Vipul Prakash, Founder & CEO of FireAI, talks about AI Governance in the age of regulation and how businesses can stay ahead.
The growth of human civilization is at an inflection point, particularly with the advent of Artificial Intelligence. The narrative around AI has focused primarily on innovation velocity for years, discussing aspects such as how fast we can build, deploy, and scale. However, this narrative is shifting considerably at the moment, with the question around boardrooms becoming what AI should be used for, and how its efficacy can be proven. What’s powering this shift in narrative is rather simple, as regulation and aspects like ethical implementation have arrived.
Since 2024, we have witnessed significant changes in the stance of governments and policymakers regarding AI. For instance, the European Union has formalized its AI Act, while India has released comprehensive AI Governance Guidelines. Globally, frameworks that previously focused on transparency, fairness, and accountability have changed their emphasis from voluntary principles to legal compliance. This has impacted businesses that were of the opinion that compliance was someone else’s problem, as they are also realizing that it affects everyone.
For Indian businesses, it is of paramount interest to understand that the regulations we are witnessing at the moment are not focused on overshadowing innovation, but towards channeling it. For this reason, the firms that do not resist governance will thrive in this ecosystem, embedding it into their operations from the get go.
The Regulatory Wave
In November 2025, India’s Ministry of Electronics and Information Technology released the India AI Governance Guidelines 2025, a comprehensive framework that signals India’s commitment to responsible AI development. Unlike the EU’s rigid, prescriptive AI Act, India has taken a more pragmatic approach. It is using what is known widely as a “techno-legal model,” where technology and regulation co-evolve.
In practice, this means compliance isn’t a box you check at the end. It’s embedded into how systems work as audit trails, consent frameworks, and incident reporting are all built in. Simultaneously, the EU AI Act entered its first enforcement phase on February 2, 2025. By 2026, it will be fully enforceable globally. For Indian companies with any European presence, and increasingly, for any company that sells or operates across borders, this matters enormously. Non-compliance can result in fines ranging from €7 million to €35 million, or 1-7% of global turnover, whichever is higher.
India’s Data Protection and Digital Privacy Act (DPDP) 2023 is entering its enforcement-heavy phase in 2025. Mandatory breach notifications, expanded definitions of “significant data fiduciaries,” and closer collaboration between CERT-In and the Data Protection Board as this is the new operating environment.
The Three Pillars of Responsible AI Governance
Effective AI governance rests on three pillars — Transparency, accountability, and verifiability. Transparency, in short, remains the right to understand, and where most organizations stumble first. Transparency in AI does not mean divulging the proprietary secrets, but being able to explain, in plain language, why the system made a particular decision. When an AI model rejects a loan applicant, flags an anomaly in financial data, or recommends a customer for retention, it needs to articulate the reason behind the decision, not just the fact that it did.
Regulators now require this. The EU AI Act mandates that high-risk AI systems provide explanations of their decisions. India’s governance guidelines emphasize the principle of explainability as foundational. And across the globe, customers increasingly expect it. In practicality, transparency means clear model documentation, decision explainability, and plain-language summaries. Every AI system should come with a model card that outlines its purpose, training data, limitations, and known biases. Furthermore, when the system makes a prediction or recommendation, it should be able to trace back through the logic to explain the output. Additionally, technical explainability is important, but so is explaining decisions in language a non-technical stakeholder can understand.
With accountability, the chain of responsibility becomes a crucial cog in the wheel. For instance, audit trails are becoming the regulatory standard, with the EU AI Act mandating them for high-risk systems. Anticipated US regulations (from the SEC, FTC, and others) will likely require them, along with India’s CERT-In and the Data Protection Board, now collaborating to investigate incidents, which means audit trails are no longer optional for companies in India either.
What an audit trail answers is a critical question: if something goes wrong, can it be proven what happened and who was responsible? This means every decision, input, and change to the system must be logged with precision. Alongside, anyone reviewing the audit trail should be able to reconstruct exactly how a decision was made, using exactly the same data and model version that existed at that time. It also means clear ownership, as in the details regarding deployment, approval, and monitoring.
The final pillar is verifiability. Governance cannot be integrated in aspects that cannot be measured. This is where bias detection comes in. Regulators expect businesses to proactively detect bias in AI systems before they harm people, monitor ongoing performance to ensure systems don’t drift into unfair behavior, and document remediation when bias is found. Tools like IBM AI Fairness 360, Microsoft Fairlearn, and others can automatically audit your models for bias, fairness, and performance drift. But what’s important is that verifiability is no longer a compliance check, but a business risk issue. If an AI system makes systematically unfair decisions, even unintentionally, problems arise. It exposes businesses to regulatory action, lawsuits, reputational damage, and lost customers.
Business Case for Compliance
Companies that embed governance into their AI systems early avoid penalties and move faster. This is because with governance built in, businesses don’t need months of audits before deploying a new model. Furthermore, they do not need to face regulatory surprises or spend time and money defending decisions that lack explainability.
Organizations that have implemented robust compliance frameworks report 38% fewer regulatory violations, compared to those without proper frameworks. They also have faster time-to-market for AI-driven products and features, and garner stronger customer trust as their systems are transparent and fair. They also reduce operational risk with fewer surprise audits and incidents.
Staying Ahead
Businesses can stay ahead in regulation in three phases. In the first phase, they must work with Compliance Readiness, meaning audit trails are built, models are documented, and bias is tested. In this phase, they must establish who owns responsibility for each AI system.
In phase 2, the governance is integrated as businesses move beyond checks and balances. In this phase, compliance is integrated within the development process. This phase also ensures that models can’t go live without passing fairness tests, explainability thresholds, and audit trail requirements.
In the third and final phase, once governance is standard in your organization, businesses can operate confidently in regulated markets, expand to new geographies without compliance fears, build products with higher trust and customer confidence, while also moving faster than competitors.
Proactive Governance
If founders are waiting for the regulators to issue an enforcement action before taking governance seriously, they are already behind. The companies that will lead in the next age of AI are those that are building responsibly today. That means these businesses are auditing their current AI systems for bias, explainability, and traceability. They are also establishing clear governance processes for new AI deployments, investing in tools and platforms that make automatic compliance, and building a culture where responsible AI is how they operate.
Regulation enables innovation and creates a level playing field. It builds customer trust while also reducing risk. For Indian businesses, it’s an opportunity to become the trusted providers of AI in an ecosystem that demands responsible intelligence. Businesses that are moving now will shape what responsible AI looks like for the next decade.