Artificial Intelligence is transforming how enterprises operate, innovate, and compete. From intelligent automation and analytics to generative AI and industry-specific applications, businesses are generating and processing more data than ever before. As AI adoption accelerates, this raises an important question: Who controls the data that powers your AI?
This was one of the key themes explored during Synology’s Data Intelligence Dialogue, presented in association with CNBC-TV18. The discussion brought together industry leaders Joanne Weng, Director of International Business, Synology Inc.; Brajesh Shrivastava, Director, Deduce Technologies; and Rajiv Mathur, Director, Eagle, to discuss how enterprises can build secure, resilient, and future-ready data infrastructure in the AI era.
For Rajiv Mathur of Eagle, the conversation goes beyond technology. As enterprises adopt AI and manage growing volumes of critical data, security, governance, and control must become fundamental business priorities. Ensuring that data remains protected, accessible, and under the right level of control is essential for building a secure and future-ready AI strategy.
AI Is Only as Strong as the Data Behind It
AI models may be powerful, but their success depends on the quality, accessibility, and security of the data behind them. Today, enterprise data is spread across applications, endpoints, servers, databases, cloud platforms, and other environments. Without proper visibility and governance, this fragmented data can create security risks and make it difficult for organizations to get the right value from their AI investments.
As AI adoption grows, businesses need to rethink their approach to data infrastructure. The question is no longer simply “Where should we store our data?” Instead, organizations need to understand where their data resides, who can access it, how it is protected, and how quickly it can be recovered when required. They also need to determine which data should remain on-premises, which can move to the cloud, and whether their infrastructure provides the necessary visibility, control, and compliance.
These considerations are becoming even more important as AI systems increasingly interact with sensitive business and customer data. A future-ready AI strategy therefore requires more than computing power and storage—it requires a strong foundation of security governance, accessibility, and control to ensure that data remains protected while supporting innovation.
Rajiv Mathur: Security Must Come First
During the Data Intelligence Dialogue, Rajiv Mathur highlighted a point that is becoming increasingly important for businesses adopting AI: “Security of data is paramount. Whether we are compliant or not, security is going to be a very important chapter.” His message reflects a fundamental shift in how enterprises need to approach data as AI becomes a larger part of everyday business operations.
While compliance remains important, it cannot by itself protect an organization from every threat. Businesses need to make security an integral part of how data is stored, accessed, managed, monitored, and backed up. With enterprise data growing in both volume and value, organizations must have the right safeguards in place to reduce risks and maintain control over their critical information.
A security incident involving sensitive business or customer data can have consequences far beyond the IT department. It can disrupt business continuity, affect customer trust, and damage an organization’s reputation and operations. For businesses investing in AI, protecting the data that powers these investments must be just as important as the technology itself.
The Cloud Is Not the Only Answer
Cloud computing has played a major role in digital transformation by offering businesses flexibility, scalability, and access to powerful computing resources. However, as organizations manage increasing volumes of data, many are now taking a more balanced approach. Sensitive information may require greater control, regulatory requirements can influence where data is stored, and growing data volumes can also increase concerns around long-term infrastructure and operational costs.
This is driving greater interest in hybrid infrastructure, where businesses can benefit from the flexibility of the cloud while retaining greater control over critical data on-premises. During the Data Intelligence Dialogue, Rajiv Mathur highlighted the importance of this approach, with organizations increasingly evaluating their data based on its sensitivity, business value, security requirements, and operational needs.
Rather than putting all data into a single environment, businesses can decide where each type of data belongs. Critical or sensitive information can remain within controlled on-premises environments, while other workloads can leverage the cloud where it makes business sense. This approach allows organizations to achieve the right balance between flexibility, security, cost, and control as they prepare their infrastructure for the growing demands of AI.
Data Sovereignty Is Becoming a Business Issue
Data sovereignty is no longer just a regulatory or compliance concern; it is becoming an important business and security priority. As governments introduce stronger data protection and localization requirements, organizations need greater clarity over where their data is stored, processed, and managed. Maintaining control over critical data is increasingly important for businesses looking to reduce risk and build long-term resilience.
For enterprises in India, this becomes particularly relevant as the country develops its own AI ecosystem and focuses on secure, locally relevant AI capabilities. Regional language models, industry-specific AI applications, and sensitive datasets require infrastructure that can support innovation while providing the right level of security, governance, and control.
Ultimately, organizations need to know where their data is, who can access it, and how it is protected throughout its lifecycle. Having this visibility and control helps businesses strengthen security, meet evolving requirements, and build a resilient data infrastructure that is ready for the growing demands of AI.
The New Priority: Control Your Data
The AI era is changing how organizations think about data infrastructure. While storage capacity remains important, it is only one part of the bigger picture. A future-ready environment needs to bring together data, security, governance, backup, accessibility, and control to ensure that businesses can manage their growing data securely and efficiently.
For enterprises, the focus must now move beyond simply storing and managing data to actively governing, protecting, and controlling it. This means having clear visibility into where data resides, who can access it, how it is protected, and how quickly it can be recovered when needed. A strong data infrastructure provides the foundation businesses need to support AI while reducing risks to critical information.
At Eagle, this approach is central to helping businesses build infrastructure that meets today’s data requirements while preparing them for the growing demands of AI. The goal is not to avoid cloud or AI technologies but to help organizations adopt them without losing control of their most valuable asset—their data. With the right infrastructure, businesses can embrace innovation while keeping security, governance, and control at the forefront.
Building an AI-Ready Data Strategy
As Rajiv Mathur emphasized during the discussion, security needs to remain a priority as businesses transform.
A strong AI-ready data strategy should therefore consider:
- Understand Your Data: Identify what data you have, where it resides, and how critical it is to your business.
- Classify Data by Sensitivity: Not every dataset needs the same level of protection or the same infrastructure environment.
- Strengthen Security: Protect data against unauthorized access, cyberattacks, accidental deletion, and other risks.
- Build Reliable Backup and Recovery: AI and digital transformation increase the importance of business continuity. Critical data must be recoverable when required.
- Maintain Visibility and Governance: Organizations need clear policies around who can access data, where it is stored, and how it is used.
- Choose the Right Infrastructure: Cloud, on-premises, and hybrid environments can all play a role. The right choice depends on the organization’s data, security, compliance, and business requirements.
The AI Future Belongs to Businesses That Control Their Data
AI is creating enormous opportunities for enterprises, but it is also changing how businesses need to manage their most valuable resource—their data. The question is no longer simply whether an organization is ready to adopt AI. The bigger question is: Is your data infrastructure ready for AI?
The discussion at Synology’s Data Intelligence Dialogue makes one thing clear: businesses need to look beyond storage and focus on security, governance, sovereignty, and control. For organizations navigating this transformation, Rajiv Mathur’s message is especially relevant: data security must remain paramount. As AI systems handle increasingly valuable and sensitive information, protecting the data behind these technologies must be a business priority.
As AI continues to reshape the enterprise, organizations that build a strong foundation for their data today will be better positioned to innovate securely tomorrow. At Eagle, the focus is on helping businesses build data environments that are secure, governed, resilient, and ready for the future. Because AI may be the future of business but data remains the foundation. And that foundation needs to be secure, governed, and under control.