How India’s DPDP Act Affects Web Scraping
Web scraping has become a core part of how many businesses collect market intelligence, monitor competitors, and power analytics tools. In e-commerce, retail, and AI, scraping can help teams understand pricing, product availability, consumer behavior, and industry trends faster than manual research ever could. But in India, the Digital Personal Data Protection Act, 2023, has changed the rules of the game.
The most important thing to understand is this: the DPDP Act does not ban web scraping outright, but it does make scraping much more sensitive when the data being collected can identify a person.
That means businesses need to think beyond “Can I technically collect this data?” and ask “Should I collect it, and if so, under what legal basis?”
What the DPDP Act actually covers
The DPDP Act applies to digital personal data, which means personal data in digital form. Personal data is any data about an identifiable individual, and processing includes collection, storage, use, sharing, disclosure, and erasure.
In plain English, if your scraper collects information that can point to a real person, you are likely dealing with regulated personal data.The law applies to processing done in India, and it can also apply outside India if the processing is connected to offering goods or services to people in India.
So a scraping operation does not escape compliance simply because the server is overseas or the scraping vendor is foreign. If Indian users are involved, the Act may still matter.
Why web scraping creates privacy risk
Many scraping projects start with data that appears harmless on the surface. A public profile page, a product review, a business listing, or a social media post may look open to everyone. But if the page includes a person’s name, photo, username, phone number, email address, or any other identifying detail, it may still be personal data under the DPDP Act.
That is why scraping is not just a technical issue. It is also a data governance issue. Once a company stores scraped personal data, it becomes responsible for how that data is used, retained, protected, and shared.
Consent and notice matter
Under the DPDP Act, personal data can generally be processed for a lawful purpose based on either consent or certain legitimate uses. Consent must be free, specific, informed, unconditional, and given through a clear affirmative action.
That is a much higher standard than simply leaving data visible on a website.The Act also requires notice before consent, explaining what data is being processed, why it is being processed, and how the person can exercise rights or complain.
The DPDP Rules explanatory note adds that the notice should be clear, standalone, understandable, and itemized.
For web scraping, this is important because scraping usually happens without any direct relationship between the business and the individual whose data is being collected.
When scraping may be lower risk
Not all scraping is equally risky. Some use cases focus on non-personal public data, such as product prices, stock levels, category names, delivery promises, or item descriptions. These are often more defensible because they are not centered on identifiable individuals.
For example, a retailer scraping competitor product prices is usually dealing with commercial data rather than personal data. A market intelligence team that tracks listings across shopping sites may also stay on safer ground if it avoids collecting customer identities or account-level information.
The lower the emphasis on identifying people, the lower the DPDP risk.
Exemptions are not a free pass
The Act does include exemptions. It says it does not apply to personal data made publicly available by the data principal or by another person who is required by law to make it public.
At first glance, this may sound like a loophole for scraping public websites.But the exemption is narrower than many businesses assume. Publicly available does not automatically mean free to use for any purpose. A public review, comment, directory entry, or social post may still create concerns around fairness, purpose limitation, retention, and downstream use.
In other words, “public” is not the same as “unrestricted.”
What businesses should do differently
If a business wants to use web scraping in India, the safest approach is to build compliance into the workflow from the start. First, classify the data: is it purely commercial, or can it identify a person? Second, collect only what is necessary for the business purpose. Third, document why the scraping is needed and how long the data will be kept.
The Act requires organizations to use reasonable security safeguards, report breaches, and erase personal data once the purpose is over or consent is withdrawn, unless retention is required by law.
The DPDP Rules explanatory note also emphasizes security measures such as encryption, access control, breach intimation, and retention-based erasure. That means privacy compliance is not a one-time legal review; it is an ongoing operational process.
A practical compliance checklist for businesses would include:
- Avoid collecting personal data unless it is truly necessary.
- Remove identifiers as early as possible.
- Keep a clear record of the source and purpose of the data.
- Set retention and deletion rules.
- Restrict access to scraped datasets.
- Review vendor contracts if a third party performs the scraping.
How it affects AI model training
This issue becomes even more important when scraped data is used to train AI models. AI systems often rely on large datasets collected from websites, apps, forums, and public platforms. If those datasets contain personal data, then collecting and using them for training is still processing under the DPDP Act.
That means businesses cannot assume that public data is automatically safe just because it is accessible online. If the model is trained on identifiable data, the company should consider whether consent exists, whether the data is necessary, whether identifiers can be removed, and whether retention and deletion controls are in place.
The safest AI training strategy is to use anonymized or aggregated data wherever possible and keep a clear paper trail of source, purpose, and deletion.This matters especially for AI systems used in customer profiling, lead scoring, recommendation engines, moderation tools, or automated decision-making. In those cases, the business is not just scraping data; it is building systems that may affect real people.
Industry examples
The difference between low-risk and high-risk scraping is easy to see in practice. A price-monitoring tool that collects product names, prices, and availability from public product pages is generally lower risk than a scraper that builds a database of customer identities from social platforms.
Similarly, a retail intelligence team that aggregates store-level trends is usually in a better position than a lead generation team that scrapes names, emails, and phone numbers for outreach. The more a project resembles people-tracking rather than market-tracking, the more carefully it should be reviewed.
What this means for business teams
For business leaders, the main lesson is simple: web scraping is still useful, but the compliance standard is higher now. The DPDP Act pushes organizations to be deliberate about what they collect, why they collect it, and how long they keep it.
That is actually a useful shift. Companies that design responsible scraping workflows will reduce legal risk, improve data quality, and build more trust with customers and partners. In the long run, the best scraping strategy is not “collect everything”; it is “collect only what you can justify.”
Conclusion
India’s DPDP Act does not eliminate web scraping, but it makes personal data handling more accountable. Businesses that scrape non-personal public data for pricing, catalog research, or market intelligence can often work safely with good controls. Businesses that collect identifiable personal data, especially for AI training or outreach, need a much stronger compliance mindset.
REFERENCE
- Ministry of Electronics and Information Technology. The Digital Personal Data Protection Act, 2023. India Code. Available at: https://www.indiacode.nic.in/indiacode/handle/123456789/22037?view_type=browse.
- Ministry of Electronics and Information Technology. Explanatory Note to the Digital Personal Data Protection Rules, 2025. Available at: https://www.meity.gov.in/writereaddata/files/Explanatory-Note-DPDP-Rules-2025.pdf.
- Press Information Bureau. Government notifies DPDP Rules to empower citizens and protect digital personal data. Available at: https://www.pib.gov.in/PressReleasePage.aspx?PRID=2190014.
- EY India. DPDP Rules 2025 Notified by MeitY: Complete guide. Available at: https://www.ey.com/en_in/insights/cybersecurity/transforming-data-privacy-digital-personal-data-protection-rules-2025.
- DLA Piper Data Protection. Data protection laws in India. Available at: https://www.dlapiperdataprotection.com/?t=law&c=IN.
Author https://afsanafaisal.com