The AI Effect - Rethinking Governance And Risk In Corporate Law

Author: LawSphereX

Category: Corporate & Insolvency

Published: 2026-08-23T21:13:23.146319+00:00

Updated: 2026-08-30T07:05:21.177Z

THE AI EFFECT: RETHINKING GOVERNANCE AND RISK

IN CORPORATE LAW

Madhu Srikar Chintalapudi, BA LLB, BMS College of Law, Bengaluru

ABSTRACT

Artificial Intelligence (AI) has steadily advanced from experimental novelty to mainstream utility across industries as diverse as aviation, healthcare, finance, and cybersecurity. Corporate law has not remained untouched by this trend. Law firms and corporate legal departments increasingly employ AI-driven tools to streamline repetitive tasks, draft and review contracts, assist in compliance, and conduct due diligence. This integration promises significant efficiency gains, yet it also presents complex legal, ethical, and governance challenges. This paper examines the intersection of corporate law and AI through two interrelated perspectives. First, it considers the practical adoption of AI by corporates in India, focusing on benefits and potential risks ranging from data privacy concerns to the fiduciary obligations of directors and managers.

Second, it situates these issues within a comparative legal framework by analysing recent doctrinal developments in the United States particularly the Anthropic and Meta litigation which tested whether training language models on copyrighted material qualifies as “transformative use” under the doctrine of fair use. Although these U. S. judgments provide a permissive view of AI training, they underscore unresolved risks of copyright infringement, market substitution, and liability. Drawing lessons from these cases, the paper argues that Indian corporate entities must approach AI adoption with caution. Unlike the United States, India’s copyright law recognises the narrower doctrine of “fair dealing. ” Without explicit legislative exceptions for text and data mining, corporations face heightened risk when deploying AI models that rely on copyrighted inputs.

The paper concludes with recommendations for corporate governance frameworks, contractual safeguards with AI vendors, and hybrid compliance strategies that balance innovation with accountability.

Introduction

The last decade has witnessed a technological transformation that rivals any in modern history. Artificial Intelligence, once confined to academic laboratories, now powers search engines, chatbots, financial trading platforms, and autonomous vehicles. Its appeal lies in speed, accuracy, and the promise of reducing human effort across domains. Corporate law, often viewed as conservative in its embrace of innovation, has nonetheless been drawn into this current. Legal departments of multinational corporations and leading law firms in India are increasingly experimenting with AI-driven tools. Tasks such as reviewing standard-form contracts, conducting legal research, or scanning thousands of documents during due diligence are no longer exclusively performed by junior associates. Instead, AI systems are deployed to ease the workload and improve efficiency.

Yet the adoption of AI in corporate law is not merely a matter of operational convenience. It raises legal and ethical questions: How should boards discharge their fiduciary duties when deploying such tools? What contractual safeguards must be negotiated with AI vendors? How does Indian copyright law treat the data that powers these systems? To address these questions, this paper blends practical corporate law considerations with insights from emerging global jurisprudence. The analysis of two recent U. S. copyright cases involving Anthropic1 and Meta2 demonstrates how courts are beginning to reconcile AI training with established doctrines of fair use. These foreign precedents are instructive for Indian corporate actors, but they also highlight the divergence in legal regimes. By comparing the U. S.

approach with India’s more restrictive “fair dealing” standard, the paper explores the legal, compliance, and governance challenges that corporations must navigate. AI in Corporate Legal Practice The adoption of AI in corporate law is largely motivated by efficiency. Law firms and corporate legal teams are under constant pressure to deliver more at lower cost. AI-powered tools offer solutions in several areas:

1 Bartz v. Anthropic (3:24-cv-05417 (N.D. Cal.))

2 Kadrey v. Meta Platforms, Inc. is No. 3:23-cv-03417-VC (N.D. Cal.)

1. Contract Lifecycle Management - AI systems can review voluminous contracts, flag deviations from standard clauses, and even suggest amendments. This reduces turnaround time and frees lawyers to focus on negotiation strategy. 2. Regulatory Compliance - Corporations must navigate complex frameworks under the Companies Act, SEBI regulations, and sector-specific norms. AI tools can monitor compliance obligations, track deadlines, and generate alerts for filings. 3. Due Diligence in M&A - Traditionally a labour-intensive task, due diligence can be accelerated by AI platforms that sift through thousands of documents, classify risks, and highlight red flags. 4. Litigation Support - AI tools assist in e-discovery by identifying relevant documents from large datasets, thereby reducing costs and minimising oversight. Despite these advantages, risks abound.

