INTRODUCTION
The era of digital transformations has increasingly involved corporations and business entities in digitising their operations. Due to the rise in concerns about data privacy, sector-specific compliance requirements, companies have the requirement to maintain timely regulatory reporting and stringent legal compliance. Noncompliance with the regulatory norms may lead to damage to the goodwill and reputation of the company, with legal liabilities for directors and management under corporate and securities laws.[1]
While the corporate world is facing this backdrop, the emergence of artificial intelligence (AI) has disrupted corporate functioning and regulations. AI systems have both efficiency and accuracy in risk detection, regulatory tracking, and compliance reporting. This sudden shift has integrated the field of Regulatory Technology (RegTech) into the regulatory and compliance procedures, which acts as a bridge to streamline AI and compliance processes.
Although the integration of AI has made the procedure easier, it also raises the issues of legal accountability, data governance, and the relevance of legal judgments by humans, which are all critical aspects in determining fairness and interpretation of regulations. This blog examines the capability of AI to replace human resources for regulatory reporting or whether it merely augments them, by analysing legal frameworks, technological limitations, and institutional practices.
THE CURRENT COMPLIANCE LANDSCAPE
Corporate entities in the modern era have several overlapping reporting requirements imposed by regulatory bodies of finance, tax, labour departments, environmental authorities, and data protection agencies. In the Indian context, under the Companies Act 2013, directors must ensure disclosures of the financial statement, corporate social responsibility (CSR), and compliance with applicable laws.[2] Further, Public companies have compliance requirements under SEBI’s Listing and Disclosure Requirements (LODR) Regulations 2015, including the timely reporting of material events.[3] In addition, the introduction of the Data Protection Act 2023 (DPDP Act) and existing legislations such as the Income Tax Act 1961, Environmental Protection Act 1986 impose periodic, transaction-based, and event-triggered reporting duties.
The corporate legal teams play a strategic role in the interpretation and implementation of the legal obligations. They must stay updated with the regulatory changes and form the internal structure of a corporation under legal norms. The main objective is to advise on legal implications and ensure documentary compliance. The team coordinates with auditors, board committees, and external professional counsels and exercises legal judgment in situations of ambiguity, especially in laws such as SEBI LODR, where laws require a principle-based approach, requiring interpretation in specific corporate contexts. Thus, the legal team balances risks with the corporation’s objectives and forms the backbone of regulatory governance.
THE RISE OF REG–TECH
RegTech refers to the emerging technologies that aim at automating and enhancing regulatory requirements. Technologies currently in use are artificial intelligence (AI), Machine Learning (ML), and natural language processing (NLP).[4] RegTech originally evolved to aid FinTech and was a subset of it, but in the current scenarios, it covers all the sectors offering innovative and very efficient solutions to problems that require highly skilled human resources.
AI tools used currently
AI-powered RegTech has gained several use cases and is attracting global corporations due to its efficiency in tasks such as tracking regulatory change and using NLP to map them to relevant business operations. Platforms such as Ascent RegTech perform such tasks with accuracy and help corporations cut down on human resource costs. At the same time, AI tools like Ayfie and Kira Systems are successfully reducing annual workloads by performing document reviews and incorporating contract intelligence, enabling real-time compliance. Mindbridge AI utilises machine learning (ML) algorithms to perform continuous auditing, helping companies manage the risks of regulatory liability by detecting anomalies in financial reporting.[5]
Advantages over manual compliance
AI has multiple merits when compared with traditional methods of compliance. Its efficiency is not only in assessing present noncompliance risks but also future inconsistencies by processing vast volumes of information in the form of regulatory texts across jurisdictions and identifying the inconsistencies accurately in a fraction of the time. These merits of AI make them preferable over human teams; moreover, AI also has more accountability for audits and decision-making. Even after having so many merits over humans, the question stands whether AI can read between the lines of the actual laws and make ethical judgements regardless of the written compliance functions. To answer that, one must examine the ethical challenges that AI poses.
LEGAL AND ETHICAL CHALLENGES OF REPLACING HUMAN TEAMS WITH AI
Under section 447 of the Companies Act 2013, fraud arising from misreporting can lead to penalties. This section creates accountability and responsibility in legal teams, ensuring compliance, and the same legal accountability is not present with the use of AI for regulatory compliance. Human legal teams bound by the law have a sense of responsibility, and the liability arising from such duties creates better judgment and accountable decisions, including imprisonment of responsible officers in cases of compliance failures.[6] In case of autonomous interpretation of regulatory norms by AI software, there are chances of flawed reports in such cases, deciding the liability is difficult under current legislation.
The principle of vicarious liability can currently be applied in the absence of specific statutory provisions, imposing liability on the corporate entity itself. But in these cases, the jurisdictions that lack algorithmic accountability frameworks, this approach blurs the line between human intent and machine error.[7]
Section 8(9) of India’s Digital Personal Data Protection Act 2023 mandates the use of human oversight over significant automated decision-making processes. The European Union also mandates transparency and human intervention in situations referred to as “high risk” in compliance matters. Though all the necessary steps are taken to avoid the uncertainty of AI-generated audits and reports, the regulatory framework remains fragmented and unclear about the admissibility of such reports.
