Artificial intelligence and machine learning systems depend heavily on data. Businesses use AI to analyse customers, automate decisions, personalise services, detect fraud, support employees and generate content. As these systems become more sophisticated, the relationship between technology and privacy becomes increasingly important. AI and data protection laws therefore need to be considered together whenever an AI system collects, analyses, stores or generates information relating to identifiable individuals. In India, businesses must consider the Digital Personal Data Protection Act, 2023 alongside information technology laws, contractual obligations, cybersecurity requirements, intellectual property rules and sector specific regulations. The legal question is no longer simply whether a company can use AI. It is whether the company can use the relevant data for the specific AI purpose in a lawful, transparent and responsible manner.

What Are AI and Data Protection Laws?

AI and data protection laws are not necessarily a single category of legislation. They describe the overlapping legal requirements which apply when artificial intelligence systems process personal data. An AI model may use information during training, testing, fine tuning, deployment and monitoring. Personal data may also appear in prompts, uploaded documents, model outputs, application logs or analytics systems. Each processing activity can raise different legal questions. The Digital Personal Data Protection Act provides India's principal modern framework for digital personal data. It does not contain a separate chapter dedicated exclusively to artificial intelligence. Instead, its general rules apply where AI related activities involve personal data within the scope of the legislation. This makes the first compliance question straightforward: a business should determine whether its AI system processes personal data and then identify the purpose, legal basis, participants and data flows involved.

Does India Have a Specific AI Data Protection Law?

India does not currently have one comprehensive statute regulating every aspect of artificial intelligence. AI governance is developing through existing laws, regulatory frameworks, government initiatives and sector specific requirements. The DPDP Act is particularly important because AI systems frequently rely on personal data for training, testing, profiling and deployment. The Information Technology Act and associated IT Rules can also become relevant depending on the AI application. Consumer protection law, intellectual property law, contract law and sector specific rules may create additional obligations. The legal position can therefore differ considerably between an AI recruitment platform, healthcare model, financial scoring system, customer service chatbot and general purpose AI application. Businesses should avoid treating the absence of a standalone AI statute as an absence of regulation. The applicable obligations may already exist across several legal frameworks.

How the DPDP Act Applies to AI Systems?

The DPDP Act regulates the processing of digital personal data. Processing is broad enough to cover automated operations involving data. As a result, an AI system can fall within the framework when it processes information capable of identifying an individual. For example, a company using employee records to train an internal AI assistant may be processing personal data. A financial institution using customer information for fraud detection may also be processing personal data. A healthcare company analysing patient records through machine learning creates another example. The relevant legal analysis depends on the actual data and purpose. An AI model trained entirely on information which is not personal data raises different questions from a model trained on identifiable customer records.

AI Training Data and Legal Basis

Training data is one of the most important privacy issues in AI development. Businesses often assume they can use any dataset available to them for model training. This approach creates significant legal risk. The organisation should establish where the training data came from, whether it contains personal data, why the information was originally collected and whether the proposed AI use is compatible with the applicable legal basis. If consent is the applicable basis, the organisation needs to consider whether the notice and consent mechanism adequately covers the intended processing. Where another lawful basis or legitimate use applies under the DPDP framework, the business should document why it applies. The analysis should also consider whether the data was obtained from a third party. A company cannot necessarily assume its vendor had the right to use personal data for AI training simply because the vendor supplied the dataset.

Can Businesses Use Publicly Available Data to Train AI?

Public availability does not automatically resolve every legal issue surrounding AI training. Information available on a website may still relate to identifiable individuals. Businesses should therefore examine how the information was collected, whether the information falls within an applicable statutory provision, how it will be used and whether other laws impose restrictions. The DPDP Act contains provisions concerning publicly available personal data, but businesses should not interpret these provisions as a universal permission to scrape any publicly accessible information for any AI purpose. The legal analysis should also consider copyright, database rights where applicable, website terms, contractual restrictions, confidentiality and cybersecurity requirements. This is particularly important for businesses building large language models or machine learning datasets through web scraping.

