As Indian enterprises move from AI pilots to business-critical deployments, data governance has shifted from a compliance function to a foundational capability. With AI now embedded in customer interactions, regulated workflows and operational decision-making, organisations are realising that trust, safety and accountability must scale at the same pace as their models.
The DPDP Act has accelerated this shift—forcing companies to re-examine how they collect, process, monitor and explain data as AI intensity rises across sectors.
Safety and Explainability Take Centre Stage
Madhu V, technology architect for machine learning platforms at Tata Elxsi, said industries such as automotive, media and healthcare are moving from experimentation to full-scale AI deployments.
According to him, the core priority is to ensure that systems remain “safe, unbiased, and transparent.” In high-stakes sectors, even “subtle model drift can impact safety or clinical outcomes,” he warned.
For media companies, guardrails around content generation and personalisation are essential for brand trust. Governance, he said, must be “engineered into the development lifecycle, not layered on top,” with lineage tracking, model versioning and audit-ready logs becoming non-negotiable.
The underlying message: Trust cannot be retrofitted.
Governance as Engineering, Not Documentation
Kanakalata Narayanan, VP of AI and ML engineering at Ascendion, said their engineering-first approach embeds governance, observability and evaluation directly into the AI lifecycle.
“Our AI-QE specialists design synthetic datasets, functional and adversarial, run them through automated test harnesses and evaluate outcomes for relevancy and guard rails,” she told AIM.
She added that LLM-based “judge or jury” techniques, paired with human review, ensure grounded outputs in regulated domains. By combining LLM flexibility with the reliability of deterministic logic, Ascension aims to build predictable, risk-aligned systems.
BFSI: Accountability Over Accuracy
In finance, governance is becoming the primary design principle.
Yashas Khoday, CPO and co-founder of FYERS, said the DPDP Act has shifted BFSI from compliance to accountability. Lineage, consent, explainability and auditability are now essential for AI-driven trading, customer service and risk management.
He noted that in high-velocity environments, “the reliability, transparency and ethical treatment of data is becoming more crucial than the model’s accuracy.”
FYERS restricts customer data exposure to underlying models, keeps systems non-advisory and enforces strict validation and fairness checks, supported by human oversight.
A New Era of Continuous Oversight
Sharda Tickoo, country manager (India and SAARC) at Trend Micro, said threat actors are weaponising AI faster than ever, forcing firms to rethink governance.
Organisations must now apply Zero Trust to AI—verifying every access point, tracking which models use which datasets and enforcing visibility across the pipeline. The DPDP Act, she said, mandates algorithmic audits and impact assessments.
Continuous oversight has evolved from an operational best practice to a regulatory requirement. With models refraining frequently, real-time detection of compliance drift and unauthorised interference is now essential.
Governance as a Strategic Capability
Varun Babbar, VP and managing director at Qlik India, saidenterprises are shifting from traditional governance to real-time, AI-aligned frameworks.
“The evolution of the DPDP Rules is raising expectations around consent, transparency and accountable data use,” he said. Qlik research shows nearly half of Indian enterprises cite data quality and governance as their biggest AI bottlenecks.
Governed analytics platforms, offering lineage, auditability and quality controls, are now critical to scaling AI responsibly and preparing for upcoming 2026 regulations.
Sajith Nambiar, head of solutions at UST, said Responsible AI is embedded into their accelerators through metadata-driven validation of data quality, lineage and consent. Explainability frameworks generate contextual narratives for every AI decision with human-in-the-loop oversight, ensuring ethical alignment.
Their goal: systems that are “accurate, explainable, auditable and ethically governed.”
The Road Ahead
India’s next stage of AI maturity will be defined not by model speed but by the strength of governance structures that power it. The message across industries is clear: governance must be designed into AI from day zero.
Enterprises that treat governance as a strategic capability—not a regulatory checkbox—will scale faster, innovate more responsibly and build the trust required to compete globally.
In India’s AI landscape, governance is no longer a burden. It is becoming a competitive advantage.
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