The Need for Secure Generative AI Infrastructure
Raido Linde
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August 9, 2024
There has been a big transition from virtual machines to microservices and from on-premises to the cloud by hyperscalers in the past decade. While some of this transition will continue and accelerate, we have seen a new trend of deploying generative AI on-premises rather than the public cloud due to concerns around data privacy, IP leaks and AI sovereignty, as well as high overhead costs. Because of that, there is a need for modern generative AI infrastructure that solves these issues and gives control back to organizations.
Data privacy issues are present with large language models and API-based solutions. Enterprises are concerned that their confidential data will end up in the training data for these models. This aspect is one of the key drivers for the increased number of on-premises AI deployments.
Many large organizations often depend on multiple cloud vendors and their on-premises infrastructure for added resilience, this combination brings added complexity to adapt generative AI.
There are many products, co-pilots, and ready-to-use applications available on the market. But what happens if every company uses the same tools? For example, in law, there are tools like Leya and Harvey. If every law organization uses the same tools, there is no real way to differentiate.
The most valuable data that companies have is usually stored in their own data centers, on servers, or in data warehouses. This is because that data is the core intellectual property of those companies. It can be recipes, proprietary code in software development, car designs in automotive companies, or powertrain designs. That data should never go to the public cloud.
The issue here is that you should be able to utilize the most valuable data with AI capabilities, but how?
We believe in data and AI sovereignty - that every enterprise, nation, and large organization should have internal AI capabilities. This means having the right skills and easily accessing and leveraging AI without being data scientists. Organizations should be able to secure their data to prevent leaks and run AI workloads themselves. Having these capabilities reduces the dependency on the largest cloud providers and their agenda which rarely aligns with the company’s own. This further reduces the need to hire specialized roles that can easily spin up the cost.
Realizing the three key pillars of future-proof generative AI (data privacy, IP differentiation, and AI sovereignty) is a challenging task. It requires operating in a challenging on-premises environment and having many capable employees in AI to develop and manage generative AI software infrastructure. This is a very complex process and it may take years to succeed. At the same time the speed of adaptation should be kept fast and the overall costs down. That is where ConfidentialMind comes in.
In the public cloud, you can quickly prototype and scale to production, but this often comes with unacceptably high fees from the cloud providers and data privacy issues.
We offer similar capabilities without those downsides. We are platform-agnostic, which means that with our generative AI infrastructure, you can run applications on-premises, hybrid, or in multi-cloud scenarios.
"The reason we started this generative AI software infrastructure company is that I have been working with two other co-founders, Markku Räsänen (CEO) and Esko Vähämäki (Chief Architect), for many years. We have started different companies and projects, and we noticed that in every project, we needed to build the infrastructure from scratch,” said Severi Tikkala, CTO and co-founder at ConfidentialMind. “You always have to decide which cloud to use, whether to build it yourself or use some abstracted services. You need to handle authentication, user management, and develop many other complicated components from the ground up. We realized there wasn't a good product for this purpose. So, we took the challenge to build a solid AI platform - a generative software infrastructure you can build upon for any business and technology."
That means organizations can now develop, deploy, and scale their own generative AI applications quickly and securely in their data centers using their confidential data and infrastructure. As a result, teams can understand data better, make decisions faster, and reduce operational costs.
Enterprises are starting to realize that adopting generative AI safely and securely requires transition back from public cloud to on-premises. As a result, they need a modern infrastructure that, aside from addressing data privacy, AI sovereignty, and securing IP, is cost-efficient.
ConfidentialMind is at the forefront of this shift, providing enterprises with the generative AI software infrastructure and tools to deploy and develop generative AI capabilities entirely on their own terms, using their own data and own infrastructure.
