New Delhi, Jan. 28 -- As enterprises scale AI workloads across hybrid and multi-cloud environments, data privacy is emerging as a core architectural consideration rather than a downstream compliance task. It is increasingly shaping how organisations design data platforms, deploy AI systems, and manage operational risk.

Conversations with executives and technology leaders indicate a broader reset underway inside enterprises. AI systems rely on vast and continuously moving data sets, but weak governance, fragmented visibility, and inconsistent controls are widening exposure to breaches, outages, and regulatory scrutiny. As a result, data privacy is being reframed from a legal requirement to a critical element of business resilience.

Privacy moves from the perimeter to the core

Traditional enterprise security models were built around perimeter defences such as firewalls, access controls, and network segmentation. But AI workloads now operate across hybrid and multi-cloud environments, ingesting vast volumes of structured and unstructured data from a growing mix of internal and external sources.

Sanjay Agrawal, Head Presales and CTO at Hitachi Vantara India and SAARC, says this has made perimeter-based thinking inadequate. As AI-generated and machine-processed data becomes indistinguishable from human-created data, enterprises can no longer assume trust by default. Privacy, governance, security, and availability, he argues, must be engineered directly into the data infrastructure itself.

This architectural rethink is being driven by measurable risk. Industry research cited by Agrawal shows that more than half of Indian IT leaders see data security gaps as the single biggest barrier to scaling AI, while concerns around AI-enabled data breaches continue to rise. The financial consequences are becoming more severe as well. Recent reports estimate the average cost of a data breach in India at Rs.220 million, with outages, ransomware incidents, and data loss events now carrying the same regulatory and reputational impact as conventional breaches.

AI adoption sharpens accountability

The enforcement of India's Digital Personal Data Protection (DPDP) Act has further accelerated this shift. Unlike earlier frameworks that focused primarily on breach response, the DPDP Act introduces sustained accountability across the entire data lifecycle-from collection and processing to access, storage, and recovery.

For enterprises deploying AI, this accountability extends to training data, model behaviour, and automated decision-making systems. "Checkbox compliance is no longer sufficient," says Vaibhav Tare, Chief Information Security Officer at Fulcrum Digital. Managing privacy risk in an automated environment, he adds, requires strong governance, accountable leadership, and greater transparency in how data and AI models are used.

Cybersecurity and data management firms increasingly frame privacy as inseparable from operational continuity. Sandeep Bhambure, Vice President and Managing Director for India and SAARC at Veeam Software, points to ransomware research showing that 69% of affected organisations faced multiple attacks in a single year, while 90% saw their backups targeted. Fragmented visibility and weak governance, he warns, expose enterprises not just to breaches but to prolonged disruptions that can derail AI-led transformation efforts.

Identity, cloud, and real-time systems raise the stakes

As enterprises move deeper into SaaS platforms, cloud-native architectures, and API-driven ecosystems, identity has emerged as a critical vulnerability. According to Balaji Rao, Area Vice President for India and SAARC at Commvault, nine out of ten cyberattacks now target identity systems because they control access to data, applications, and AI workflows.

In distributed environments where data and identities are constantly in motion, privacy depends on real-time visibility, policy-driven governance, and resilient recovery capabilities. Identity resilience-maintaining access while limiting exposure-has become as essential as encryption or backup.

At the network and edge layer, Akamai's Reuben Koh notes that data now moves "at machine speed" across cloud platforms, APIs, and intelligent systems. As a result, the ability to discover, classify, and secure sensitive data must operate just as quickly. Granular segmentation, real-time threat intelligence, and automated defences are increasingly central to limiting the impact of inevitable incidents.

Payments, real-time engagement, and user trust

The privacy challenge is particularly pronounced in sectors built on real-time data exchange. In digital payments, fintech platforms process large volumes of sensitive financial and business data, making privacy-by-design essential to sustaining trust. Prakash Ravindran, CEO of InstiFi, says strong data protection practices allow merchants and users to engage confidently with digital systems while reducing operational risk.

A similar tension exists for companies running real-time engagement platforms. Agora's Ranga Jagannath points out that while AI, IoT, and real-time communications generate enormous volumes of personal data, privacy must be intrinsic to every user interaction. Secure APIs, end-to-end encryption, transparency, and user control are becoming baseline expectations rather than differentiators.

Privacy as an enabler, not a brake

A consistent theme across enterprise leaders is that privacy is no longer viewed as a constraint on innovation, but as an enabler of responsible AI adoption. Indigenous solutions such as those developed by Quick Heal's enterprise arm, Seqrite are increasingly focused on making data protection practical, scalable, and aligned with India's regulatory context-particularly for startups and mid-sized firms with limited compliance resources.

Sunil Sharma, Managing Director at Sophos India, adds that technology alone cannot address data risk. As AI systems become more autonomous and data-driven, building a culture of accountability and awareness across organisations is just as critical.

The enterprise reset

Experts believe India's AI ambitions will be shaped as much by data privacy architecture as by algorithms. Enterprises that embed privacy, governance, and resilience into the core of their data strategies will be better positioned to scale AI responsibly, withstand cyber shocks, and sustain customer trust. In the AI-first enterprise, data privacy is no longer a side function. It is the operating system.

Published by HT Digital Content Services with permission from TechCircle.