ABSTRACT
While artificial intelligence reshapes the technological landscape globally, startups in India face a crucial weakness- how to protect proprietary algorithmic trade secrets and ensure statutory compliance amid high employee mobility and the pervasive use of public generative AI tools. This paper analyses the systematic legal void created by Section 27 of the Indian Contract Act, 1872, which makes post-employment non-compete clauses void, alongside the heightened regulatory risks of feeding customer datasets into public models under the Digital Personal Data Protection Act, 2023 (DPDP Act). This paper has argued that since conventional Non-Disclosure Agreements (NDAs) and reactive prolonged litigation fail to stop any sort of tacit knowledge transfer and Shadow AI leaks, startups must shift from contractual concepts to preventive operational governance. This paper has laid down a comprehensive framework combining compartmentalised access controls, tacit-to-explicit knowledge conversion, cryptographic data lineage, and strategic garden leave provision. By charting the safeguard mechanisms into an actionable governance playbook, this study offers a pragmatic solution for tech startups. Lastly, it establishes how the startups can safeguard core intellectual property and maintain rigorous data fiduciary compliance without stifling the professional mobility.
INTRODUCTION
When core employees leave, a company is likely to lose their competitive edge. The operational implications of this transition are overwhelming, with empirical data showing that intellectual property loss is basically guaranteed during turnovers. As per the 2024 Annual Data Exposure Report by Code42, 80% of cybersecurity leaders have specifically claimed that departing employees take valuable IP with them.[1] Moreover, data from Cyberhaven (2025) has shown that data exfiltration usually begins months before an employee officially resigns, which calls for proactive governance.[2] For startups, the risk of a developer quitting a company and leaving with proprietary source code, client lists, or strategic roadmaps is a high stake business hazard. In the software ecosystems, trade secret theft is typically deliberate and tangible like a downloaded repository or an exported database.
However, in the context of artificial intelligence, the very nature of this risk is altered. For an AI innovator, the product is not merely a software application; the training data and the resulting AI model weights are company’s biggest competitive advantage. Leaving vast silos of proprietary training data unprotected does not just risk a feature leak, it risks the entire competitive advantage. Such vulnerabilities have profound impact on business. The IBM’s 2026 Cost of a Data Breach Report highlights that standard breaches cost an average of $4.99 million globally, alongside a global average breach cost of $6 million for an AI model inversion attack, which reflects the growing difficulty in protecting training data and sensitive information.[3] To add to this risk, Cyberhaven Labs (2026) reports show that 39.7% of the data employees input into public generative AI tools is extremely sensitive corporate information, which basically extinguishes its trade secret status.[4] The severe risks of this overlap were observed globally in the Waymo LLC v. Uber Technologies, Inc., where a departing lead engineer downloaded 14,000 confidential files regarding autonomous driving AI, ultimately forcing Uber into a $245 million mid-trial settlement to prevent a complete halt of their program.[5]
In India, there is a major legal vacuum since Section 27 of the Indian Contract Act, 1872, renders post-employment non-compete clauses largely unenforceable. This leads to startups being forced to rely mostly on Non-Disclosure Agreements (NDAs) to protect their algorithmic secrets. However, traditional NDAs fail when the theft cannot be traced to a file copied or downloaded, but rather to the algorithmic intuition a developer applies at their new firm. Nevertheless, Indian startups are not left without any defence. They can utilise a patchwork of existing workarounds using the Copyright Act, 1957, the Information Technology Act, 2000, and common law principles to get some relief.
[1] Code42, A Mimecast Company, Annual Data Exposure Report 2024 (2024), https://www.mimecast.com/resources/white-papers/annual-data-exposure-report-2024/
[2] CYBERHAVEN, Slow Offboarding Enables Employee Data Theft, Cyberhaven Blog (Apr. 22, 2025), https://www.cyberhaven.com/blog/secure-employee-offboarding-improvements
[3] IBM & Ponemon Institute, Cost of a Data Breach Report 2026 (2026), https://www.ibm.com/reports/data-breach
[4] CYBERHAVEN LABS, 2026 AI Adoption & Risk Report (2026), https://www.cyberhaven.com/resources/report/ai-adoption-risk-report-2026
[5] Waymo LLC v. Uber Techs., Inc., No. C 17-00939 WHA, 2017 WL 2123560 (N.D. Cal. May 15, 2017)
THE LEGAL BOTTLENECK IN INDIA
India’s structural inadequacy in the context of its current legal framework forces AI startups to rely on a haphazard patchwork of contract law, general criminal provisions, and common law principles of equity. This leads to a severe legal bottleneck for tech enterprises that misguidedly count on on generic employment contracts to protect their deep-tech assets.
