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White Black Legal – International Law Journal · ISSN 2581-8503
Treatment of Artificial Intelligence Enterprises Under the Insolvency and Bankruptcy Code 2016
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Abstract
TREATMENT
OF ARTIFICIAL INTELLIGENCE ENTERPRISES UNDER THE INSOLVENCY AND BANKRUPTCY CODE
2016
AUTHORED
BY - MADHUR ANAND,
AMITABH
KUMAR SAXENA & RADHIKA BADERIA
Abstract
The
Insolvency and Bankruptcy Code 2016 (“IBC”) was drafted for an economy of
factories, inventory, and immovable property. Artificial intelligence
enterprises invert this premise, their commercial worth resides in “asset
stacks,” interdependent bundles of training datasets, model weights, application
programming interfaces, licensed software, and user-contributed data, few of
which the corporate debtor owns outright. This paper examines whether India’s
insolvency framework can accommodate such enterprises when they fail. It
argues, first, that the IBC’s ownership-centric conception of “property” under
section 3(27) and the resolution professional’s custodial mandate under section
18(f) sit uneasily with the fragmented, licence-dependent character of AI asset
stacks. Second, drawing on the Supreme Court’s commercial-interest reasoning in
Victory Iron Works Ltd v Jitendra Lohia and State Bank of India v
Union of India, it contends that AI asset stacks should nonetheless form
part of the insolvency estate as transferable commercial interests, triggering
a functional rather than possessory duty of preservation on the resolution
professional. Third, it draws comparative lessons from the United States’
consumer-privacy-ombudsman mechanism and the European Union’s lifecycle
approach to AI governance. The paper concludes with recommendations for an
AI-native insolvency framework calibrated to India’s Digital Personal Data
Protection Act 2023.
Introduction
In
May 2025, Builder.ai, a London-headquartered, Microsoft-backed
enterprise once valued at USD 1.5 billion and marketed as an “AI-native”
software-development platform, collapsed into insolvency after a creditor
seized funds from its accounts, and subsequently entered Chapter 7 bankruptcy
proceedings in the United States.[1]
The company’s downfall, precipitated by revelations that much of its advertised
automated development process was in fact performed by hundreds of human
engineers, has become a cautionary emblem for a sector whose valuations have
often outpaced its balance sheets.[2]
Builder.ai is not an isolated episode. Since 2025, at least eighteen Indian AI
start-ups, including Subtl.ai, have entered bankruptcy or insolvency
proceedings, even as investment in India’s AI start-up ecosystem reached
approximately USD 1.48 billion in the first quarter of 2026 alone, accounting
for nearly thirty-eight per cent of all start-up funding in that period, before
equity funding is reported to have contracted by roughly a third shortly
thereafter.[3] As capital tightens and
the artificial intelligence sector matures past its initial hype cycle, a
growing number of AI enterprises will be brought within the ambit of India’s
IBC framework.
This
trend exposes a structural mismatch between the Code and the enterprises it is
now being asked to resolve. The IBC was conceived for an economy in which a
corporate debtor’s value is embodied in identifiable, ownable assets, land,
plant, machinery, inventory, and receivables, capable of being taken into
custody, preserved, and eventually transferred by a resolution professional
(“RP”) so as to maximise returns for creditors. AI-native enterprises invert
this premise. Their commercial worth lies not in tangible capital but in what
this paper terms the “AI asset stack,” an integrated, interdependent bundle
comprising proprietary source code, trained model weights, licensed and
user-contributed datasets, application programming interfaces (“APIs”), cloud-compute
arrangements, and third-party software components. Ownership over these
constituent elements is rarely unified in the corporate debtor. Datasets may be
licensed from third parties or drawn from user interactions subject to
contractual and statutory restrictions; APIs and cloud infrastructure typically
operate under revocable service agreements; and model weights may themselves be
derivative of open-weight or licensed foundation models. The result is that the
very asset which makes an AI company valuable as a going concern may not
qualify as “property” which the corporate debtor can be said to “own” within
the meaning the Code presupposes.[4]
This
paper addresses three connected questions. First, does the emergence of AI
asset stacks expose a conceptual limitation in the IBC’s ownership-centric
notion of insolvency property? Second, if AI asset stacks nonetheless ought to
form part of the insolvency estate, on what legal basis, and what obligations
does this impose on the RP in preserving, valuing, and realising them? Third,
what legislative or regulatory reforms would allow India’s insolvency framework
to accommodate AI-native enterprises without abandoning the Code’s foundational
commitments to property rights, contractual sanctity, and data protection?
