Home / Volume 8, Issue 3 / Algorithmic Offenders: When Corporations Commit Cybercrimes via AI Open access · CC BY-NC 4.0
Research Paper Volume 8 Issue 3 1203 - 1216 May 21, 2025

Algorithmic Offenders: When Corporations Commit Cybercrimes via AI

Lead author · Corresponding
Khush Dalbir
LL.M (Cyber Law) Student at IILM University, Greater Noida, India
View PDF Full text DOIhttps://doij.org/10.10000/IJLMH.119809
Abstract

The increasing reliance on Artificial Intelligence (AI) by corporations has revolutionised business operations, decision-making, and digital engagement. However, it has also introduced unprecedented avenues for corporate misconduct and cybercrime. From automated phishing tools and unethical data mining to algorithmic manipulation in financial markets and targeted disinformation, AI systems are now being weaponised—either deliberately or negligently—by corporations to gain unlawful advantages. This paper examines the phenomenon of AI-enabled corporate cybercrimes, identifies the regulatory and legal gaps in the Indian context, and explores the challenge of assigning liability when algorithms become the offenders. Through doctrinal analysis, comparative legal frameworks, and real-world case illustrations, the study proposes a robust policy and legal structure to prevent and regulate such emerging threats. It argues that India must move swiftly to enact algorithmic accountability legislation and empower regulatory agencies to audit, monitor, and penalise AI-based corporate misconduct.

Type
Research Paper
Information
International Journal of Law Management and Humanities, Volume 8, Issue 3, Page 1203 - 1216
DOI: https://doij.org/10.10000/IJLMH.119809
Creative Commons
CC BY-NC 4.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution–NonCommercial 4.0 International (CC BY-NC 4.0) (https://creativecommons.org/licenses/by-nc/4.0/), which permits remixing, adapting, and building upon the work for non-commercial use, provided the original work is properly cited.
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Copyright © IJLMH 2026
Disclaimer
The views and opinions expressed in this manuscript are those of the author(s) alone and do not reflect the views, policies, or position of the Journal.

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