The Failure of Vague Non-Competes Under Section 27
As per Section 27 of the Indian Contract Act, 1872, an agreement by which a person is restrained from exercising a lawful profession, trade, or business is void. The judicial interpretation of this statute establishes a strict time-based boundary. In the landmark judgment of Niranjan Shankar Golikari v. Century Spinning & Manufacturing Co. Ltd.[6], the Supreme Court of India ruled that any negative covenants which restricts an employee from working for a competitor during the active term of their employment are valid and is not an unlawful restraint of trade.
However, Indian courts have time and again struck down post-employment non-compete clauses as void and unenforceable. The Delhi High Court in the case of Navigators Logistics Ltd. v. Kashif Qureshi[7] demonstrated the peril of relying on vague contractual restraints. The court held that generic claims of confidentiality over business data, combined with broad post-employment non-compete clauses, are unenforceable according to Section 27. An employer cannot rely on a non-compete clause in order to seek legal relief. They have to prove the existence of a distinct, identifiable trade secret and prove that strict, reasonable steps were actively taken to ensure its secrecy.
AI and the Inevitable Disclosure Doctrine (IDD)
The challenge of enforcing trade secrecy is compounded by the Inevitable Disclosure Doctrine (IDD), a legal concept suggesting that a former employee’s new job role will inevitably cause them to rely upon their former employer’s trade secrets. In the AI sector, a machine learning engineer transitioning between rival large language model (LLM) developers will naturally carry the know-how of optimal hyper-parameter tuning and prompt structuring.
Differentiating between an employee’s personal professional skill and a company’s algorithmic trade secret proves to be quite a significant hurdle while garnering evidence. The Law Commission of India’s 289th Report and the draft Protection of Trade Secrets Bill, 2024, explicitly address this boundary. It clearly stipulates that the general skills and experience of an employee which they have acquired through normal professional practice cannot be categorised as a trade secret.[8] Subsequently, without thorough, documented segregation of what classifies as the company’s proprietary model weights versus the engineer’s general machine learning judgment, corporate litigation often fails due to ambiguity in classification.
[6] Niranjan Shankar Golikari v. Century Spinning & Mfg. Co., [1967] 2 S.C.R. 378, A.I.R. 1967 S.C. 1098.
[7] Navigators Logistics Ltd. v. Kashif Qureshi, 2018 SCC OnLine Del 11321.
[8] Intepat, The Protection of Trade Secrets Bill 2024: What It Proposes and What It Still Leaves Open, Intepat Blog (Feb. 19, 2025), https://www.intepat.com/blog/the-trade-secret-bill-2024-a-comprehensive-analysis
The Public AI Trap and Breach of Confidence
A new and emerging threat to algorithmic secrecy globally is the Public AI Trap. In the absence of a specific trade secrets statute, Indian courts tend to rely excessively on the doctrine of breach of confidence. This equitable protection requires proof that the information was genuinely confidential and conveyed to the employee under an explicit obligation of secrecy.
While employee mobility carries a risk of tacit knowledge transfer, an equally brutal threat to algorithmic secrecy lies while the employee is still employed in the company; often referred to as of Shadow AI. Startups, in their early-stages, the pressure they feel to ship their products rapidly often outpaces compliance. This has led to developers frequently turning to public, third-party generative AI platforms (such as open-access LLMs) to debug proprietary code snippets, optimise algorithmic logic, or format raw customer datasets.
This practice is not usually malicious but the legal consequences they carry are devastating. Developers intuitively use the LLM prompt box like a private search engine, unaware that public tiers of these models usually retain user inputs as training data.