The Anatomy of an AI Insolvency Wave
The
scale of the present problem is best understood against the backdrop of the
extraordinary expansion of India’s AI ecosystem. The global AI dataset market
alone is estimated at USD 4.44 billion, reflecting the extent to which data,
rather than physical infrastructure, has become the primary input of value
creation for AI enterprises.[5]
Indian start-ups attracted roughly USD 1.48 billion in AI-related investment in
the first quarter of 2026, representing an unusually large share of aggregate
start-up funding for that period. Yet this expansion has been accompanied by a
sharp correction, a reported thirty-three per cent drop in equity funding
within months of the funding peak has coincided with a wave of failures among
AI-native start-ups, of which Subtl.ai is only the most visible example within
a cohort of at least eighteen insolvencies.
The
structural fragility underlying this wave is not accidental. Many AI-native
enterprises operate on business models characterised by high fixed compute
costs, thin or negative gross margins on inference, and valuations built
substantially on projected rather than realised revenue. Builder.ai’s
trajectory is illustrative, the company raised over USD 445 million from
investors including Microsoft and the Qatar Investment Authority, yet its core
commercial promise, an AI assistant capable of autonomously generating
software, was substantially reliant on manual engineering labour, a discrepancy
that eroded investor confidence once disclosed and ultimately precipitated its
collapse.[6]
Whatever the cause of an individual AI enterprise’s distress, whether
overstated automation, compute-cost overruns, or a broader market correction,
the consequence for insolvency law is the same, an increasing number of
corporate debtors whose primary commercial value lies in intangible,
data-dependent, and contractually fragmented resources rather than in
conventional balance-sheet assets. It is this consequence, rather than the
causes of AI-sector distress as such, that occupies the remainder of this paper.
The IBC’s Ownership-Centric Architecture and Its Limits
The
IBC’s architecture proceeds from the assumption that a corporate debtor’s
estate consists of assets capable of being identified, possessed, and
transferred. The Preamble frames the Code’s object as the “maximisation of
value of assets” of persons subject to its provisions, within a time-bound
process designed to balance the interests of all stakeholders.[7]
Section 3(27) defines “property” expansively to include “money, goods,
actionable claims, land and every description of property situated in India or
outside India and every description of interest including present or future or
vested or contingent interest arising out of, or incidental to, property”.[8] On
its face, this definition is broad enough to encompass intangible and
contingent interests, including, potentially, data and software assets.
The
more restrictive gloss emerges at the level of implementation. Section 18(f)
empowers the interim resolution professional to take control and custody of
“assets” over which the corporate debtor has “ownership rights”, whether or not
such assets are in the corporate debtor’s possession.[9]
Crucially, the accompanying Explanation to section 18 excludes from this
custodial mandate assets owned by a third party but held by the corporate
debtor under contractual arrangements, including bailment.[10]
Read together, sections 3(27) and 18(f) create an architecture in which the
RP’s statutory authority to control an asset is contingent upon the corporate
debtor’s ownership of that asset, a standard that sits uneasily with an AI
enterprise whose commercial value typically depends on components it does not
exclusively own.
