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13 Feb 2026·Source: The Hindu
3 min
Polity & GovernanceScience & TechnologyNEWS

AI Governance: Prioritizing Decision-Making Over Infrastructure in the Digital Age

Experts emphasize governing decisions over infrastructure in the age of AI systems.

AI Governance: Prioritizing Decision-Making Over Infrastructure in the Digital Age

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Experts at The Hindu Tech Summit 2026 highlighted that traditional governance, risk, and compliance (GRC) models are struggling to keep pace with the rapid advancements in AI systems. Balakrishna Kanniah noted that while platforms evolve, the fundamentals of GRC remain crucial. Gowdhaman Jothilingam emphasized the importance of governing decisions rather than infrastructure, advocating for accountability.

He recommended the FAIR model for quantifying cyber risk. Venimalai Sundaresan discussed the shift to continuous governance oversight in regulated sectors like banking. Sakthi Balan Muthaiah cautioned against anthropomorphism in AI, highlighting differences in how AI and humans learn.

The session was moderated by Koushik Ramani.

Key Facts

1.

Traditional governance, risk, and compliance (GRC) models are struggling to keep pace with AI advancements.

2.

Governing decisions is more important than governing infrastructure in the context of AI.

3.

Accountability is a key differentiator in AI governance.

4.

The FAIR (Factor Analysis of Information Risk) model is recommended for quantifying cyber risk.

5.

Anthropomorphism in AI can lead to incorrect assumptions in auditing and regulation.

UPSC Exam Angles

1.

GS Paper II: Governance, Polity, Social Justice

2.

Ethical considerations in AI and technology governance

3.

Statement-based MCQs on AI regulations and frameworks

More Information

Background

The concept of governance, risk, and compliance (GRC) has evolved significantly over time. Initially, GRC was largely focused on regulatory compliance and internal controls, particularly in the financial sector. The Sarbanes-Oxley Act of 2002 in the United States, for example, mandated stricter financial reporting and internal controls for publicly traded companies, influencing GRC practices globally. As technology advanced, GRC expanded to include IT governance and cybersecurity. The rise of the internet and digital data created new risks related to data privacy, security breaches, and intellectual property theft. Frameworks like COBIT (Control Objectives for Information and Related Technologies) emerged to provide guidance on IT governance and management. More recently, the advent of artificial intelligence has introduced new challenges for GRC. AI systems raise complex ethical, legal, and societal questions, including issues of bias, fairness, accountability, and transparency. Traditional GRC models, designed for more predictable and rule-based systems, often struggle to address the dynamic and adaptive nature of AI. This necessitates a shift towards more agile and decision-centric governance approaches, as highlighted in the news.

Latest Developments

Recent developments in AI governance include the development of ethical guidelines and frameworks by various organizations and governments. The European Union's AI Act, for example, proposes a comprehensive legal framework for AI, classifying AI systems based on risk and imposing specific requirements for high-risk applications. There is also growing emphasis on AI auditing and certification to ensure that AI systems meet certain standards of fairness, transparency, and accountability. Organizations like the IEEE (Institute of Electrical and Electronics Engineers) are developing standards and certifications for AI ethics and governance. Looking ahead, AI governance is expected to become increasingly important as AI systems become more pervasive and impactful. Key challenges include developing effective mechanisms for monitoring and enforcing AI regulations, addressing the potential for bias and discrimination in AI algorithms, and ensuring that AI is used in a way that benefits society as a whole.

Frequently Asked Questions

1. What is the main focus of current discussions on AI governance?

The current focus is shifting towards governing the decisions made by AI systems, rather than solely focusing on the infrastructure supporting them. Experts emphasize accountability in AI decision-making.

2. What are the key areas where traditional Governance, Risk, and Compliance (GRC) models are facing challenges?

Traditional GRC models are struggling to keep pace with the rapid advancements in AI systems. They need to adapt to the complexities and unique challenges presented by AI.

3. What is the FAIR model, and why is it relevant to AI governance?

The FAIR (Factor Analysis of Information Risk) model is a method for quantifying cyber risk. It is recommended for AI governance to help organizations understand and manage the risks associated with AI systems.

4. What is 'anthropomorphism' in the context of AI, and why should it be avoided?

Anthropomorphism in AI refers to attributing human-like qualities or characteristics to AI systems. It should be avoided because AI learns and operates differently than humans, and anthropomorphism can lead to incorrect assumptions in auditing and regulation.

5. What is the significance of continuous governance oversight in sectors like banking, especially with the rise of AI?

Continuous governance oversight is becoming increasingly important in regulated sectors like banking to ensure that AI systems are used responsibly and ethically. It helps in monitoring AI's impact and ensuring compliance with regulations.

6. How might prioritizing decision-making over infrastructure in AI governance affect common citizens?

Prioritizing decision-making in AI governance can lead to more accountable and transparent AI systems. This can impact common citizens by ensuring that AI-driven decisions affecting their lives are fair, ethical, and explainable.

Practice Questions (MCQs)

1. Consider the following statements regarding the FAIR model, as discussed in the context of AI governance: 1. FAIR model is primarily used for quantifying financial risks associated with AI investments. 2. FAIR model emphasizes governing decisions related to AI rather than focusing solely on infrastructure. 3. FAIR model is a proprietary framework developed by a single technology company. Which of the statements given above is/are correct?

  • A.1 only
  • B.2 only
  • C.1 and 3 only
  • D.2 and 3 only
Show Answer

Answer: B

Statement 1 is INCORRECT: The FAIR model is used for quantifying cyber risk, not specifically financial risks associated with AI investments. Statement 2 is CORRECT: Gowdhaman Jothilingam emphasized the importance of governing decisions rather than infrastructure, advocating for accountability, and recommended the FAIR model for quantifying cyber risk. Statement 3 is INCORRECT: The FAIR model is not a proprietary framework developed by a single company. It is an open standard for information risk management.

2. Which of the following statements best describes the central argument made by experts at The Hindu Tech Summit 2026 regarding AI governance?

  • A.AI infrastructure development should be the primary focus of governance efforts.
  • B.Traditional governance, risk, and compliance (GRC) models are fully adequate for managing AI systems.
  • C.Governing decisions made by AI systems is more critical than governing the underlying infrastructure.
  • D.Anthropomorphism in AI is essential for effective governance.
Show Answer

Answer: C

The experts at The Hindu Tech Summit 2026 emphasized that traditional governance, risk, and compliance (GRC) models are struggling to keep pace with the rapid advancements in AI systems. Gowdhaman Jothilingam specifically advocated for governing decisions rather than infrastructure, highlighting the importance of accountability.

3. Sakthi Balan Muthaiah cautioned against anthropomorphism in AI. Which of the following statements reflects the key concern associated with anthropomorphism in the context of AI governance?

  • A.Anthropomorphism helps in better understanding and predicting AI behavior.
  • B.Anthropomorphism ensures that AI systems are aligned with human values and ethics.
  • C.Anthropomorphism can lead to misinterpretations of AI capabilities and limitations due to differences in how AI and humans learn.
  • D.Anthropomorphism facilitates the integration of AI systems into human society.
Show Answer

Answer: C

Sakthi Balan Muthaiah cautioned against anthropomorphism in AI, highlighting differences in how AI and humans learn. This suggests that attributing human-like qualities to AI can lead to misinterpretations of its capabilities and limitations, which is a key concern in AI governance.

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