Purpose Limitation and AI Model Development

Purpose limitation creates a difficult question for AI developers. A company may originally collect information to provide a service. Later, its product team may want to use the same information to train an AI model, improve an algorithm or develop a new product. The company should not assume the new purpose is automatically covered by the original collection. It should assess the relationship between the original purpose and the proposed AI use, identify the applicable legal basis and determine whether additional notice or consent is required. A clear data purpose map can help prevent inappropriate secondary use.

Data Minimisation in Artificial Intelligence

AI systems often create pressure to collect as much data as possible. More information can appear useful for improving model performance, but privacy law does not necessarily support unlimited collection. Businesses should identify the information genuinely needed for the intended AI purpose. Where a model can perform effectively without direct identifiers, those identifiers should be removed or separated where practical. Where aggregated or anonymised information is sufficient, using identifiable information may create unnecessary privacy risk. Data minimisation should also apply to testing datasets. Developers should not automatically use live customer information when synthetic or appropriately anonymised data can achieve the same technical objective.

AI Data Governance Throughout the Model Lifecycle

AI privacy governance should cover more than training. Personal data can enter an AI system during data collection, training, validation, fine tuning, deployment, prompting, retrieval augmented generation, monitoring and model improvement. A business should therefore map the complete lifecycle. For a generative AI application, this can include user prompts, uploaded files, retrieval databases, external model providers, conversation history, logs and generated outputs. Each component can create a separate data processing activity. This is one of the most important differences between conventional software privacy reviews and AI privacy reviews. AI systems can create complex and sometimes unpredictable data flows.

Automated Decision Making and Profiling

AI can influence decisions about individuals even when the system does not make the final decision itself. Examples include recruitment screening, credit assessment, insurance risk analysis, fraud detection, employee monitoring and customer segmentation. Businesses should determine whether an AI system is merely assisting a human or materially influencing an outcome affecting an individual. They should also consider whether the information used by the system is accurate, relevant and appropriate for the decision. The DPDP Act does not create a general GDPR style right to object to every automated decision. However, businesses still need to comply with applicable data processing requirements, and other sector specific laws may impose additional obligations. High impact uses should receive stronger governance because errors can have serious consequences for individuals.

Accuracy and Data Quality in Machine Learning

Poor quality training data can produce inaccurate or discriminatory outcomes. From a privacy perspective, businesses should consider whether personal information used by an AI system is accurate and appropriately maintained where accuracy matters to the processing purpose. An outdated customer profile, incorrect financial information or inaccurate employment record can affect an automated recommendation. Data governance should therefore include processes for correcting relevant information and assessing the quality of datasets used in material AI applications.

Sensitive and High Risk Data in AI

The DPDP Act does not reproduce the former SPDI framework by creating a general statutory category called sensitive personal data. However, some types of information can create significantly greater privacy risks when processed through AI. Healthcare records, financial information, biometric information, children's information and employment data can require enhanced governance depending on the context and applicable legal framework. Businesses using such information should apply stronger access controls, security measures, purpose restrictions and monitoring. The risk assessment should consider both the sensitivity of the data and the consequences of an AI failure.

AI and Children's Personal Data

Children require special protection under the DPDP Act. Businesses processing children's personal data must consider the specific requirements concerning verifiable parental consent and restrictions on certain processing activities. AI systems used in education, gaming, social platforms, healthcare and children's services can therefore require additional safeguards. Age verification, parental consent mechanisms, profiling controls and product design should be reviewed before deploying AI features involving children.

Privacy by Design for AI Products

Privacy should be incorporated into AI development from the beginning. A privacy by design approach requires product teams to consider data collection, purpose, access, retention, security and deletion before an AI feature is launched. The design should also account for model inputs and outputs. For example, an AI assistant may unintentionally reproduce personal information contained in its training or retrieval sources. A company should therefore consider access controls, filtering, data segregation and output monitoring. Privacy testing should form part of AI testing rather than being treated as a separate legal exercise after deployment.