Judicial
Elaboration: From Possession to Commercial Interest
Indian
courts have, however, already begun to soften the rigidity of a purely
possessory or title-based reading of “asset” under the Code. In Victory Iron
Works Ltd v Jitendra Lohia[11],
the Supreme Court considered whether development rights held by a corporate
debtor over a 10.19-acre parcel of land, rights that fell short of full
ownership of the underlying land, could nonetheless constitute “property” and,
therefore, an “asset” for the purposes of the Code. The Court held that such
development rights, being a bundle of contractual and proprietary interests
created for valid consideration, satisfied the expansive definition of
“property” under section 3(27) and accordingly qualified as “assets” under
sections 18(f) and 25(2)(a).[12]
Significantly, the Court also clarified that the Explanation excluding
third-party-owned assets under section 18 is confined textually to that section
and does not extend to section 25, which governs the RP’s broader duty to
preserve and protect the corporate debtor’s assets during the resolution
process.[13] The judgment therefore
signals a doctrinal shift away from formal title as the exclusive touchstone of
insolvency “property”, toward a substance-based enquiry into whether the
corporate debtor holds a commercially exploitable bundle of rights.
A
parallel development is visible in State Bank of India v Union of India[14],
where the Supreme Court examined whether telecommunications spectrum, held by a
corporate debtor under a licence granted by the Government of India rather than
through outright ownership, could be treated as an “asset” realisable within
corporate insolvency proceedings. While the Court was careful to preserve the
primacy of sector-specific telecommunications law governing the trading and
transfer of spectrum, and cautioned that characterising spectrum as an “asset”
under the IBC could not override those sectoral controls, the very fact that
the question required extended consideration illustrates that licensed,
non-owned, yet commercially indispensable interests are increasingly presenting
themselves for classification within the insolvency estate. Read together, Victory
Iron Works and the spectrum litigation in State Bank of India v Union of
India suggest an emerging judicial willingness to locate the relevant
insolvency asset in the corporate debtor’s integrated commercial interest,
rather than in undivided legal title over each constituent component, subject
always to compliance with the specific regulatory regime, if any, governing
that component. It is this reasoning, this paper argues, that offers the most
workable doctrinal foundation for treating AI asset stacks as insolvency
property.
AI Asset Stacks- Composition, Value, and Fragmented Ownership
An
AI enterprise’s most valuable resource is rarely its computing hardware or
office premises but rather the accumulated product of years of interaction with
users, licensing arrangements, and internally generated technical assets. This
paper uses the term “AI asset stack” to describe the integrated bundle
typically comprising: (i) proprietary source code and system architecture; (ii)
trained model weights, which may themselves be derived from open-weight or
third-party-licensed foundation models; (iii) training, validation, and testing
datasets, drawn from a combination of customer-generated interactions, publicly
available material, licensed third-party data, and synthetic data generated by
the AI system itself; (iv) APIs and integrations connecting the system to
external services; and (v) cloud-compute and infrastructure contracts without
which the system cannot be operated or retrained. It is the composite operation
of these elements, rather than any single component in isolation, that
generates the commercial value a resolution applicant would seek to acquire.
This
composite character generates two distinct problems for insolvency valuation
and administration. The first is fragmented ownership. Unlike a factory or a
fleet of vehicles, an AI asset stack rarely consists of property owned
exclusively by the corporate debtor. Some datasets may be proprietary; others
are licensed subject to field-of-use or non-transferability restrictions, or
comprise personal data collected from users subject to consent-based
restrictions on further processing or transfer. Model weights may be subject to
open-source licence conditions restricting commercial redistribution, or may
themselves be fine-tuned derivatives of a third party’s foundation model,
licensed on terms that could be rendered void or voidable upon a change of
control. Cloud-compute and API contracts typically contain
termination-on-insolvency clauses, meaning that the technical infrastructure
required to operate the stack may lawfully be withdrawn by a counterparty at
precisely the moment a resolution applicant most needs continuity.
The
second problem is valuation instability. Data assets, unlike land or machinery,
are not fungible, do not depreciate on any standard schedule, and derive much
of their value from network effects and continuous refresh, a dataset frozen at
the moment of insolvency may lose commercial relevance within months if not
maintained, retrained against, or supplemented. There is presently no settled
methodology under Indian valuation practice, whether under the Companies
(Registered Valuers and Valuation) Rules 2017 or under the regulations framed
by the Insolvency and Bankruptcy Board of India (“IBBI”), for valuing an
interdependent bundle of licensed, proprietary, and personal-data components
whose individual components may have negative, positive, or contingent value
depending on whether they are severed from or kept within the stack. The
absence of clear legal classification compounds this valuation difficulty:
because the IBC does not recognise the “AI asset stack” as a distinct category
of insolvency asset, its constituent elements are left to fall into
pre-existing legal silos, intellectual property, contractual rights, licensed
data, and third-party assets, each attracting a different treatment under the
Code, with no provision addressing how their interdependence ought to be
preserved or realised as a single commercial unit.
