Private and Sovereign AI Is No Longer Optional – It’s the New Enterprise Standard
Manasi Vartak, VP of AI & Machine Learning at Cloudera, explains why enterprises are rapidly shifting to fully controlled, geography-aware AI models, how hybrid architectures are solving the cloud-vs-security dilemma, and why strong data governance has become the single biggest bottleneck holding most companies back from production-scale AI. From ethics boards to measurable ROI, the rules of enterprise AI plays by in 2025 have completely changed.
How are private and sovereign AI models changing the way enterprises approach data security and regulatory compliance?
Right now, there is a strong push for enterprises to have full control over their data and AI models. While large language models already know everything available on the public internet, they do not have knowledge of your customers, contracts, CRM data, or other internal information, which is where private AI and sovereign AI become very important.
We have seen that, depending on geography and the types of customers and workloads, organisations want to manage their AI estates in the same way they manage their data estates. Private and sovereign AI help them achieve this.
A key advantage of these approaches is that they take data security into account by default. Private AI ensures that your data does not leave your security perimeter while being used for AI. It stays within your secure boundaries, which automatically supports regulatory compliance.
Since we work with many multinational companies, we’ve seen that some customers prefer their data to be hosted in the Americas, while others require it to stay in Europe, each region having its own regulatory standards. Private and sovereign AI help organizations manage these requirements efficiently and with greater flexibility.
What does the latest Cloudera AI enterprise survey reveal about the pace and maturity of AI adoption today?
The latest Cloudera AI enterprise survey reveals strong enthusiasm for putting AI into production. However, many organisations continue to face challenges, particularly around data management, because AI is only as good as the data behind it.
At Cloudera, we help our customers prepare and enable their data for AI. Additionally, depending on the geography in which an organisation operates, there are different guardrails on how AI and data can be used. Moving from experimentation to production requires solving governance challenges, enablement challenges, and data security challenges, all of which are critical to successful AI adoption.
As AI moves from experimentation to execution, what new challenges are emerging in data governance and ethics?
One of the major gaps organisations face as AI moves from experimentation to execution is access to production data. If your data is not strongly governed and you don’t have proper lineage, you may be able to run small or toy examples, but you will not be able to scale AI effectively with enterprise data. That is why governance is now even more critical in the AI age than it ever was in the data age.
Ethics is an even broader challenge. With Generative AI, organisations can build applications that were never possible before. As a result, we are seeing enterprises form AI steering committees and AI ethics or review boards, and this trend is expected to continue and grow.
How can organisations future-proof their data architectures to stay agile amid AI technologies evolve?
Getting data architectures in order is essential to unlocking AI. Organisations need to ensure they have the right data lineage in place. They should have a proper data fabric and a well-organised data lake. It is also important to make sure that the data is ready for AI, which could include vectorising the data and using the right vector databases. Proper access controls must also be in place.
All the elements that have been critical for data and analytics workloads are becoming even more important in the AI era. Fortunately, many Cloudera technologies can help organisations achieve this and keep their data architectures future-proof.
How can a hybrid data architecture bridge the gap between cloud scalability and on-premise intelligence?
Cloud environments are known for their scalability, while on-premise environments are known for their security. Cloudera believes that organisations should not have to choose between the two. This is the idea behind our Anywhere Cloud, which provides the same cloud experience whether you are on-premises or in the cloud.
With our systems and our recent acquisition called Taikun, you can move workloads between these environments very easily. For example, if you find that running LLMs in the cloud is too expensive, you can quickly move that workload on-premise and use our optimised NVIDIA inference systems to run inference at scale.
This approach gives customers the flexibility to choose the setup that makes the most sense for their needs, whether that is cloud, on-premise, or a combination of both.
How can enterprises quantify the ROI of hybrid intelligence in terms of decision-making speed and business growth?
For AI, the return on investment must start with the business case. That is always where we advise customers to begin. Take a customer success or support use case, for example. If AI can reduce the time taken to resolve an issue from an hour to just a few minutes, the next question becomes: what impact does that have on revenue and customer satisfaction?
Once those outcomes are clear, it is much easier to work backwards into the technology. Too often, people get caught up in the excitement of new technology without first considering the real business need. That is often why some pilots never reach production. When a use case is directly linked to what matters most to the business, it becomes far easier to measure the ROI and demonstrate the value of AI initiatives.
