Can AI be governed before it governs us?

In today’s data-driven economy, trust is the new currency. Organizations that embed responsibility into their AI models stay ahead of evolving regulations and make long-term impact. Findings of the 2025 EY Global Responsible AI Pulse survey of 975 C-suite leaders across the world, reveal that nearly every company in the survey has already suffered financial losses from AI-related incidents, with average damages conservatively topping US$4.4 million. With great power comes great responsibility. Today, that power belongs to AI. However, are we responsible enough to tame it to a point where it becomes a competitive advantage rather than a risk-inducing cost center?
 

Rise and pitfalls of AI

The need for robust governance has never been more urgent as evidenced by recent incidents where deepfake videos of celebrities went viral. AI systems are also facing the brunt for offering support to emotionally fragile teens, often with disastrous consequences. Be it a rogue AI overriding system controls to delete data or the crisis over fraudsters manipulating AI to hijack a crosswalk—the writing is on the wall: AI regulation is the need of the hour.
 

When left unchecked, AI systems can introduce risks that are difficult to mitigate, as a US lawyer realized after presenting AI-researched ‘hallucinated’ case studies to establish precedence during a trial. In another instance, an AI system developed to advise started suggesting inapplicable and often, dangerous lines of medical treatment because it was trained on hypothetical data. Across the globe, manipulated media, biased algorithms, and imagined responses are surfacing in challenging ways. Governments are responding with urgency, but the pace of regulation is yet to catch up with the speed of AI adoption. 

Evolving AI governance approach

The EU AI Act, that was implemented around a year ago, introduces a risk-based framework that categorizes AI systems and levies heavy penalties for violations. Depending on the severity of the AI compliance violation, companies can be fined as high as Euro 40 million or up to 7% of their global turnover—conveying a strong message to prioritize AI compliance. The US has taken a multi-pronged approach, targeting algorithmic discrimination and consumer transparency. China, meanwhile, has implemented stringent content labeling and licensing requirements for generative AI models, emphasizing information control.
 

India’s AI governance framework is designed to be non-prescriptive and aims to establish safety boundaries while allowing innovation to flourish. The Framework for Responsible and Ethical Enablement of Artificial Intelligence (FREE-AI), developed by the Reserve Bank of India, to offer a forward-looking blueprint for stakeholders in the financial sector, endorses this adaptive strategy by proposing seven guiding principles that include fairness, accountability, and explainability. It recommends structured governance mechanisms such as board-approved AI policies, AI incident reporting frameworks, and risk-based audit protocols to create an AI ecosystem that is ethically aligned and legally compliant. Developed to harness AI’s potential in the financial sector, these guidelines can help companies implement systems reliant on human oversight, especially in other high-stakes sectors like healthcare, law enforcement, and IT.
 

While it may take some years to achieve global synergy on regulations, it is imperative to agree on a set of universal non-negotiables to ensure AI innovation does not threaten organizational integrity. Like any other third party, AI tools must be approached with caution and screened through a strict risk assessment protocol that will not only check for negative incidences caused by the AI system but also scan it for potential risks. 

AI risk management: From principles to practice

Encouragingly, 80% of EY Global Responsible AI Pulse survey respondents said their organization had defined a clear set of responsible AI governance principles. However, when it came to execution only 66% had established real-time monitoring to ensure adherence. To harness the potential of AI, organizations must implement structured safety guardrails—risk assessments, accountability mechanisms, periodic testing of learning data to check for bias, continuous monitoring of results, and AI incident management mechanisms.
 

Majority of framework creators like ISO and NIST have introduced AI system management frameworks. ISO 42001 provides guidelines to manage AI-related risks effectively by creating a robust framework to manage the AI ecosystem. Training the workforce in line with these principles can help achieve AI accountability and transparency. This will help lay a strong foundation to build AI infrastructure, establish good governance around it, and set up risk management protocols and compliance checkpoints to prevent regulatory and legal pitfalls. Through responsible implementation, organizations can leverage AI governance as competitive advantage in India.
 

Next steps

AI holds immense power to enhance productivity, and transform communication but without the right guardrails, that power can quickly become a liability. As organizations increasingly adopt AI, those that lead with governance by embedding ethics, transparency, and accountability, will not only innovate faster, but more sustainably. In the AI era, responsible innovation is not just good practice, but a competitive advantage when deeply rooted in governance.