Locating AI Asset Stacks Within the Insolvency Estate
Despite
this absence of express recognition, this paper argues that AI asset stacks
should nonetheless form part of the insolvency estate, and that their exclusion
would defeat the Code’s central objective of maximising value for creditors.[15]
The relevant enquiry, drawing on Victory Iron Works, is not whether the
corporate debtor holds undivided title to every underlying dataset, model, or
piece of infrastructure, but whether it possesses a transferable commercial
interest capable of preservation and realisation during the resolution process.
Applying this standard, an AI enterprise typically possesses precisely such an
interest: even where individual datasets are licensed, where cloud
infrastructure operates under contract, and where certain model components are
themselves derivative works, the corporate debtor holds an integrated bundle of
proprietary and contractual rights that, taken together, enables it to develop,
deploy, and commercialise the AI system as a going concern. It is this integrated
commercial interest, the right to operate, exploit, and further develop the AI
asset stack as a whole, rather than absolute ownership of each underlying
component, that constitutes the relevant insolvency asset.
This
approach is consistent with, rather than a departure from, the Code’s existing
treatment of licensed and contractual interests elsewhere in its jurisprudence.
In Gujarat Urja Vikas Nigam Ltd v Amit Gupta[16],
the Supreme Court recognised the Adjudicating Authority’s jurisdiction to
restrain the termination of a contract critical to a corporate debtor’s
survival during the corporate insolvency resolution process, where such
termination would result in the corporate debtor’s “corporate death,” a
recognition that the Code’s object of preserving going-concern value can, in
appropriate cases, qualify the strict contractual entitlements of
counterparties. The same logic supports treating licensed API access, foundation-model
licences, and cloud-infrastructure contracts as components of the insolvency
estate’s commercial value, notwithstanding that the corporate debtor does not
“own” them in the section 18(f) sense, at least for the limited purpose of the
RP’s duty to preserve and protect the value of the corporate debtor’s property.[17]
Reconceptualising the Resolution Professional’s Duty of Custody and
Control
Once
the AI asset stack is recognised as the relevant insolvency asset, a further
difficulty arises regarding the content of the RP’s statutory obligation to
take “custody and control” and to preserve the value of the corporate debtor’s
property.[18] Unlike a factory or item
of machinery, whose value can typically be preserved through physical security
and routine maintenance, an AI asset stack depreciates through inaction. Model
weights become stale without periodic retraining; datasets lose relevance
without continuous refresh; and access to third-party APIs, cloud
infrastructure, and licensed model components can lapse automatically upon
non-payment or the occurrence of an insolvency-related event of default.
Physical control over servers, source-code repositories, or office premises
does not, by itself, preserve the commercial value the insolvency process is
meant to protect.
The
statutory obligation to take “custody and control” must therefore be read
functionally rather than physically for AI-native debtors. This entails, at
minimum, maintaining continuity of API access and cloud-compute subscriptions
during the moratorium period[19];
complying with the terms of third-party data and model licences so that they
are not terminated for breach during the resolution process; safeguarding the
integrity, security, and, where technically necessary, the continued limited
processing of proprietary and user-contributed datasets; and preserving
technical documentation and version-control records sufficient to allow a
prospective resolution applicant to assess and continue operating the stack.
Failure to discharge this functional obligation risks precisely the erosion of
value that section 25(2)(a) is designed to prevent, not because the RP has
failed to physically secure any tangible asset, but because the RP has failed
to maintain the operating conditions on which an intangible, interdependent asset
stack depends.
