Why India’s regulated sectors are turning to on-prem AI

Piyush Agarwal shares insights on why highly regulated sectors in India such as banking, insurance, and healthcare are now leveraging on-premise and hybrid AI infrastructure as data privacy requirements are on the rise.

Why is on-prem AI infrastructure crucial for sectors dealing with strict data regulations in India? What are some real-world use cases of on-prem AI in India?

In India, highly regulated sectors like banking, insurance, public services, and healthcare must address stringent data residency and privacy mandates, especially with the enforcement of the Digital Personal Data Protection Act (DPDPA) and RBI’s data localization norms. For these industries, data sensitivity and strict governance are non-negotiable, and AI must be deployed where data can be controlled securely, which often means on premises or in virtual private clouds.

That said, Cloudera is not prescriptive about where enterprises must run AI. Instead, we empower them with the flexibility to run AI wherever they need to, on premises, on cloud, or in a hybrid environment, while maintaining enterprise-grade governance, security, and control. Our goal is to help customers perform analytics and AI at the point of data origination and residency, bringing models to the data rather than the other way around.

A great example is our work with PhonePe, one of India’s leading fintech platforms. Cloudera enabled PhonePe to modernize its data strategy and optimize infrastructure across on premises and cloud environments. By enabling flexible workload migration and a unified data and analytics layer, we helped them scale AI confidently while maintaining cost-efficiency and regulatory compliance.

In an era of cloud-first hype, why double down on on-premise and hybrid deployment for AI analytics?

We fully support cloud-first ambitions, but enterprise AI requires a data-first mindset, especially in regulated environments. Organizations today need the freedom to choose where AI runs best for their operational, financial, and compliance goals. That might be in the cloud, but it can just as often be on premises or in hybrid environments.

Cloudera delivers an open, secure, and interoperable platform that allows enterprises to build and deploy AI applications using their enterprise data, whether on premises, in a virtual private cloud, or across both.

This is especially critical for companies managing large volumes of sensitive, complex data. With Cloudera’s hybrid platform, organizations can control performance, cost, and security without compromise, and ensure AI models are governed, transparent, and responsible. Through a data-first strategy, Cloudera enables organizations to derive value from all their data, across divisions, geographies, and workloads. Our open data lakehouse architecture provides a scalable and trusted foundation for Enterprise AI, regardless of where the data resides, be it public cloud, private cloud, or on-premises.

For instance, Cloudera Data Visualization, recently made available for on-premises deployments, allows enterprises to securely access AI-powered self-service analytics and insights across hybrid environments without moving data. That’s a game-changer for regulated sectors.

What kind of enterprise maturity is ideal to extract full value from this tool?

Enterprises that extract the most value from Cloudera Data Visualization are typically those that have mature data governance, security, and collaboration strategies, especially across hybrid and multi-cloud environments.

These organizations often deal with high compliance requirements and require tight control over data residency. This maturity enables seamless use of AI-powered visualization and  natural language querying, enabling data engineers, analysts, and scientists to work together more effectively.

Because Cloudera’s Shared Data Experience (SDX) underpins the Cloudera platform, this layer of security extends to Cloudera Data Visualization with unified security and governance across environments, breaking down silos and enabling secure, predictive analytics and AI applications without needing to move data.

Organizations that are ready to embrace self-service visualization and built-in AI tools can accelerate insights, streamline operations, and make smarter decisions using intuitive, out-of-the-box imaging.

Organizations already leveraging Cloudera are well-positioned to maximize the benefits of this tool, empowering teams to make real-time, data-driven decisions and collaborate more effectively through self-service visualizations and natural language querying.

How does the tool integrate with existing data lakes, warehouses, or AI models? What role does open-source play in the tool’s architecture or ecosystem?

We believe that open-source is essential to building trustworthy, flexible, and future-ready AI. That’s why Cloudera AI is designed to let enterprises build and deploy their own AI applications using any open-source large language model (LLM) of their choice. This approach gives organizations the ability to fine-tune models on their own data, while keeping everything secure, private, and fully under their control, whether running on premises or in a virtual private cloud.

Cloudera also provides a growing library of Applied ML Prototypes (AMPs), including the LLM Chatbot Augmented with Enterprise Data, that help customers operationalize generative AI use cases using open frameworks and toolkits. Additionally, technologies like Apache Iceberg, which power Cloudera’s open data lakehouse architecture, allow users to work with the analytics or AI tools they prefer without being locked to a single vendor.

Cloudera Data Visualization builds on this open, interoperable foundation by serving as the intuitive interface that brings AI-powered insights to life. It allows users to explore, visualize, and interact with data from across the enterprise, structured or unstructured, from data lakes or warehouses, without the need to move data.

In essence, Cloudera offers an open, secure, and enterprise-grade AI platform, where tools like Data Visualization help bridge the gap between complex models and actionable insights. By supporting open-source innovation and enterprise-specific governance, we help organizations build trusted, contextual AI, on their terms and infrastructure.

How does this launch align with India’s Digital India and digital upskilling missions? What kind of impact do you foresee this tool having on the way Indian enterprises use and visualise data?

This launch directly aligns with Digital India’s goals of empowering businesses to be more data- and AI-driven while maintaining sovereignty and control.

As data becomes the most strategic asset for modern enterprises, Indian businesses are under growing pressure to unlock actionable insights in real time. However, fragmented architectures and evolving data governance demands impede this progress. With Cloudera Data Visualization now available in on-premises environments, we are empowering organizations to access AI-powered insights securely, while maintaining complete control over their infrastructure.

This launch underscores our commitment to supporting Indian enterprises in becoming more agile, compliant, and insight-driven amid a rapidly evolving AI and data-first economy. It also aligns closely with the goals of Digital India and digital upskilling by democratizing access to advanced analytics and fostering a data-literate workforce equipped to lead in an AI-driven future.

With intuitive out-of-the-box imaging and natural language querying, the tool empowers a broader range of users, including business analysts and non-technical teams, to explore and understand complex datasets without relying heavily on IT. Its built-in AI tools and predictive application builder allow enterprises to integrate advanced analytics directly into their workflows, enabling faster, smarter decision-making.

The ability to deploy and visualize AI insights securely, without moving data, is especially valuable for sectors with strict compliance needs such as BFSI, healthcare, and public services. Backed by Cloudera’s Shared Data Experience (SDX), the tool ensures enterprise-grade governance and security, giving organizations the confidence to scale self-service analytics across teams and democratise AI. Overall, this tool encourages a more data-driven culture by bridging the gap between raw data and real-time business impact.