THE BLACK BOX PROBLEM: EFFECTS ON LEGAL INTERPRETABILITY
The “black box” nature of AI models refers to the use of deep learning and neural networks by the software to generate highly accurate outputs. The persistent challenge that lies here is that the output generated is quite opaque to the users and even the developers of that software. The opacity in output raises concerns about their explanatory ability, as legal actions must be rational, traceable, and accountable. This requirement is that significant decisions affecting the rights or obligations must be accompanied by reasons stemming from the rule of law, which ensures that decisions are subject to judicial review and non-arbitrary, and since the audited reports lack this reasoning, they undermine the principle of legal interpretability.
The SC of India in the case of State of Orissa v. Binapani Dei held that even purely administrative decisions affecting a person’s rights must be subject to procedural fairness, including a duty to give reasons.[8] AI systems that surpass such ability to explain should not be accepted by regulatory bodies and should not be upheld in courts if they lack procedural transparency and logical accountability.
The EU has already incorporated legislations such as Article 22 under the Union’s General Data Protection Regulation (GDPR), which incorporates “right to explanation” for decisions made solely on automated means.[9] In the Indian context, though such significant legislations have not been formulated, section 8(9) of the DPDP Act, 2023 mandates human oversight in such scenarios, which marks the foundation of an accountable framework.
HUMAN-AI COLLABORATION: AUGMENTATION VS. REPLACEMENT
Statutes demand context-sensitive valuation by using terms such as “reasonable” and “material” due to this legal interpretation remaining a fundamental human exercise.
Hence, rather than replacement of one another, scholars argue for an augmentation model that integrates both the components and increases overall efficiency, as AI tools, which are designed to assist legal professionals in increasing speed and accuracy of tasks, are much more suited in the current evolving landscape. Institutions such as the International Bar Association advocate such integration and not replacement in ethical decision-making.[10] The role of AI in such mode is preliminary review, risk scoring, and regulatory tracking, while human legal professionals perform final reviews, legal interpretation, and stakeholder communication. This approach creates a hybrid model where accountability and professional ethics are balanced.
In the current context, AI can only be posed as the co-pilot and cannot be handed over arbitrary control. The skill of nuanced reasoning and contextual awareness still lies with legal professionals and is irreplaceable.
CONCLUSION
AI-powered RegTech has certainly revolutionised the field of compliance and regulations. It also plays a compelling and significant role in enhancing the accuracy and scope of regulatory reporting. Despite these theoretical merits, this technological innovation must confront real-world legal, ethical, and operational realities. Therefore, compliance is not just a mechanistic task; it requires interpretative reasoning and context-sensitive decision-making skills that currently fall beyond the scope of AI technology.
A hybrid approach, where both entities collaborate to achieve efficiency and embrace technological innovation, is essential. Compliance should remain a human-led and AI-assisted endeavour, ensuring that accountability is maintained while upholding the rule of law.
Author(s) Name: Rajat Patel & Salini Tiwari (KIIT School of Law & KIIT School of Law)
References:
[1] OECD, ‘Corporate governance and compliance Risk’ (OECD Publishing, 2016) https://www.oecd.org/en/topics/corporate-governance.html accessed 14 June 2025
[2] Companies Act 2013, ss 134, 135, 149, 177.
[3] SEBI Circular CIR/CFD/CMD/4/2015 (9 September, 2015) https://ca2013.com/clarifications/sebi-circular-circfdcmd42015-dated-15092015/ accessed 14 June 2025
[4] Financial Conduct Authority (FCA), ‘RegTech: A Watershed Moment?’ (FCA, 2020) https://webarchive.nationalarchives.gov.uk/ukgwa/20240308200606/https://www.fca.org.uk/insight/regtech-watershed-moment accessed 15 June 2025
[5] Xapien, ‘How AI is impacting RegTech, and why it’s the future for firms’ https://xapien.com/insights/regulation/how-ai-is-impacting-regtech accessed 15 June 2025.
[6] CBI v. Ramesh Gelli (2016) 3 SCC 788.
[7] Brent Daniel Mittelstadt et al, ‘The Ethics of Algorithms: Mapping the Debate’ (2016) 3(2) Big Data and Society 1.
[8] State of Orissa v. Binapani Dei AIR 1967 SC 1269.
[9] Regulation (EU) 2016/679 of the European Parliament and of the Council [2016] OJ L119/1 (General Data Protection Regulation), art 22.
[10] International Bar Association, ‘Artificial Intelligence and the Legal Professional’ (IBA Legal Policy & Research Unit, 2021) https://www.ibanet.org/document?id=AI-journal-report accessed 16 June 2025