A
further layer of complexity arises from the personal data frequently embedded
within an AI asset stack. India’s Digital Personal Data Protection Act 2023
(“DPDP Act”) imposes continuing obligations on a “Data Fiduciary,” the entity
determining the purpose and means of processing personal data, to process
personal data only for a lawful purpose to which the data principal has
consented, and to maintain its accuracy and security.[20]
These obligations, grounded in the constitutional recognition of informational
privacy as an incident of the right to life and personal liberty under article
21 of the Constitution of India,[21]
do not lapse merely because the Data Fiduciary has entered insolvency. Where a
resolution plan or a liquidation sale contemplates the transfer of a dataset
containing personal data to a resolution applicant or acquirer, the transfer
itself constitutes further “processing” under the Act and must be justified by
reference to the purpose for which the data principal originally consented,
subject to the Act’s provisions on cross-border and onward transfer of personal
data.[22]
An RP administering an AI enterprise’s insolvency therefore effectively steps
into obligations analogous to those of a Data Fiduciary for the duration of the
resolution process, even though the Code itself contains no provision
addressing this overlap. A resolution plan that ignores this overlay, for
instance, by proposing to transfer a customer dataset to an unrelated acquirer
for a purpose inconsistent with the original terms of collection, would expose
the successful resolution applicant, and potentially the RP, to liability under
the DPDP Act notwithstanding its approval under the IBC.
Comparative Perspectives
United
States: The Consumer Privacy Ombudsman and Section 363 Sales
The
United States has, since 2005, possessed a statutory mechanism directly
addressing the tension between the sale of data assets in bankruptcy and the
privacy commitments under which that data was originally collected. The
mechanism emerged from the bankruptcy of Toysmart.com, an online retailer that
had collected children’s personal information under a privacy policy promising
never to share it with third parties, but which subsequently sought to sell
that customer database as part of its bankruptcy estate.[23]
Following intervention by the Federal Trade Commission and a contested
settlement, Congress responded through the Bankruptcy Abuse Prevention and
Consumer Protection Act 2005, which inserted section 332 into the Bankruptcy
Code, requiring the appointment of a consumer privacy ombudsman before a debtor
may sell or lease personally identifiable information inconsistent with its
stated privacy policy, and amended section 363(b)(1) to restrict such sales
accordingly.[24] This mechanism has since
been applied to precisely the kind of data-intensive, technology-driven
enterprise this paper is concerned with in the 2025 bankruptcy of the
genetic-testing company 23andMe, a court-appointed consumer privacy ombudsman
examined the proposed transfer of the company’s genetic and personal data
holdings to an affiliated acquirer, assessing the adequacy of the acquirer’s
privacy and security commitments before the transfer could proceed.[25]
The American experience demonstrates a workable institutional response to the
precise problem, which is rather than leaving data protection compliance to the
general discretion of the insolvency officeholder, the law interposes a
dedicated, technically informed reviewer whose role is confined to certifying
that a data transfer respects the terms under which the data was originally
collected.
European
Union: The Lifecycle Approach of the AI Act
The
European Union’s Artificial Intelligence Act does not address insolvency at
all, yet it offers an instructive conceptual parallel. Article 10 of the Act
imposes on providers of high-risk AI systems continuing data-governance
obligations extending across the system’s lifecycle requiring, among other things, that training,
validation, and testing datasets be documented, version-controlled, and
periodically assessed for continued relevance, completeness, and freedom from
error, rather than treated as a static asset fixed at the point of development.[26]
Although framed as a product-safety and fundamental-rights instrument rather
than an insolvency measure, the Act’s central premise, that an AI system
derives its value and its compliance status from continuous, lifecycle-long
management of its data and model components rather than from a one-off transfer
of static property, maps closely onto the functional conception of the RP’s
custodial duty. It reinforces the conclusion that any insolvency framework
applied to AI enterprises must be designed around the maintenance of ongoing
processes, not merely the transfer of discrete objects.