AI systems may generate inaccurate outputs, compromise confidential information, or perpetuate biases present in training datasets. These risks have direct implications for corporate governance. If a board of directors approves the use of AI tools without adequate due diligence, they may be accused of breaching their duty of care under company law. Similarly, if AI systems inadvertently leak trade secrets or client data, corporations could face liability under contractual and statutory regimes. The question is therefore not whether AI will transform corporate law it already has but how legal systems and governance frameworks can adapt to manage its risks. Comparative Jurisprudence: Lessons from the U. S. Recent U. S. cases provide valuable insights into how courts are beginning to grapple with the copyright implications of AI training. Two notable litigations Bartz v.

Anthropic and Kadrey v. Meta tested whether training large language models on copyrighted works constituted infringement or could be justified under the doctrine of fair use. The Anthropic Case In this case, a group of authors, including Andrea Bartz, alleged that Anthropic copied their works without permission to train its AI model, Claude. They argued that pirated and scanned copies were stored in a “digital library,” violating their copyrights. Judge William Alsup of the Northern District of California ruled in June 2025 that using books to train AI models was “exceedingly transformative” and therefore permissible under fair use. He reasoned that the training process was akin to how human writers learn from reading: not to reproduce but to create new works.

However, the court distinguished between training and storage, holding that maintaining pirated works could not be justified as fair use. The Meta Case In Kadrey v. Meta, thirteen authors, including Sarah Silverman and Junot Díaz, challenged Meta’s use of their works to train its LLaMA models. Judge Vince Chhabria likewise found that AI training was highly transformative. The plaintiffs could not show market harm, a crucial factor under fair use. Although Meta had used pirated copies, the court deemed the use justified because the purpose was fundamentally different from that of the original works. Doctrinal Themes Both cases turned on the “transformative use” doctrine, developed in earlier precedents such as Authors Guild v. Google3 (Google Books, 2015) and Google v. Oracle4 (2021).

Courts recognised that AI training serves a purpose distinct from the consumption of books: it teaches models linguistic patterns. Yet both judgments also imposed limits: if AI outputs reproduce copyrighted material directly or substitute the market for original works, fair use will not apply. Comparative Perspective The U. S. approach, anchored in a flexible fair use doctrine, is relatively permissive. By contrast, Indian copyright law recognises only limited fair dealing exceptions, primarily for research, private study, and criticism. There is no explicit exception for text and data mining. Consequently, if an Indian corporation were to train an AI model on copyrighted books without permission, it would likely constitute infringement. This divergence underscores the heightened legal risks for Indian companies that adopt AI tools without adequate licensing or safeguards.

3 Authors Guild v. Google, Inc., No. 13-4829 (2d Cir. 2015)

4 Google LLC v. Oracle Am., Inc. 141 S. Ct. 1163 (2021)

Policy and Governance Recommendations To responsibly harness AI in corporate law, corporations should adopt multi-layered governance strategies: 1. Internal AI Governance Frameworks – Establish dedicated committees to oversee AI adoption, monitor risks, and ensure compliance with intellectual property and data protection laws. 2. Hybrid Compliance Models - Combine reliance on permissible fair use/fair dealing with selective licensing of high-risk content, such as books and news media. 3. Robust Contract Management - Draft contracts with vendors that include indemnities, warranties, and audit rights. 4. Transparency and Disclosure - Maintain detailed records of datasets used, policies adopted, and compliance measures undertaken. This demonstrates good faith in case of litigation. 5. Ongoing Monitoring - Track international legal developments, particularly in the U. S.

, EU, and UK, where copyright regimes are evolving rapidly. Conclusion AI is no longer a futuristic concept in corporate law it is already embedded in practice. Yet the enthusiasm for efficiency must be tempered with caution. The Anthropic and Meta cases in the United States illustrate how courts are willing to view AI training as transformative, but they also highlight unresolved risks. In India, the absence of a broad fair use doctrine makes corporations more vulnerable to copyright claims. For directors, lawyers, and compliance officers, the message is clear: AI can and should be embraced, but only within a framework of legal accountability. Fiduciary duties require boards to adopt safeguards, negotiate protective contracts, and maintain oversight.

The future of corporate law will undoubtedly be intertwined with AI, but the balance between innovation and responsibility will determine whether that future is sustainable.