, which complicates data security and necessitates additional protective measures.

   As a result, there is a high risk of data being easily exposed to malicious actors during these transitions.

   Here's an example of how things can go wrong while experimenting with AI

   Let's say a company is using an AI tool to analyze sensitive customer data stored in the public cloud for some sort of predictive activities related to their sales report.

   While this tool processes information, data is transferred between different geographical regions in the background.

   The department is unaware of this movement, and as a result, security measures aren't planned and implemented accordingly.

   This left these pieces of data exposed to potential breaches.

3. Identity Management Misconfiguration Due to Rapid Scaling

   While the public cloud is easy to scale and highly flexible, achieving these benefits isn't always straightforward.

   In some cases, due to rapid scaling of businesses, it becomes difficult to determine who is responsible for what and to what extent, leading to IAM (Identity and Access Management) misconfigurations.

   These misconfigurations can result in system failures.

   To give you a sense of the impact, Gartner research shows that system failures due to misconfigurations increased by 73% in 2023, up from 50% in 2020.

   Here is a hypothetical scenario for better understanding

   A new marketing department is testing an AI tool to analyze their webinar audience. This process involves using sensitive user data, like email addresses and phone numbers, which are stored in the company's public cloud.

   Meanwhile, the IT team, located in a different time zone, wasn't aware of this, and as a result, IAM configurations, such as user roles and resource allocations, weren't set up properly.

   This occurred due to a communication gap following the opening of a new office.

   As a result, the customer data is now vulnerable and could be accessed by both unauthorized employees and external parties.

Does all of this mean you should shift to a private cloud as well?

Not really.

The decision isn’t binary. It depends on your workload profile, industry, and risk tolerance. For example:

1. 1. Private Cloud: Best for regulated industries (finance, healthcare, government) and sensitive AI experiments.
2. 2. Public Cloud: Ideal for scalability, global deployment, and non-sensitive workloads.
3. 3. Hybrid Cloud: The pragmatic choice for most enterprises, combining control with agility.

In short, it’s about aligning infrastructure with business goals.

As AI adoption accelerates, enterprises must ensure their environments deliver performance, security, and compliance. For many, this means moving workloads back to the private cloud or adopting a hybrid approach.

And reaching this conclusion can sometimes require expert guidance. If you find yourself requiring one, then reach out to us at info@ideacrestsolutions.com