Towards an AI-Native Insolvency Framework for India
Drawing
on the preceding analysis, this paper proposes four specific reforms to align
the IBC with the commercial reality of AI-native enterprises. First, the IBBI
should, by regulation or circular, expressly recognise the “AI asset stack” as
a distinct category of insolvency asset, defined by reference to the integrated
bundle of proprietary, contractual, and data-related rights that enable a
corporate debtor to operate an AI system, rather than by reference to title
over each constituent component. Such recognition would formalise the
commercial-interest approach already implicit in Victory Iron Works and
would give RPs a clear statutory anchor for including such stacks within
information memoranda and resolution plans.
Second,
the Insolvency and Bankruptcy Board of India, in coordination with the
Institute of Chartered Accountants of India and the Insolvency Professional
Agencies, should develop valuation guidance specific to AI-native debtors under
the Companies (Registered Valuers and Valuation) Rules 2017, addressing the
treatment of licensed and personal-data components, the depreciation profile of
stale or unmaintained datasets, and the discount to be applied where key
infrastructure or model-licensing contracts are subject to
termination-on-insolvency clauses.
Third,
drawing directly on the American consumer privacy ombudsman model, the CIRP
Regulations should be amended to require the appointment of an independent
technical or data-protection expert, analogous to the valuers already
contemplated under the existing regulations[27]
wherever a resolution plan or liquidation sale contemplates the transfer of a
dataset containing personal data, whose function would be to certify that such
a transfer complies with the consent, purpose-limitation, and cross-border
transfer requirements of the Digital Personal Data Protection Act 2023 before
the Committee of Creditors approves the relevant plan.
Fourth,
the continuity of critical third-party contracts, cloud-compute agreements, API
licences, and foundation-model licences, during the moratorium period should be
clarified by extending the reasoning in Gujarat Urja Vikas Nigam Ltd v Amit
Gupta through express statutory guidance, empowering the Adjudicating
Authority to restrain the termination of such contracts where termination would
extinguish the going-concern value of the AI asset stack, subject to the
corporate debtor continuing to meet its payment obligations as they fall due
during the resolution process. Together, these reforms would allow the IBC to
treat the AI asset stack as a coherent, preservable, and realisable insolvency
asset, without displacing the property, contract, and data-protection
principles on which the wider legal system depends.
Conclusion
The
IBC was designed on the assumption that a company’s value lies in assets that
can be easily identified, owned, and transferred; the emerging wave of
AI-sector insolvencies, of which Builder.ai and a growing cohort of Indian AI
start-ups are only the most visible instances, consistently challenges that
assumption.[28] AI enterprises derive
their commercial worth not from any single owned asset but from an integrated
AI asset stack of datasets, model weights, licences, and connected digital
resources, few of which the corporate debtor holds outright. This paper has argued
that, rather than focusing on who owns each individual component, the IBC
should recognise the corporate debtor’s integrated commercial interest in the
AI asset stack as the relevant insolvency property, a conclusion supported by
the Supreme Court’s commercial-interest reasoning in Victory Iron Works Ltd
v Jitendra Lohia and State Bank of India v Union of India. It has
further argued that recognising this asset carries with it a reconceptualised,
functional duty of custody and control on the part of the resolution
professional, informed by comparative practice in the United States and the
European Union and calibrated to India’s own data protection regime under the
Digital Personal Data Protection Act 2023. As artificial intelligence becomes a
larger and more volatile component of India’s economy, an insolvency framework
that can preserve and responsibly resolve AI asset stacks, rather than allowing
them to be dismembered into legally incoherent fragments, will be essential to
ensuring that value built over years of user interaction and technical
investment is not needlessly destroyed at the moment it is most needed by
creditors, employees, and the users whose data gave the enterprise its worth in
the first place.
[1]Amanda Silberling,
‘Builder.ai Was Once Worth $1.5B. Now It’s Filing for Bankruptcy’
TechCrunch (20 May 2025); ‘Builder.ai Files for Bankruptcy After Creditors
Seize Accounts’ Bloomberg (5 June 2025).
