The Shifting Governance Landscape
The rapid integration of Artificial Intelligence across various sectors has fundamentally altered the landscape of organizational governance. Experts at The Hindu
Tech Summit 2026 emphasized that the focus has shifted dramatically from managing and controlling the underlying technological infrastructure to overseeing and regulating the actual decisions these AI systems make in real-time. Traditional Governance, Risk, and Compliance (GRC) frameworks, designed for more static environments, are proving inadequate for the dynamic and often opaque nature of AI operations. This mismatch exposes businesses to novel and poorly understood risks, necessitating a reevaluation of how organizations approach oversight. Balakrishna Kanniah, a seasoned industry veteran, noted that while the underlying principles of GRC remain, the adoption rate and the perceived importance of GRC have accelerated significantly. It has moved from an audit-driven afterthought to a critical enabler of success for operations, new ventures, and product development, underscoring its central role in modern business strategy.
Governing Decisions, Not Hardware
The core of modern organizational oversight, according to insights from the tech summit, lies not in the hardware or software platforms, but in the critical decisions that AI systems facilitate. Gowdhaman Jothilingam, Global CISO and Head of IT at LatentView Analytics Ltd., articulated this shift, stating that companies are now primarily focused on governing the outcomes and choices made by AI, rather than the infrastructure itself, which has largely become standardized and scalable. The paramount differentiator in this new era is accountability for these AI-driven decisions. From a cybersecurity perspective, Jothilingam advocates for the FAIR (Factor Analysis of Information Risk) model, an open-source standard that quantifies cyber risk. This model is invaluable for translating complex cyber threats into tangible business impacts, aiding leadership in prioritizing security investments and justifying budgets by framing risks in financial terms that resonate with executive boards.
Adapting to Real-Time Governance
Regulated industries, particularly the banking sector, are grappling with the challenge of aligning their robust, albeit traditionally rigid, GRC frameworks with the inherently dynamic and adaptive nature of AI and cloud technologies. Vennimalai Sundaresan, vice president of data governance at Standard Chartered, highlighted this critical mismatch. Historically, banking systems were rule-based and predictable, allowing for governance processes with fixed review cycles – annual policy updates, quarterly control checks, and yearly audits. However, current AI and cloud-driven systems operate in real-time, creating a significant disconnect. This disparity makes it difficult to provide leadership with the real-time dashboards and immediate risk visibility they increasingly demand. Sundaresan stressed that governance can no longer function as a mere external supervisory layer; it must be intrinsically embedded at every organizational level to effectively manage AI's impact.
The Nuances of AI Understanding
A fundamental challenge in governing AI, as pointed out by Professor Sakthi Balan Muthaiah from Shiv Nadar University, stems from human tendencies to anthropomorphize these systems. We often use terms like 'intelligence,' 'learning,' and 'understanding,' projecting human cognitive processes onto AI. However, AI operates differently; for instance, humans learn multiplication by first understanding addition, a foundational concept. AI models, conversely, learn through pattern recognition, which is a distinct mechanism. This misconception can lead to significant errors, particularly in areas like auditing and regulation, where a clear and accurate understanding of how AI functions is crucial for effective oversight and risk management.
