AI and Machine Learning Under DPDP Guide
AI and ML models are built on data. Book a DPDP clarity call.
Discuss this page with an LLM
DPDP Action Sheet
Use this before your next workflow goes live. It keeps the useful parts visible and turns DPDP into checks your team can actually answer.
For AI and Machine Learning Under DPDP Guide, the DPDP question is how personal data enters the workflow, where it is stored, which tools touch it, what purpose was explained, and how deletion or withdrawal will work.
1. Lead Forms
Check:
- What data are you collecting?
- Is the purpose clear at the point of collection?
- Is marketing consent separate from service communication?
- Can the user withdraw consent later?
Common mistake: one checkbox that silently covers newsletters, sales calls, partner sharing and remarketing.
2. Email and WhatsApp
Check:
- Who is on the list?
- Where did consent come from?
- Is the list imported from a vendor, event, webinar, scrape or old CRM?
- Can you prove the source of consent?
Common mistake: treating every lead as permanently marketable.
3. Ads and Retargeting
Check:
- Are pixels or ad platforms receiving identifiable user behavior?
- Are audiences built from customer lists?
- Are lookalike or remarketing audiences using personal data?
Common mistake: assuming "the ad platform handles it" means your company has no DPDP responsibility.
4. Website Analytics
Check:
- Which tools run on the site?
- Are IP address, device identifiers, session IDs or form fields being captured?
- Is analytics used only for measurement, or also for profiling and targeting?
Common mistake: installing tools first and asking privacy questions later.
5. Vendor List
Make a quick list:
- CRM
- Email platform
- WhatsApp provider
- Analytics
- Ad pixels
- Form tool
- Landing page builder
- Webinar tool
For each vendor, answer: what data goes there, why, who can access it and how deletion works.
6. This Week's Action
Map one campaign from first click to final follow-up. Mark every place personal data is collected, enriched, shared, uploaded or used for targeting.
If your team cannot answer where the data came from and where it goes next, start with a data flow map before rewriting policy copy.
Book a DPDP clarity callWant all of this handled, end to end? Sanctum is the all-in-one DPDP compliance programme behind this site: legal position, data map, gap analysis, implementation, tooling, training, readiness opinion, and breach cover under one accountable owner. How all-in-one DPDP compliance works or see the Sanctum programme.
The Model Training Conflict
AI developers often use historical datasets to train new models. Under DPDP, you cannot use data collected for a specific service, such as a customer support chatbot, to train a separate generative model without getting fresh consent. If your training pipeline pulls from “publicly available” Indian data, you must verify the data was made public by the individual themselves rather than a third-party scraper.
The Right to Erasure in Neural Networks
DPDP grants users the right to have their data deleted. For AI firms, this is more complex than deleting a row in a database. If a user withdraws consent, you must determine if their data influenced the weights of a production model. Standard database deletion does not automatically remove the patterns learned from that user’s specific behavior or biometric markers.
| AI Workflow | Personal Data Involved | DPDP Risk |
|---|---|---|
| LLM Fine-tuning | Customer support transcripts | Personal data leakage in model responses |
| Computer Vision | Facial geometry and biometrics | Processing sensitive data without explicit notice |
| Predictive Scoring | Financial history and age | Automated profiling without a clear legal basis |
| Dataset Cleaning | Raw PII before anonymization | Retaining raw data longer than the training phase |
This week
Review your data ingestion pipeline and tag every dataset used for training with its original consent timestamp. Identify any datasets scraped from the web that contain Indian residents’ names or photos to verify if they meet the “publicly available” criteria.
Now think about your work. Where does personal data enter your workflows? Where does it sit? Who else touches it?
Frequently asked questions
Can I use anonymized data to train my AI models without consent?
If the data is truly anonymized and cannot be linked back to an individual, DPDP does not apply. However, if your model can "re-identify" individuals by combining datasets, you are still handling personal data and need a legal basis.
How do I handle a "right to correction" if the AI generates false info about a person?
You must provide a way for users to correct inaccurate personal data that your model outputs. This may require updating the model's knowledge base or using Retrieval-Augmented Generation (RAG) to override old training data.
Do I need consent to use synthetic data?
You do not need consent for the synthetic data itself. However, you must have valid consent for the original personal data used to train the synthetic data generator.