AI Vendors and Third Party Model Providers

Many businesses do not build AI models themselves. They use external providers through APIs, cloud platforms or software products. This creates additional privacy considerations. Before sending personal data to an AI vendor, the business should understand where the information is processed, whether the provider retains prompts, whether data is used for model training, how long information is retained and whether subcontractors receive access. Contracts should clearly address permitted processing, confidentiality, security, incident reporting, deletion and use of customer information for model improvement. A vendor's general statement claiming it is privacy compliant should not replace contractual and technical due diligence.

AI and Data Processors

An AI provider may act as a Data Processor where it processes personal data on behalf of a Data Fiduciary. The role depends on the actual processing arrangement. For example, a company may instruct an AI service to analyse customer support records for the company's internal purposes. In such a situation, the AI provider may be processing information on behalf of the business. The contractual framework should reflect this relationship and define the permitted processing activities. A business should also assess whether an AI vendor uses customer information for its own independent purposes. If it does, the legal analysis may become more complex.

Data Retention and AI Systems

Retention can be difficult in AI environments. Deleting personal information from a customer database may not remove copies contained in training datasets, vector databases, model evaluation files, prompts, logs or backups. Businesses should therefore establish retention controls before using personal data for AI. They should also understand whether information has been incorporated into model development and whether it can be technically removed or isolated later. Where the technical ability to delete training information is limited, this issue should be assessed before the dataset is used rather than after a rights request arrives.

Data Principal Rights and AI

The DPDP Act provides Data Principals with rights including access to information, correction and completion, updating, erasure in applicable circumstances, grievance redressal and nomination. AI systems can make these rights operationally difficult. A company may know an individual's information exists in its CRM but not realise it also appears in a training dataset or AI monitoring system. Businesses should therefore connect their data rights processes with AI data inventories. When a correction or erasure request is received, the organisation should know which systems need to be assessed and whether another legal requirement permits or requires retention.

AI Security and Data Protection

AI introduces security risks alongside privacy risks. Prompt injection, model extraction, unauthorised access, insecure APIs, data leakage and compromised model infrastructure can expose personal information. Security controls should therefore cover both the underlying data and the AI application. Businesses should use appropriate authentication, access controls, encryption, logging, monitoring and vulnerability management. AI specific testing should also consider whether the system can be manipulated into disclosing information it should not reveal. A secure model is not necessarily a privacy compliant model, but privacy compliance becomes difficult to demonstrate without appropriate security.

Cross Border AI Processing

AI providers frequently operate infrastructure across several countries. An Indian company sending personal data to an overseas AI provider should assess the applicable cross border requirements under Indian law and any contractual or foreign legal requirements. The DPDP Act provides a framework under which the Central Government may restrict transfers to specified countries or territories. Sector specific laws can create additional requirements. Businesses should map where prompts, training datasets, model outputs, logs and backups are stored or accessed. The location of the AI vendor's headquarters is not enough. Actual processing locations and remote access arrangements should also be considered.

AI and International Privacy Laws

Businesses serving customers outside India may need to consider foreign privacy regimes alongside Indian law. The GDPR is particularly relevant for organisations within its territorial scope. Other jurisdictions may have their own privacy requirements. The European framework also contains specific rules concerning automated decision making and high risk AI when the relevant laws apply. An Indian company developing AI for international markets should therefore conduct jurisdictional analysis before deployment rather than assuming compliance with Indian law will automatically satisfy overseas requirements.

AI Governance and the DPDP Framework in India

India's AI governance approach is evolving through existing laws, policy initiatives and emerging governance frameworks rather than one comprehensive AI statute. Government discussions have increasingly recognised the need to address responsible AI, data governance, cybersecurity, fairness, transparency and accountability. The DPDP Act is an important part of this framework because it governs personal data processing even when the processing is performed through AI. This means businesses should avoid waiting for a future standalone AI law before establishing governance controls.