In April 2023, Samsung engineers infamously pasted proprietary semiconductor source code and internal meeting notes into ChatGPT to check for errors and optimise the logic. Because OpenAI retains this data for training by default, Samsung’s trade secrets were functionally leaked to the public model, which led to Samsung banning the use of ChatGPT among its employees.[9]
In both Indian and international trade secret jurisprudence, a proprietary asset only can retain its legal protection if the owner takes reasonable steps to maintain its secrecy. When core logic is being fed into a third-party model without a zero-data-retention agreement, the startup involuntarily broadcasts its IP, arguably losing its trade secret status entirely. Furthermore, inputting customer datasets in this manner risks severe regulatory penalties.
[9] Siladitya Ray, Samsung Bans ChatGPT Among Employees After Sensitive Code Leak, FORBES (May 2, 2023), https://www.forbes.com/sites/siladityaray/2023/05/02/samsung-bans-chatgpt-and-other-chatbots-for-employees-after-sensitive-code-leak/.
Pre-Existing Statutory Workarounds in India
While India currently lacks a dedicated, sui generis trade secrets statute, startups can assemble a protective shield by using overlapping provisions across existing laws to ensure accountability of the exfiltration.
The Information Technology Act, 2000 (Sections 43 and 66): Section 43 imposes heavy civil penalties for the unauthorised downloading, extraction, or copying of data from a computer system, while Section 66 criminalises these acts when done dishonestly. Thus, if a departing employee transfers model weights or customer data to a personal drive before resigning, the startup can trigger financial and criminal liabilities based purely on the physical act of data theft.
The Specific Relief Act, 1963 (Sections 38 and 39): These provisions empower courts to grant perpetual and mandatory injunctions to prevent the breach of a specific obligation. If a startup suspects an imminent threat of data leakage by a former employee, it can proactively use this Act to seek a court order. This legally restrains the individual from disclosing confidential AI models to a new employer or competitor.
Common Law (Breach of Confidence): Indian courts recognise the equitable doctrine that no individual should unfairly benefit from information imparted under an obligation of secrecy. Even if a post-employment contract is invalidated under Section 27 of the Contract Act, startups can invoke this doctrine to seek relief if an employee misappropriates tacit algorithmic knowledge they knew was strictly confidential.
Digital Personal Data Protection Act, 2023 (DPDP Act): As the primary regulatory regime governing digital personal data in India, the DPDP Act imposes strict obligations on Data Fiduciaries:
- Purpose Limitation (Section 6): Mates that personal data can only be processed for the specific, lawful purpose for which the data principal gave consent or for permitted legitimate uses.
- Data Fiduciary Security Safeguards (Section 8(5)): Requires Data Fiduciaries to implement reasonable security safeguards to prevent personal data breaches.
- Mandatory Breach Notification (Section 8(6)): Failing to report data breaches immediately to the Data Protection Board of India (DPBI) and affected principals.
- Cross-Border Transfer Restrictions (Section 16(1)): Central Government has the power to restrict or prohibit the transfer of personal data by a Data Fiduciary to specified countries or territories outside India.
The Practical Solution
While using a patchwork of statutory mechanisms provide basic legal relief, relying on them as a primary defence in the AI sector can be a little challenging. Indian statutory remedies are inherently reactive and post-facto. Most of the statutes/ doctrines can be used after the exfiltration or disclosure has already occurred. In the AI industry, where an architecture or tuning configuration can become obsolete in six months, prolonged litigation offers little practical consolation once core model weights have leaked into a competitor’s pipeline.
Furthermore, existing statutes are designed for demonstrable data theft such as physical downloads or unauthorised server access. They are not quite suited to police the tacit, intuitive knowledge transfer inherent in the algorithmic engineering process. Proving copyright infringement or criminal breach of trust becomes nearly impossible when a departing engineer applies internalised hyperparameter intuitions at a rival firm without copying a single line of raw code. Consequently, tech enterprises must transition from reactive judicial remedies to pre-emptive computational governance, hardcoding IP hygiene directly into the development infrastructure before a talent transition ever takes place.
1. Compartmentalising access
In most new startups, it is quite common for a sole developer to handle most of the work including scraping the data, cleaning the dataset, tuning the hyperparameters, and training the model. Having full access lets the developer learn exactly how the system is built.
Thus, Startups must try to adhere strictly to Role-Based Access Controls (RBAC) aligned with ISO/IEC 27001[10] and the NIST AI Risk Management Framework[11] which will keep proprietary data engineering pipelines separate from downstream model fine-tuning teams. By dividing the pipeline, no single departing engineer possesses full insight into the end-to-end proprietary architecture.