[2]Thomas Claburn, ‘Builder.ai
Coded Itself into a Corner – Now It’s Bankrupt’ The Register (21 May 2025);
on the underlying allegations of overstated automation see WebProNews, ‘Builder.ai’s
Collapse: AI Hype Fuels $1.5B Startup’s 2025 Bankruptcy’ (30 July 2025).
[3]Raghav Krishna
Agnihotri and Madhav Krishna Agnihotri, ‘Reconceptualising the IBC for AI: A
Case for Asset Stacks’ (Blog post, 2026).
[4]Insolvency and
Bankruptcy Code 2016, s 3(27).
[5]Agnihotri and
Agnihotri (n 3).
[6]ibid (n 1).
[7]ibid (n 4).
[8]Insolvency and
Bankruptcy Code 2016, s 3(27); see also text to (n 5) above.
[9]Insolvency and
Bankruptcy Code 2016, s 18(f).
[10]ibid s 18,
Explanation (a).
[11]Victory Iron Works
Ltd v Jitendra Lohia, Civil Appeal Nos 1743 and 1782 of 2021 (Supreme Court of
India, 14 March 2023) (V Ramasubramanian and Pankaj Mithal JJ).
[12]ibid, holding that
development rights constitute ‘property’ under s 3(27) of the IBC and hence
‘assets’ under ss 18(f) and 25(2)(a) of the Code.
[13]Victory Iron Works
(n 12).
[14]State Bank of
India v Union of India, Civil Appeal No 1810 of 2021 (Supreme Court of
India, 13 February 2026).
[15]Report of the
Insolvency Law Committee (Ministry of Corporate Affairs, Government of India,
March 2018).
[17]Insolvency and
Bankruptcy Code 2016, s 25(2)(a).
[18]ibid s 18(f); see
(n 10).
[19]Insolvency and
Bankruptcy Code 2016, s 14.
[20]Digital Personal
Data Protection Act 2023 (India), s 8.
[21]Justice K S
Puttaswamy (Retd) v Union of India (2017) 10 SCC 1, recognising informational
privacy as an incident of the fundamental right to life and personal liberty
under article 21 of the Constitution of India, and forming the constitutional
foundation of the Digital Personal Data Protection Act 2023.
[22]Digital Personal
Data Protection Act 2023 (India), s 16.
[23]Federal Trade
Commission v Toysmart.com LLC, 2000 WL 34016434 (D Mass 2000); In re
Toysmart.com LLC, Case No 00-13995-CJK (Bankr D Mass 2000).
[24]Bankruptcy Abuse
Prevention and Consumer Protection Act 2005 (US), inserting 11 USC § 332
(consumer privacy ombudsman) and amending 11 USC § 363(b)(1) to restrict the
sale or lease of personally identifiable information inconsistent with a
debtor’s stated privacy policy.
[25]In re 23andMe
Holding Co, United States Bankruptcy Court, Eastern District of Missouri
(2025), Report of the Consumer Privacy Ombudsman.
[26]Regulation (EU)
2024/1689, art 10.
[27]Insolvency and
Bankruptcy Board of India (Insolvency Resolution Process for Corporate Persons)
Regulations 2016, reg 30, as considered in Victory Iron Works (n 12).
[28]ibid (n 1).
How to Cite This Article
MADHUR ANAND, AMITABH KUMAR SAXENA, RADHIKA BADERIA, Treatment of Artificial Intelligence Enterprises Under the Insolvency and Bankruptcy Code 2016., White Black Legal – International Law Journal, ISSN: 2581-8503, Vol. Volume 4, Issue VOLUME 4 ISSUE 2, October 2026, pp. 433-447. Available at: https://www.whiteblacklegal.co.in/details/treatment-of-artificial-intelligence-enterprises-under-the-insolvency-and-bankruptcy-code-2016
Author & Publication Record
Authors: MADHUR
ANAND, AMITABH
KUMAR SAXENA & RADHIKA BADERIA
Registration ID: 107154 | Published Paper ID: WBL7154, WBL7155 & WBL7156
Year: Oct- 2026 | Volume: 4 | Issue: 2
Approved ISSN: 2581-8503 | Country: Delhi, India
Page No.: 433-447
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