Significant Data Fiduciaries and AI Risk

The DPDP Act creates additional obligations for Significant Data Fiduciaries. Where an organisation is designated as an SDF, the framework includes requirements concerning a Data Protection Officer, independent data auditing and Data Protection Impact Assessments. The DPDP Rules also introduce specific due diligence relating to algorithmic software used for processing personal data by Significant Data Fiduciaries. This is important for businesses operating large scale AI systems involving substantial volumes of personal data. An organisation should therefore assess whether its size, data processing activities and risk profile could bring it within the SDF framework.

AI Impact Assessments

A formal AI impact assessment can help businesses identify privacy and other risks before deployment. The assessment should examine the purpose of the AI system, categories of personal data, affected individuals, data sources, model behaviour, potential harms, security controls, vendor dependencies and mitigation measures. Where the organisation is subject to statutory DPIA requirements, those obligations should be integrated into the assessment process. Even where a DPIA is not legally mandatory, a structured risk assessment can provide valuable evidence of responsible governance.

AI Training Data Provenance

Businesses should maintain records showing where important training datasets came from. Data provenance can help answer questions about ownership, permissions, privacy rights, contractual restrictions and data quality. This is particularly important when datasets come from vendors or are assembled from multiple sources. A company should be able to explain the origin and permitted use of material datasets used in an important AI system. Poor provenance can create privacy, contractual, copyright and regulatory risks simultaneously.

How Businesses Can Build an AI Data Compliance Framework?

Businesses should begin by creating an inventory of AI systems. The inventory should identify which systems are in development, testing and production and whether each system processes personal data. The next step is data mapping. Organisations should identify the source, purpose, location and recipients of information used by each AI system. They should then assess the applicable legal basis and review privacy notices, contracts and vendor arrangements. Technical teams should test access controls, data leakage, retention and deletion. Legal and compliance teams should assess regulatory obligations and contractual exposure. For businesses deploying AI at scale, AI data compliance should become part of the organisation's wider privacy and technology governance framework rather than a one time project.

Common AI Privacy Mistakes Businesses Should Avoid

One common mistake is assuming publicly available information can always be scraped and used for model training without further legal analysis. Another is using customer data for AI training when the original purpose of collection did not clearly contemplate such use. Businesses also frequently overlook data contained in prompts, logs and monitoring systems. A company may protect its main database while sending personal information to an external AI provider without adequate contractual controls. Another mistake is failing to distinguish AI vendor roles. A provider may act as a processor for one activity and have an independent purpose for another. Finally, businesses may focus on model accuracy while overlooking privacy rights, data provenance, retention and security.

Conclusion

AI and data protection laws are becoming increasingly interconnected because modern AI systems depend on large volumes of information. Businesses cannot treat privacy as a separate issue to be considered after a model has been developed. Data protection needs to be incorporated into dataset selection, model development, testing, deployment, vendor management and ongoing monitoring.

For Indian businesses, the DPDP Act provides an important foundation, but it operates within a wider legal environment involving information technology law, cybersecurity, intellectual property, consumer protection and sector specific regulation. India is also continuing to develop its broader AI governance approach. The most practical approach is to begin with the data. Businesses should know what information an AI system uses, where it came from, why it is being processed, who can access it, where it is transferred and how long it remains available. They should also understand whether they are acting as a Data Fiduciary, Data Processor or both across different activities.

AI governance should then connect legal requirements with technical controls. Data mapping, provenance records, privacy notices, consent mechanisms, vendor agreements, access controls, model testing, retention policies and incident response should work together. Businesses developing or deploying high impact AI should also consider structured privacy and AI risk assessments before launch. This approach can identify problems early, when changes to datasets, contracts or architecture are still practical.