[10] ISO/IEC 27001:2022, Information security, cybersecurity and privacy protection — Information security management systems — Requirements (International Organisation for Standardization 2022).
[11] National Institute of Standards & Technology, U.S. Department of Commerce, NIST AI 100-1, Artificial Intelligence Risk Management Framework (AI RMF 1.0) (2023)
2. Converting Tacit to Explicit Knowledge
You can’t easily protect a developer’s thought process. If they don’t document exactly why they adjusted a model, that valuable knowledge leaves with them when they quit.
Startups must require developers to log their algorithmic choices and the rationale behind hyperparameter tuning in internal company repositories. By obliging developers to write down their intuition, the company basically gets to legally convert a subjective, tacit skill into an objective, explicit trade secret which will be owned by the enterprise.
3. Cryptographic Data Lineage
When an NDA dispute reaches the courts, the burden of proof lies on the startup have to show proof that specific proprietary data was misappropriated. They cannot simply use a competitor’s similar model as evidence.
Startups should adopt the practice of integrating cryptographic watermarking within their proprietary datasets and embed tamper-evident audit logs across their model weight registries. In the case of any suspected exfiltration, access lineage will serve as conclusive evidence of unauthorised data transfer, eliminating the need to rely on speculative claims of disclosure.
4. Garden Leave as a Section 27 Workaround
Since an AI developer cannot be legally barred from joining a rival startup immediately after resigning, the half-life of algorithmic secrets becomes critical. In the AI industry, a specific hyperparameter configuration or dataset might be obsolete in six months.
Instead of banking on void non-competes, startups should start incorporating extended notice periods coupled with Garden Leave provisions in their employment contracts. By paying the developer to sit out of the industry for 3 to 6 months during their notice period, the startup can ensure that the hyper-specific, tacit intuition the developer possesses loses its immediate competitive value before they start their new job.
In M/s. Kuoni Travel (I) Pvt. Ltd. v. Mr. Ashish Kishore, the Bombay High Court observed that “…it is obvious that the “doctrine of restraint” on trade secret can be invoked even after the service or employment period has come to an end…” and held that the employer was entitled to restrain the former employee from competing during the garden leave period.[12]
[12] Kuoni Travel (India) Pvt. Ltd. v. Ashish Kishore, 2007 SCC OnLine Bom 1604, (2007) 6 All MR 808.
Safeguard Implementation Matrix
Safeguard Pillar | Primary Owning Function | Implementation Cost & Complexity | Operationalised Indian Statute / Doctrine |
Compartmentalising Access | InfoSec & Engineering | Medium-High: Requires IAM infrastructure, RBAC policies, and auditing tools | Common Law (Breach of Confidence) & Information Technology Act, 2000 (reasonable security practices under Sec. 43A) & DPDP Act. |
Tacit-to-Explicit Conversion | Engineering & Legal | Low-Medium: Requires process enforcement, internal documentation repositories, | Copyright Act, 1957 & common law doctrine of breach of confidence. |
Cryptographic Data Lineage | Engineering & InfoSec | High: Requires specialised watermarking tools, secure version control, | Specific Relief Act, 1963 (for injunctions) & Bharatiya Sakshya Adhiniyam, 2023 (governing electronic evidence admissibility) |
Garden Leave | HR & Legal | Low-Medium: Requires continued salary payouts during the | Garden leave doctrine. |
CONCLUSION
The intersection of AI and Indian IP law exposes the structural limits of traditional legal instruments. With post-employment non-compete clauses void under Section 27 of the Indian Contract Act and statutory remedies acting only post-facto, conventional NDAs are ill-equipped to address the tacit, intuitive nature of algorithmic innovation. Relying on reactive litigation offers little protection once core model weights or training methodologies become the subject.
Resolving this tension requires a shift from contractual abstraction to empirical, computational governance. By embedding operational IP hygiene directly into development workflows, startups can construct an indisputable defence. This operational approach successfully harmonises the legal mandate for employee mobility with the commercial necessity of tech secrecy, promoting a resilient foundation for India’s AI ecosystem.
Authored by: Aastha Pattanayak
