By Sameer Bhatia, Senior Regional Director for India, Middle East, Türkiye and Africa at Seagate
Every organisation wants to harness AI, but few ask the most fundamental question: Can we trust the data powering it, and the infrastructure that’s used to store and access it?
The truth is that AI readiness starts with data readiness and reliable data infrastructure. Today, when data powers AI models, digital infrastructure, and national security frameworks, it is essential to ensure the integrity of the underlying hardware. Unverified or low-quality data storage poses a risk to video analytics and security systems, enterprise infrastructure, and consumer trust.
And so, I welcome the Governments’ recent decision to bring standalone hard disk drives (HDDs) under the Bureau of Indian Standards (BIS) Compulsory Registration Scheme. It strengthens the foundations for AI deployments and brings much-needed accountability, standardization, and confidence into the ecosystem.
Why trusted infrastructure matters in the AI era
Organisations need reliable, high-quality data to train AI models, optimize operations, improve customer experience, and make strategic business decisions. At the same time, AI implementation, cloud adoption, and digital transformation are creating increasingly large volumes of business-critical data. Much of this data can be fed back into AI models. And if we cannot trust the security, availability or reliability of data, how can we trust the quality of AI outcomes?
Today, data is the most valuable enterprise asset; the backbone of business growth, innovation and decision-making. Its integrity is no longer merely an IT mandate but a prerequisite for resilience and competitiveness. As AI increasingly powers healthcare, financial services, manufacturing and public safety, the reliability of the underlying data infrastructure becomes a strategic national asset. Without confidence in the data that powers AI systems, organisations will struggle to innovate, scale and realize value from their investments. Trustworthy AI calls for trusted data, which in turn must be stored and managed using secure, reliable and available infrastructure. It’s a core business consideration.
The cost of getting it wrong
Low-quality or unverified storage hardware can fail unexpectedly, disrupting access to critical systems, applications and business data. The consequences can range from operational downtime and recovery costs to data loss, business disruption and reduced productivity.
In video security environments, the impact can be particularly significant. Faulty storage can result in missing footage, poor video quality or incomplete records, potentially affecting investigations, public safety and regulatory compliance.
All these risks get amplified in an AI environment. Consider an AI startup in India. It may invest heavily in talent, models and computing resources, but if the infrastructure underpinning its data is unreliable, the value of those investments can quickly be undermined. AI models may be trained on incomplete or unreliable data, leading to less dependable business insights and poorer decision-making. This can result in weaker returns on AI investments, while innovation may slow or become misdirected.
Now scale that challenge across thousands of startups, enterprises and public-sector initiatives. As India accelerates AI adoption, organisations will need confidence that the data underpinning these investments remains secure, reliable and available. Trusted infrastructure, therefore, is not merely a technology consideration; it is a foundation for competitiveness, resilience and long-term growth.
Building trust requires both policy and industry
Technology works best when policy and industry go hand in hand. The Government’s decision to mandate BIS compliance for standalone HDDs signals the growing importance of trusted digital infrastructure. It also underlines India’s commitment to improving quality, accountability, and trustworthiness of infrastructure for driving AI, cloud and data center growth.
The move will enable organisations to procure storage hardware with greater assurance of safety and reliability. Quality standards will not just be a matter of routine compliance anymore, but an integral part of India’s AI infrastructure readiness journey. It will inspire greater confidence among customers, partners, and the broader technology ecosystems of enterprises. Companies too, on their part, should commit to using standards-driven and future-proof storage infrastructure as it is a backbone of the AI era.
Looking ahead: Trust will be India’s AI advantage
Two years ago, India had announced ambitions of “Making AI in India” and “Making AI Work for India”. This included, among other things, ensuring technological sovereignty, establishing large-scale computing infrastructure, and focusing on inclusive AI development and innovation. It was a clear signal for all businesses to design, relook, or enhance (as the need might be) their data strategies, starting with how they store data. It’s also an opportunity for data storage solution providers to contribute to nation-building by helping to create a trusted, sovereign, resilient, and scalable data foundation for India’s expanding AI ecosystem.
India has a fast-growing data center sector and has also begun developing its own AI standards and governance frameworks. India’s AI ambitions depend, among other things, on the robustness and trustworthiness of the country’s data infrastructure. Quality standards will be enablers of trusted AI, digital resilience, and long-term competitiveness. A secure, reliable, and standards-based data infrastructure will give the country a significant competitive advantage in attracting investment, developing trusted AI products, and competing at a global level.