As AI adoption continues across financial services, healthcare, employment, education, retail and enterprise technology, responsible data governance will become increasingly important. Organisations developing AI products or integrating third party AI systems should ensure privacy considerations are addressed throughout the technology lifecycle. For businesses developing AI products or integrating third party AI systems, technology business lawyers can assist with the legal issues arising from data protection, AI contracts, technology licensing, vendor arrangements, intellectual property, cybersecurity and regulatory compliance. The objective should not be to prevent responsible AI use. It should be to create a framework in which innovation can take place with clear accountability for the data used and the people affected.

Frequently Asked Questions (FAQs)

Q1. Do AI systems fall under Indian data protection law?

AI systems can fall within the DPDP framework when they process digital personal data within its scope. The law applies to the relevant processing rather than simply to the label “AI”.

Q2. Does the DPDP Act specifically regulate artificial intelligence?

The DPDP Act does not contain a standalone chapter dedicated to AI. Its general provisions can apply to AI related processing of personal data. Additional obligations may arise under other laws and sector specific frameworks.

Q3. Can personal data be used to train AI models in India?

It may be possible depending on the data, purpose, legal basis, applicable provisions and contractual restrictions. Businesses should not assume all personal data can automatically be used for AI training.

Q4. Can publicly available data be used for AI training?

Public availability does not automatically resolve every legal issue. Businesses should examine the applicable DPDP provisions as well as copyright, contractual, cybersecurity and other legal considerations.

Q5. Does consent have to be obtained for AI training?

Not in every situation. The applicable legal basis depends on the processing and circumstances. Where consent is relied upon, businesses must ensure the consent framework meets the requirements of applicable law.

Q6. Can a company use customer data to train its AI model?

It depends on the relationship with the customer, the purpose for which the information was collected, the applicable legal basis, contractual terms and the nature of the proposed AI use. Customer data should not automatically be treated as unrestricted training material.

Q7. Do AI vendors need Data Processing Agreements?

Where an AI vendor processes personal data on behalf of a Data Fiduciary, appropriate contractual arrangements should address the processing relationship, security, confidentiality, sub processors, incidents, retention and deletion.

Q8. Does GDPR apply to AI systems used by Indian companies?

The GDPR can apply where its territorial requirements are met. Indian companies serving overseas markets should assess the relevant jurisdiction rather than assuming Indian law alone applies.

Q9. What are the main privacy risks associated with generative AI?

Important risks include inappropriate training data, excessive data collection, prompt leakage, use of personal data for model improvement, inaccurate outputs, uncontrolled retention, third party processing and difficulty responding to individual rights requests.

Q10. Can an individual request deletion of personal data used in an AI model?

The DPDP Act provides an erasure right in applicable circumstances, but the practical application to trained models can be technically complex. Businesses should assess where personal data exists within the AI lifecycle and whether any legal retention requirement applies.

Q11. Are AI systems allowed to make automated decisions about individuals?

Indian law does not contain a universal prohibition on automated decision making. However, organisations must comply with applicable privacy requirements and any sector specific laws. High impact uses should receive careful governance and risk assessment.

Q12. Do AI businesses need a Data Protection Officer?

Not every AI company automatically requires a statutory Data Protection Officer. Additional requirements can apply where an organisation is designated as a Significant Data Fiduciary under the DPDP framework.

Q13. What should businesses do before deploying an AI system?

They should identify the data being processed, establish the purpose and legal basis, assess vendors, review privacy notices and contracts, map data flows, evaluate risks, implement security controls and establish retention and rights management processes.

This update was released on 06 Oct 2026.

The views expressed in this update are personal and should not be construed as any legal advice. Please contact us directly on +91 22 40565252 or contact@mhcolaw.com for any assistance.

Legal Update Team
MANSUKHLAL HIRALAL & COMPANY
Advocates, Solicitors and Notaries
T: +91 22 40565252
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